Biomass burning can significantly impact the chemical and
optical properties of carbonaceous aerosols. Here, the biomass burning
impacts were studied during wintertime in a megacity of Nanjing, eastern China.
The high abundance of biomass burning tracers such as levoglucosan (lev),
mannosan (man), galactosan (gal) and non-sea-salt potassium (nss-K+)
was found during the studied period with the concentration ranges of
22.4–1476 ng m-3, 2.1–56.2 ng m-3, 1.4–32.2 ng m-3 and
0.2–3.8 µg m-3, respectively. The significant contribution of
biomass burning to water-soluble organic carbon (WSOC; 22.3±9.9 %) and organic carbon (OC; 20.9±9.3 %) was observed in this
study. Backward air mass origin analysis, potential emission sensitivity of
elemental carbon (EC) and MODIS fire spot information indicated that the
elevations of the carbonaceous aerosols were due to the transported
biomass-burning aerosols from southeastern China. The characteristic mass
ratio maps of lev/man and lev/nss-K+ suggested that the biomass fuels
were mainly crop residuals. Furthermore, the strong correlation (p<0.01) between biomass burning tracers (such as lev) and light absorption
coefficient (babs) for water-soluble brown carbon (BrC) revealed that
biomass burning emissions played a significant role in the light-absorption
properties of carbonaceous aerosols. The solar energy absorption due to
water-soluble brown carbon and EC was estimated by a calculation based on
measured light-absorbing parameters and a simulation based on a radiative
transfer model (RRTMG_SW). The solar energy absorption of
water-soluble BrC in short wavelengths (300–400 nm) was 0.8±0.4
(0.2–2.3) W m-2 (figures in parentheses represent the variation range of each parameter) from the calculation and 1.2±0.5 (0.3–1.9) W m-2 from the RRTMG_SW model. The absorption capacity of
water-soluble BrC accounted for about 20 %–30 % of the total absorption of
EC aerosols. The solar energy absorption of water-soluble BrC due to biomass
burning was estimated as 0.2±0.1 (0.0–0.9) W m-2, considering
the biomass burning contribution to carbonaceous aerosols. Potential source
contribution function model simulations showed that the solar energy
absorption induced by water-soluble BrC and EC aerosols was mostly due to
the regionally transported carbonaceous aerosols from source regions such
as southeastern China. Our results illustrate the importance of the
absorbing water-soluble brown carbon aerosols in trapping additional solar
energy in the low-level atmosphere, heating the surface and inhibiting the
energy from escaping the atmosphere.
Introduction
Biomass burning has been of great concern in recent years due to its severe impact
on air quality and climate (Zhang et al., 2015; Gilman et al., 2015; J. Chen
et al., 2017). Water-soluble organic carbon (WSOC) from biomass burning
emission has a pronounced influence on the increase in aerosol cloud
condensation nuclei (CCN) activity, which can lead to a cooling impact
(Gao et al., 2003; Rogers et al., 1991; Novakov and Corrigan, 1996).
Meanwhile, a portion of organic carbon (OC) could have a cooling effect on
the land surface by scattering sunlight (Zhang et al., 2017; Myhre et
al., 2013). A portion of OC is involved in the aging process of elemental
carbon (EC) or black carbon (BC) as coating materials for the BC core,
enhancing BC radiative absorption (Peng et al., 2016; Y. Wang et al.,
2018). A fraction of OC can also directly absorb solar energy, functioning
similarly to BC, which is referred to as brown carbon (BrC) (Laskin
et al., 2015). But BrC is different from BC due to its strong light
absorption from visible (VIS) to ultraviolet (UV) wavelengths, thus having a
stronger wavelength dependence than BC (Andreae and
Gelencsér, 2006). Radiative forcing of BC is estimated to be 0.2–1.2 W m-2 (Moffet and Prather, 2009), while radiative forcing of
BrC contribution is suggested to be up to 0.25 W m-2 and contribute up
to 19 % of the total atmospheric absorption computed by model simulations
(Feng et al., 2013). As a part of WSOC, water-soluble
BrC is rich in the biomass burning smoke (Washenfelder et al., 2015).
Taken together, biomass burning can affect climate and aerosol chemical
compositions in an extremely complex way. All effects mentioned above are
both regional and global owing to the aerosol long-range transport.
Numerical studies have shown that emissions from biomass burning can be
transported to remote sites, even across the oceans (Aouizerats et al.,
2015; Zhu et al., 2016; Ancellet et al., 2016). Aouizerats et al. (2015) modeled a large fire-induced haze episode in 2006 stemming
mostly from Indonesia using the Weather Research and Forecasting model
coupled with chemistry and found a notable impact of long-range
transported aerosols from Indonesia on ambient air quality and climate in
Singapore. Ancellet et al. (2016) made the assessment of aerosol
transport from North America to Europe using forward simulations of the
FLEXPART Lagrangian particle dispersion model.
Due to the significant role of carbonaceous aerosols in biomass burning
plumes, it is important to quantify the contribution from biomass burning to
carbonaceous species such as OC, EC, WSOC and BrC. In the observation-based
studies, the characteristic ratio and positive matrix factorization are
frequently used to calculate biomass burning contribution. OC and EC
proportions released by biomass burning were shown to be 45±12 %
and 12±7.3 %, respectively, in the harvest season in Daejeon,
Korea (Jung et al., 2014). These numbers are comparable with the
results for the harvest seasons in the Yangtze River Delta region, China, where
biomass burning contributed 51 % and 16 % to OC and EC concentrations,
respectively (D. Chen et al., 2017). The share of OC in PM2.5
produced by biomass burning in Beijing ranged from 18 % to 70 % in
different seasons and areas (Duan et al., 2004; T. Zhang et al., 2008;
Cheng et al., 2013). In addition, a large quantity of BrC with
light-absorbing properties was detected in biomass burning emissions in southern
Asia and the United States, and in some controlled wood pyrolysis
experiments (Bosch et al., 2014; Chen and Bond, 2010; Hecobian et al.,
2010; Kirchstetter et al., 2004). It was reported that 23±7 % and
16±7 % of WSOC originated from biomass burning in Beijing, in
winter and summer, respectively (Yan et al., 2015). Meanwhile,
the contributions of biomass burning to WSOC in the PM10 in Mexico ranged
from 7 % to 57 % (Tzompa-Sosa et al., 2016). These
studies have demonstrated important contributions of biomass burning to
carbonaceous aerosols. Levoglucosan (lev) is recommended to be a biomass
burning tracer because it is uniquely derived from cellulose pyrolysis
(Simoneit et al., 1999, 2004; Simoneit, 2002).
Levoglucosan has been initially quantified by gas chromatography–mass
spectrometry (GC-MS) with a relatively complex operation procedure and at a
high cost (Fraser and Lakshmanan, 2000; Graham et al., 2003; Oros and
Simoneit, 2001). With the improvement of technology, a new detection method,
named high-performance anion-exchange chromatography coupled to pulsed
amperometric detection (HPAEC-PAD), has emerged (Engling et
al., 2006). The HPAEC-PAD approach is as reliable as traditional methods
(such as GC), but with a higher efficiency, an easier operation and a lower
cost. The new method can simultaneously quantify other carbohydrates in
ambient samples, including mannosan (man) and galactosan (gal), which are also
anhydrosugars and used as tracers of biomass burning like levoglucosan
(Engling et al., 2006; Jung et al., 2018; Tsai et al., 2015).
Additionally, non-sea-salt potassium (nss-K+) is also a tracer of
biomass burning, calculated by the formula nss-K+=K+-0.0335×Na+, to exclude K+ originating from seawater
(Lai et al., 2007; Cao et al., 2016). Nowadays, the above four tracers
are applied to both qualitatively and quantitatively investigate the biomass
burning impact. Furthermore, their characteristic ratios help researchers to
better recognize the biomass fuel types (Puxbaum et al., 2007; Mochida et
al., 2010; Wang et al., 2011; Cheng et al., 2013, 2014; Zhang et al., 2012)
Biomass burning aerosol has significant impacts on regional and global
radiative forcing (Ramanathan and Carmichael, 2008). There is no
doubt that elemental carbon increases the solar absorption of ambient aerosols
most effectively (Ramanathan and Carmichael, 2008; Myhre and Samset,
2015; K. Li et al., 2016). Lack et al. (2012) found that the
absorptivity of BC and primary organic matter mixtures has 70 %
enhancement in biomass burning periods compared to normal days in Boulder,
Colorado. Meanwhile, the role of BrC in the radiation balance of the climate
system has attracted increasing interest. Shamjad et al. (2015) mentioned that the atmospheric radiative forcing of
total aerosol increases by 56 %, and the forcing of BrC aerosol increases
by 38 % in the biomass burning season comparing to other seasons in
Kanpur, India. Light absorption characteristics of WSOC aerosols are key
factors in the climate forcing calculation. Light-absorbing parameters,
including the absorption coefficient, mass absorption efficiency and absorption
Ångström exponent, have been involved to describe the light absorption
characteristics of BrC (Alexander et al., 2008; Yang et al., 2009;
Chakrabarty et al., 2016; Choudhary et al., 2017; Srinivas et al., 2016).
Hoffer et al. (2006) found that the contribution
of humic-like substances to total aerosol absorption was as high as 50 %
at the wavelength of 300 nm in the Amazon basin. Hecobian et al. (2010) measured the optical properties of WSOC in Atlanta, US,
and their results showed that the absorption coefficient around 365 nm had a
strong correlation with WSOC from biomass burning or from urban emissions.
Du et al. (2014b) calculated mass absorbance efficiency for WSOC
from a biomass burning factor derived from a positive matrix factorization
receptor model. It showed that the biomass burning source contributed 58 % of total light absorption at 365 nm and WSOC associated with sulfate
and oxalate contributed 21 %. Using a simplistic absorption-based
model, Kirillova et al. (2014b) estimated the relative absorptive
forcing of WSOC compared to EC in the winter season at New Delhi, India. Their
results indicated that the contribution of WSOC to total absorption was
between 3 % and 11 %. C. Li et al. (2016) applied a similar
method in the Tibetan Plateau and found that the radiative forcing caused by
WSOC was 6.03±3.62 % and 11.41±7.08 % of that caused
by EC at two stations there, respectively. The aforementioned method
provides a rough estimation of the absorption of solar energy by WSOC and
EC, by using the measured sample concentrations of WSOC and EC and the
optical properties of WSOC. Many assumptions were applied in that method,
and thus uncertainties in the estimated forcing could be substantial.
Additionally, Jung et al. (2015) evaluated the instantaneous
forcing of WSOC. They calculated the aerosol optical properties and applied
a simplified expression to account for the radiative transfer process. Their
results showed that the radiative forcing of WSOC aerosol (assumed
core-shell mixing with inorganic components) ranged from -0.07 to -0.49 W m-2 at the top of the atmosphere (TOA). In a recent study, Panicker et al. (2017) applied an aerosol optical model embedded in a radiative
transfer model to investigate the radiative forcing for OC and EC under two
urban environments in northern India. The model can produce quite realistic
results as long as the default database and user-specified inputs represent
the real atmospheric conditions in the model. In this study, we
estimate the radiative forcing of WSOC and EC with a radiative transfer
model.
Physical and optical properties of carbonaceous aerosols alter with the
transport process and modify their radiative forcing accordingly. For
example, pollutants from biomass burning in northern Asia and North America can
be transported to the Arctic with the poleward jet at midlatitudes and have
non-negligible effects on Arctic climate (Shindell and Faluvegi,
2009). During the transport process, BC and BrC are prone to being internally
mixed with other gas and particle pollutants from biomass burning, resulting
in multiplication of the absorptivity due to the lensing effect
(Bond and Bergstrom, 2006). Using a novel chamber method, Peng et
al. (2016) found two stages in the aging of BC: initial transformation with
little absorption variation and subsequent growth with a profound absorption
enhancement. It was also revealed that BC in areas with a more polluted urban
background had a more remarkable impact on the pollution development and the
radiative forcing enhancement (Peng et al., 2016). Model
simulations calibrated by chamber measurements showed significant
differences in BC burden and radiative forcing from the simulations without
considering BC aging. In addition, BC coating materials from aging processes
were found to be responsible for a net increase in BC radiative forcing
(Y. Wang et al., 2018). Quantification of BrC property change with
the transport of biomass burning aerosols and estimation of its contribution
to the regional radiative forcing are both essential for better
understanding the climate effects of BrC. So far only a few studies have
focused on the light-absorbing properties of BrC from biomass burning in China
(Cheng et al., 2011; Du et al., 2014b; Yan et al., 2015). It was shown
that over short wavelengths (300–400 nm), light absorption by WSOC, which is
often used as the surrogate for water-soluble BrC, was ∼40 % and ∼25 % of that by EC in winter and summer,
respectively (Yan et al., 2015). However, the BrC light
absorption quantification due to biomass burning has not been identified.
China, the top crop-producing country in the world, has a history of burning
crop residuals (Zhang et al., 2016), making China a hotspot of biomass
burning research. As soon as it became evident that biomass burning emission
is detrimental to the environment and human health, China's government
introduced policies to prohibit straw burning. These policies are now
implemented strictly, so that open biomass burning has been quite rare in
recent years, especially in eastern China. Under this background, this study
is mainly aimed at a wintertime pollution event in eastern China and intended to
determine the origin of biomass burning aerosols and figure out the impacts
of regionally transported biomass burning on chemical and optical properties
of carbonaceous aerosols. To achieve these goals, 3 h PM2.5 samples
have been collected and analyzed to determine the aerosol chemical
compositions and light-absorbing properties. The solar energy absorption of
carbonaceous aerosols was estimated by the calculation based on measured
light-absorbing parameters and the simulation based on a radiative transfer
model (RRTMG_SW).
MethodSite description and sample collection
The observation site NUPT (32.09∘ N, 118.78∘ E) was
located in the campus of Nanjing University of Posts and Telecommunications.
It is in an urban area of eastern China, one of the China's economic
centers and also one of the world's most advanced manufacturing bases. This
site represents a severely anthropogenically influenced environment, with a
complex pollution pattern contributed by vehicles, industries, biomass
burning and atmospheric transport.
The sampling campaign was conducted from 14 to 30 January 2015.
PM2.5 samples were collected at a flow rate of 1.05 m3 min-1
with a nominal sampling time of 3 h for each sample. Blank filters were kept
in the sampler for 1 min, without air flow, at both the beginning and the
end of this campaign. All filters used for sample collection were quartz
fiber filters, which had been prebaked at 450 ∘C for 6 h to
eliminate organic material.
Sample analysis for carbonaceous components
OC and EC were detected using a Desert Research Institute (DRI) Model 2001
Thermal Optical Carbon Analyzer (Atmoslytic Inc., Calabasas, USA). A portion
of sampled filter (0.53 cm2) was analyzed following the IMPROVE thermal
optical reflectance (TOR) protocol (Bao et al., 2017). For the
determination of WSOC, a portion of each filter with a size of 4.02 cm2
was removed from the parent filter and extracted with 10 mL ultrapure water
in a sonication bath for 30 min. The water extracts were filtered through
syringe filters (0.22 µm, ANPEL, Shanghai, China) for WSOC concentration
analysis by a TOC analyzer (TOC-L, Shimadzu, Kyoto, Japan) following the
WSTC-WSIC protocol. All the calculations related to the carbonaceous
components were blank corrected and the system error between DRI and TOC-L
analyzers was corrected by testing a sucrose solution of a specific
concentration.
Extraction and analysis of ion and carbohydrates
A portion of each sample filter (5.07 cm2) was cut off and extracted
with 10 mL ultrapure water (>18.2Ω) via ultrasonic
agitation (30 min). The extract solution was filtered to remove insoluble
materials and then used for ion analysis by ion chromatography on a
ThermoFisher Scientific ICS-5000+ system (US) equipped with a gradient
pump, a conductivity detector/chromatography compartment and an
automated sampler. The separation of cations was carried out on an
IonPac CS12A analytical column and an IonPac CG12A guard column with aqueous
methanesulfonic acid (MSA, 30 mM L-1) eluent at a flow rate of 1 mL min-1. While anions were separated on an IonPac AS11-HC analytical
column and an IonPac AG11-HC guard column using sodium hydroxide (NaOH)
gradient elution at a flow rate of 1.5 mL min-1: 0–3 min, 0.5 mM L-1; 3–5 min, 0.5–5 mM L-1; 5–15 min, 5–30 mM L-1; 15–20 min,
0.5 mM L-1.
The extraction procedure for carbohydrate analysis was basically same as that for
ions, except that the water volume was changed from 10 to 2 mL. The equipment
of the ion chromatography system used was largely the same, except that a
conductivity detector was replaced by an electrochemical detector. The
carbohydrates were separated by a CarboPac MA1 column and a matched guard
column using NaOH gradient elution at a flow rate of 0.4 mL min-1. To
determine levoglucosan and galactosan the elution was run at a
time gradient: -15–34 min, 300 mM L-1; 34–45 min, 300–480 mM L-1;
45–60 min, 480 mM L-1. For mannosan, the elution was run at another
time gradient: -15–5 min, 50 mM L-1; 5–25 min, 250 mM L-1; 25–35 min, 250–350 mM L-1; 35–40 min, 350–650 mM L-1; 40–70 min, 650 mM L-1. The volume of the sample loop was 200 µL. All ions and carbonaceous
components amount calculations were corrected by the mean value of two field
blanks.
Extraction and analysis of WSOC light-absorbing property
A fraction of sampled filter (5.02 cm2) was cut and extracted with 2 mL
ultrapure water. The filter extract was tested for light absorption by an
ultraviolet-visible absorption spectrophotometer (UV-2600, Shimadzu, Kyoto,
Japan) with a scanning wavelength range of 200–800 nm. The absorption
coefficient (babs, M m-1 or 10-6 m-1) at 365 nm was
calculated by Eq. (1):
babs=(A365-A700)×(Vwater×factor)×ln(10)÷(Vaero×L),
where A365 and A700 refer to absorbance (or light attenuation) at
365 and 700 nm, respectively, measured by the spectrometer; Vwater (mL)
corresponds to the volume of the aqueous extract (water); the factor is set to 103,
which is estimated from the absorption signal for the full filter;
Vaero (m3) refers to the volume of air filtered; and L (mm) is the
path length of the cell (10 mm). The choice of 365 nm as the reference
wavelength to represent light absorption of WSOC is made according to the
strong light-absorbing capacity and also is meant to avoid light-absorbing
disturbance by other substances in the extract. Absorbance at 700 nm
represents the baseline drift in the analysis (Bosch et al., 2014; Cheng
et al., 2011; Hecobian et al., 2010). Mass absorption efficiency (MAE,
m2 g-1) of BrC in WSOC was derived from Eq. (2):
MAE=babs/WSOC,
where WSOC refers to the concentration of WSOC (µg m-3).
Absorption Ångström exponents (AAE) were computed by Eq. (3):
babs≈K⋅λ-AAE,
where K is a constant, AAE describes the wavelength-dependent absorption
enhancement of BrC, associated with its origin, size and composition
(Bikkina, 2014); and λ is wavelength in the range of
310–460 nm. AAE for WSOC was fitted within the range of 310–460 nm in which
coefficients of determination (R2) for all samples are above 0.90. The
solar energy absorbance of WSOC and EC at the ground level was estimated
using Eqs. (4) and (5):
4EWSOC=∫Iλ⋅{1-e-(MAE⋅(365λ)AAE⋅WSOC⋅hABL)}dλ,5EEC=∫Iλ⋅{1-e-(MAEEC⋅(550λ)⋅EC⋅hABL)}dλ,
where I(λ) is the clear-sky Air Mass 1 Global Horizontal (AM1GH) solar
radiance spectrum at the surface; hABL refers to the vertical height of the
boundary layer (set to 1000 m); MAEEC is set as 7.5±1.2 m2 g-1, which is the MAE of EC at 550 nm; and EC (µg m-3) is the EC
concentration in each sample. The fraction EWSOC/EEC is the ratio of
EWSOC and EEC, comparing the light-absorbing capacity of BrC in WSOC and
EC. Here EWSOC, EEC and EWSOC/EEC were computed in a
wavelength range of 300–400 nm (Bosch et al., 2014; Kirillova et al.,
2014b; Bond and Bergstrom, 2006; Yan et al., 2015).
Radiative forcing estimation with model
We also estimated the radiative forcing of WSOC and EC with a stand-alone
radiative transfer model RRTMG_SW (Global climate model
version of Short Wave Rapid Radiative Transfer Model) (Iacono
Michael et al., 2000). The correlated-k approach was applied in
RRTMG_SW to calculate radiative fluxes and heating rates in
14 contiguous bands in the shortwave (820–500 00 cm-1). The stand-alone
RRTMG_SW can be driven by a given atmospheric profile. In
this study, we prescribed the midlatitude winter atmospheric profile, which
is default in the RRTMG package (Iacono Michael et al., 2008; Mlawer and
Clough, 1998). We chose the average solar zenith angle of 60∘ in
the midlatitude regions (Li, 2017) and ran RRTMG_SW
for clear-sky conditions without the impact of cloud.
Aerosol radiative (absorptive) properties for WSOC and EC are parameterized
in terms of their mass concentrations in RRTMG_SW. The optics
per unit mass (mass absorption efficiency, MAE*; single scattering
albedo, SSA*; and asymmetry factor, g*) of EC was
provided from the Optical Properties of Aerosols and Clouds (OPAC) dataset
(Hess et al., 1998). For WSOC, we calculated the optics
using the Mie model (Bohren and Huffman, 1998; Pruppacher and
Klett, 1997) with the input optics and prescribed size parameters.
Refractive index (RI) was set to be 1.55–0.112i (Kirchstetter et al.,
2004; Shamjad et al., 2016). The density of WSOC is 1.569 g cm-3
(Feng et al., 2013). The dry-mode radius and standard
deviation of the WSOC particle size distribution were assumed to be 0.0212 µm and 2.24, respectively (Hess et al., 1998).
Because WSOC is hydrophilic, we also calculated the wet particle radius and
wet RIs according to Pruppacher and Klett (1997). The optics of WSOC
obtained from the Mie model were provided to RRTMG_SW for
radiative calculations.
Footprint analysis, PSCF models and fire hotspots
The FLEXPART Lagrangian particle dispersion model was used to estimate the
footprint of the site (Stohl et al., 1998, 2005; Grythe et
al., 2017). FLEXPART version 10.1 was run in the backward model in which the
potential emission sensitivity (PES) of the receptor point is provided
(Seibert and Frank, 2004). The operational reanalyzed data from
the European Centre for Medium-Range Weather Forecasts (ECMWF) at a spatial
resolution of 1∘×1∘ with 61 vertical levels
were used as meteorology. The simulation considered BC (corresponding to EC
in this study) as the tracer species, and dry deposition and wet scavenging
were accounted for. The output was set as the retention time(s) of particles
in each grid during the simulation period. The HYSPLIT 4.8 model from NOAA was
used to compute 48 h backward trajectories of air masses reaching Nanjing
sites at a planetary boundary layer (PBL) height of 500 m. MODIS fire
hotspot data were applied to evaluate open biomass burning intensity during
the study period. The Potential Source Contribution Function (PSCF) model
was usually applied to localize the potential sources of pollutants. The
details about the setup of the model can be seen in the research of Bao et
al. (2017). Here we introduce the light absorbance of WSOC into
the model. The higher the PSCF value, the higher the possibility that the areas
make potential contributions to the light absorbance of WSOC in the aerosols
at the receptor site.
Time series of OC, EC, WSOC, levoglucosan (lev), mannosan
(man), galactosan (gal) and non-sea-salt potassium
(nss-K+) concentrations as well as OC/EC and WSOC/OC ratios for
PM2.5 samples. The horizontal ordinate indicates
the ending time for each sample.
Results and discussionCarbonaceous components
The temporal variations of OC/EC ratios and OC and EC concentrations in
PM2.5 during the studied episode are plotted in Fig. 1. The average
concentrations of OC and EC were 19.1±8.6 and 6.6±3.5µg m-3, respectively (Table 1). Our results are comparable to
the seasonal mean levels of OC and EC in PM2.5 in the winter of 2015 in
a nearby site in Nanjing, which were 22.5±9.6 and 8.2±3.1µg m-3, respectively (Li et al., 2015). OC
shows a robust relationship with EC (R2=0.90, p<0.01),
which underlines the significant contribution of fuel burning to OC, since
EC is only released by inefficient combustion (Liu et al., 2014a).
In this study, EC peaked at 17:00 LT on 21 January and 20:00 LT on 24 January, implying
intensive fuel burning at those times. OC ranged from 5.5 to 45.8 µg m-3 and EC ranged from 0.8 to 20.1 µg m-3. The relative
standard deviation was used to represent the variation of OC (44 %) and
EC (53 %), suggesting that EC had a larger uncertainty than OC. It
indicates that EC came from a variety of combustion sources which were
dynamic and unstable. The variation of OC and EC over time led to the
significant changes in PM2.5 levels.
Concentration and mass ratio of atmospheric gaseous
components and particulate species (PM2.5), biomass
burning contribution to carbonaceous aerosol, light-absorption parameters of
water-soluble BrC, and solar energy absorption of carbonaceous aerosols.
* Refers to values derived from the RRTMG_SW model
simulation.
The OC/EC ratios varied from 2.0 to 7.3, with a mean value of 3.2±0.8. The OC/EC ratio is usually used as the indicator of secondary organic
carbon (Hou et al., 2011; Zeng and Wang, 2011). According to the temporal
variation of the OC/EC ratio in Fig. 1, the impact of secondary organic
carbon varied in different stages of the pollution episode. The OC/EC ratio
variation range was 2.0–7.3 when the atmosphere was cleaner. Generally, an
OC/EC ratio above two means a significant contribution of secondary organic
aerosol (SOA) (Haque et al., 2019; Kunwar and Kawamura, 2014). As the
pollution got more severe over time, it shrank to 2.0–4.0 after 16 January,
indicating that the secondary sources had a relatively stable but still
considerable impact on PM2.5 at the polluted stage. According to the
previous studies, secondary organic aerosol usually played an important role
in the wintertime air pollution (Huang et al., 2014; Sun et al., 2013).
Temporal variations of WSOC concentration and WSOC/OC ratio are illustrated
in Fig. 1. The average concentration of WSOC in the event was 9.7±4.3µg m-3 and WSOC varied from 2.2 to 23.2 µg m-3. WSOC
was at a higher level in this pollution episode than in the episodes
reported by Du et al. (2014a). It was reported that WSOC was
abundant in biomass burning emissions in previous studies (Jaffrezo et
al., 2005; Park and Cho, 2011). In addition, a strong correlation was also
found between OC and WSOC (R2>0.70, p<0.01), and
the WSOC/OC mass ratio was averaged to 0.5±0.1, a value
consistent with that of another study (Bikkina, 2014). If a major
portion of OC was derived from combustion, WSOC could be very likely
released from fuel burning. Moreover, WSOC is an important form of BrC,
which is able to absorb light radiation. It seems that biomass burning can
emit BrC, increasing the light-absorbing capacity of aerosols.
Chemical species related to fossil fuel combustion
To explore the combustion sources, the roles of traffic and industrial
emissions, which are main fossil fuel combustion sources in the urban area
(Cao et al., 2005), were first
investigated. Nitrogen dioxide (NO2) is the main chemical component in
motor vehicle exhaust (Kendrick et al., 2015), and sulfur
dioxide (SO2) is usually treated as the tracer for coal combustion
widely existing in industrial activities (Akimoto and Narita,
1994). Therefore, the sum of NO2 and SO2 concentrations can
represent the contribution of fossil fuel combustion to the atmospheric
pollutants. As shown in Fig. 2, carbon monoxide (CO) and the sum of NO2
and SO2 concentrations have a positive correlation, but with a low
correlation coefficient (R2=0.40, p<0.01). The same
pattern is also shown between EC and sum of nitrate (NO3-) and
sulfate (SO42-) in PM2.5 (R2=0.46, p<0.01). These patterns suggest that fossil fuel was not the only main type of
fuel and that biomass fuel might have been burned during the episode.
Correlations of carbon monoxide (CO) with nitrogen dioxide
(NO2), sulfur dioxide (SO2)
and the sum of nitrogen dioxide and sulfate dioxide (NO2+SO2) in ambient air, and correlations of
EC with nitrate (NO3-), sulfate
(SO42-) and the sum of nitrate and sulfate
(NO3-+SO42-) in PM2.5
from 14 to 29 January 2015 in Nanjing.
Biomass burning tracers
Tracers of biomass burning have been introduced to describe the role of
biomass burning in this event. As shown in Fig. 3, all tracers strongly
correlate with each other (R2>0.60, p<0.01). But
there is a more significant correlation between anhydrosugars (R2>0.75, p<0.01) than that between nss-K+ and
anhydrosugars (0.61≤R2≤0.67, p<0.01). Because
of the non-unique origin of potassium, nss-K+ seems a weak tracer of
biomass burning, in agreement with other published results. K+ has
additional significant sources other than biomass burning, such as seawater,
soil resuspension and fertilizers (Urban et al., 2012).
Furthermore, K+ is abundant in firework aerosols according to several
recent studies (Cheng et al., 2013; Drewnick et al., 2006). Among all
three types of anhydrosugars, levoglucosan was treated as the main tracer in
this study because of its larger quantity in PM2.5 than the other two.
Correlations between four tracers of biomass burning
including levoglucosan (lev), mannosan (man), galactosan (gal) and
non-sea-salt potassium (nss-K+) in
PM2.5 from 14 to 29 January 2015 in Nanjing.
Time series plots of nss-K+, levoglucosan, mannosan and galactosan in
PM2.5 are shown in Fig. 1. In this study, nss-K+ varied from 0.2
to 3.8 µg m-3, with a mean level of 1.2±0.7µg m-3. A consistent trend between nss-K+ and EC can be seen in Fig. 1, especially with both peaks of EC and nss-K+ during 14:00–17:00 LT on
21 January (15.7 and 3.8 µg m-3) and during 17:00–20:00 LT on 24 January (20.1 and 3.5 µg m-3), suggesting that the intensities of total
fuel combustion and biomass fuel combustion changed synchronously.
The average concentration of levoglucosan in PM2.5 was 373±268 ng m-3 in this study, which was significantly higher than those
observed at the coastal sites in Europe (Puxbaum et al., 2007; von
Schneidemesser et al., 2009), the forest sites in the eastern China
(W. Wang et al., 2008) and some rural sites
in Canada and Hong Kong (Leithead et al., 2006; Sang et al., 2011). But
for the rural sites of Australia, levoglucosan was found to be much more enriched
than in this study (Reisen et al., 2011). For the urban
areas, levoglucosan levels in this study were higher than the level in
Shanghai, Hong Kong, Singapore and the southeastern US (X. Li et
al., 2016; Sang et al., 2011; Yang et al., 2013; X. Zhang et al., 2010) but
was lower than those measured in Grenoble, France, and also Beijing during
winter and a biomass burning episode in summer (Cheng et al., 2013; Favez
et al., 2010) (Table 2).
Comparison of levoglucosan concentration level in
PM2.5 during this study with those reported in the
literature.
Sampling siteSite typeSampling timeConcentrationReference(ng m-3)Summit, GreenlandcoastalMay–December0.3von Schneidemesser et al. (2009)Azores, PortugalcoastalWinter6.6Puxbaum et al. (2007)Sonnblick, AustriacoastalWinter12.4Puxbaum et al. (2007)Puy de Dôme, FrancecoastalWinter18.3Puxbaum et al. (2007)Schauinsland, GermanycoastalWinter33.7Puxbaum et al. (2007)Guangdong, ChinaforestAugust25 (0.3–61)W. Wang et al. (2008)Jilin, ChinaforestJuly42 (32–67)W. Wang et al. (2008)Hainan, ChinaforestNovember107 (19–398)W. Wang et al. (2008)Shanghai, ChinaforestJune143 (20–212)W. Wang et al. (2008)Langley, CanadaruralAugust26Leithead et al. (2006)Hok Tusi, Hong KongruralSpring30Sang et al. (2011)Ovens, AustraliaruralAutumn870Reisen et al. (2011)Manjimup, AustraliaruralAutumn1060Reisen et al. (2011)Kowloon, Hong KongurbanSpring36Sang et al. (2011)National University, SingaporeurbanSeptember91.2Yang et al. (2013)Shanghai, ChinaurbanSpring66 (18–159)X. Li et al. (2016)Shanghai, ChinaurbanSummer28 (8.6–194)X. Li et al. (2016)Shanghai, ChinaurbanAutumn229 (13–1606)X. Li et al. (2016)Shanghai, ChinaurbanWinter161 (26–614)X. Li et al. (2016)Southeastern US, USAurbanWinter204.5X. Zhang et al. (2010)Beijing, ChinaurbanSummer230Cheng et al. (2013)Beijing, ChinaurbanBB episode750Cheng et al. (2013)Beijing, ChinaurbanTypical summer120Cheng et al. (2013)Beijing, ChinaurbanWinter590Cheng et al. (2013)Beijing, ChinaurbanFirework episode460Cheng et al. (2013)Beijing, ChinaurbanTypical winter640Cheng et al. (2013)Grenoble, FranceurbanJanuary815Favez et al. (2010)Nanjing, ChinaurbanWinter373 (22.4–1476)Current study
The concentration range of levoglucosan in PM2.5 of our study was
22.4–1476 ng m-3. The maximum value approximately approached that
observed in Shanghai in the autumn when biomass burning prevailed (1606 ng m-3, Table 2). The highest level of levoglucosan appeared during
05:00–08:00 LT on 25 January, implying that the strongest biomass fuel combustion
occurred in this period. Remarkably, levoglucosan concentration presented a
valley during 11:00–14:00 LT on 19 January, when EC presented a peak. It can be
explained that the intense combustion at that time was not dominated by
biomass burning. The discrepancy of the time distribution between nss-K+ and levoglucosan were again attributed to the diversity of origins of
potassium.
The concentrations of mannosan and galactosan in PM2.5 were averaged
to 18.5±12.5 and 9.9±6.7 ng m-3, with ranges
of 2.1–56.2 and 1.4–32.2 ng m-3, respectively. The trend of
mannosan, galactosan and levoglucosan were closely matched, as illustrated
in Fig. 1. They reached their highest and second highest levels together,
which highlighted the fact that biomass burning heavily impacted the
compositions of aerosols in these two periods.
Biomass burning contribution to carbonaceous components
Figure 4 shows the correlations between carbonaceous components (OC, EC or
WSOC) and biomass burning tracers (levoglucosan, mannosan, galactosan or
nss-K+). All biomass burning tracers are associated with EC (0.40<R2<0.80, p<0.01), which demonstrates the
conjecture that biomass burning was one of the main types of combustion.
Moreover, biomass burning tracers exhibit strong correlations with OC and
WSOC (0.40<R2<0.80, p<0.01), indicating
that biomass burning might have contributed to organic carbon including
WSOC. For each tracer, the higher coefficient of determination (R2) for
OC than EC illustrates that the biomass burning impacts on OC were more
significant than those on EC. Notably, the slopes and the intercepts of the
trend line for WSOC are both about half of those for OC, in accordance with
results shown in Sect. 3.1 that WSOC accounted for around 50 % of OC. As
a consequence, biomass burning made a stable contribution to WSOC and the
contribution was even larger than that to EC in this episode.
Correlations of OC, EC and WSOC vs. levoglucosan (lev),
mannosan (man), galactosan (gal) and non-sea-salt potassium
(nss-K+) in PM2.5 from 14 to
29 January 2015 in Nanjing.
The contribution of biomass burning to OC in PM2.5 (BB-OC) was
calculated using Eq. (6):
BB-OC=(lev/(1000×OC))ambient0.082×100%,lev:ngm-3;OC:µgm-3,
(Z. Zhang et al., 2010; Puxbaum et al., 2007; Zdráhal et al., 2002;
Sang et al., 2011). In biomass burning source emission tests for three major
types of cereal straw (corn, wheat and rice) in China, 0.082 was reported
as the lev/OC ratio for PM2.5 (Zhang et al., 2007). This
value can be used in combination with the lev/OC ratios of our PM2.5
samples to roughly estimate the contribution of biomass burning smoke to the
ambient OC. The reason why we used the ratio lev/OC associated with cereal
straw burning will be explained in Sect. 3.5. As shown in Table 1, BB-OC ranged from 0.2 % to
53.6 % in this event, with an average of 20.9±9.3 %. Our
calculated biomass burning contribution has a greater span than that of
the Beijing rural area (18 %–38 %), which was computed with the same lev/OC
ratio (T. Zhang et al., 2008; Y. Zhang et al., 2008), even though January was
not in a common biomass burning prevailing season of Nanjing.
The contribution from biomass burning to the WSOC in PM2.5 (BB-WSOC)
trend in Fig. 5 makes the above analysis more convincing due to the
consistency of the contributions computed by two different methods. BB-WSOC
was estimated with Eq. (7):
BB-WSOC=lev/WSOCambient0.17×100%,lev/WSOC:µgµgC-1,
which used a lev/WSOC ratio of 0.17 µg µgC-1 from the test
burns of rice straw and wheat straw (Yan et al., 2015). BB-WSOC
ranged from 1.1 % to 55.4 %, with an average of 22.3±9.9 %.
It was at an equivalent level with that investigated in Beijing wintertime,
where BB-WSOC was averaged at 23±7 % (Yan et al.,
2015) and had a pretty large span. BB-WSOC exhibits a similar trend with
BB-OC (Fig. 5). The robust relationship between BB-OC and BB-WSOC (R2=0.81, p<0.01) with a regression slope close to 1 (0.96)
confirms the reliability of our biomass burning contribution quantification.
However, the biomass burning contribution here could be related to both local
emissions and long-range transport due to the atmospheric motion and the
subsequent mixing of aerosols. The origins of biomass burning will be
discussed in the following section.
Time series of biomass burning contribution to OC (BB-OC)
and WSOC(BB-WSOC) and the correlation between them.
Origin of biomass burningMajor fuel types of biomass burning
The biomass burning characteristics, expressed in the parameter space of
lev/man and lev/nss-K+ in Fig. 6, are used to differentiate the burning
substrates. The ratio space for needle, duff, hardwood, softwood and crop
residuals was introduced from the work of Cheng et al. (2013),
which overcomes the limitation of using only one characteristic ratio
(either lev/nss-K+ or lev/man) to distinguish the types of biomass being
burned and hence increases the reliability of determination. In previous
studies, emissions from the crop residuals burning were characterized by a
lower lev/nss-K+ ratio (mostly less than 1) and a higher lev/man ratio
which was reported to be ∼20 in general and could be as high as 41
(Sheesley et al., 2003; Sullivan et al., 2008; Engling et al., 2009; Oanh
et al., 2011).
Representative ranges of
lev/nss-K+ and lev/man ratios for
different types of biomass fuel introduced by Cheng et al. (2013). Results
from the ambient samples collected in this study are also shown for
comparison.
In the present study, the mean lev/man ratio was 22.5±12.3 and the
mean lev/nss-K+ was 0.3±0.1. According to Fig. 6,
∼94 % of the ambient samples (96 out of 102) in this study
are traced to crop residuals and grass region, demonstrating that crop
residuals were the main biomass type burned in the investigated episode, since
the contribution of grass was negligible compared to the total biomass
consumed in China (Streets et al., 2003). Our
result is similar to that reported for the typical biomass burning events in
summer in Beijing (Cheng et al., 2013).
Air mass backward trajectory from 21 to 25 January, along with
the MODIS fire spot map. Percentage refers to the air mass contribution to
Nanjing and values with a unit of nanograms per cubic meter (ng m-3) refers to the mean concentration of levoglucosan for every trajectory in that cluster.
Long-range transport
In order to examine the influence of long-range transport on local
atmospheric compositions, 48 h backward trajectories of air masses reaching
the Nanjing sites at a height of 500 m were computed via the HYSPLIT model
covering all the sampling days. Figure 7 displays the MODIS fire spot
distribution during 21 to 25 January, when open burning was strong in China.
No fire signals were found in the local region of Nanjing, suggesting the
dominant influence from long-range transport. Some fire spots appeared in
northern China, but densely distributed spots were in southeastern China. All
48 h backward air mass trajectories were classified into four clusters, and
mean levoglucosan concentrations of all trajectories for each cluster are
described in Fig. 7, in order to compare the relative levels of biomass
burning emissions transported by the four clusters. The cluster originating
from southeastern China had the highest mean levoglucosan concentration (796 ng m-3) among the four clusters, although it transported the least air
mass to Nanjing (20 %). Moreover, when the maximum levoglucosan was
observed between 05:00 and 08:00 LT on 25 January, the FLEXPART potential emission
sensitivity in Fig. 8 indicates that a notable fraction of air masses was
coming from a region in the south and southeast of Nanjing. It is evident
that air masses caught the pollutants emitted from biomass burning on 21–24 January, as was illustrated by the hotspots. Therefore, the biomass burning
emissions from southeastern China profoundly impacted the aerosols in
Nanjing during the episode.
The four-day footprint of NUPT (32.09∘ N, 118.78∘ E, black point), the observation site, starting from
05:00–08:00 LT on 25 January 2015 when the maximum levoglucosan was observed.
The footprint was illustrated as FLEXPART potential emission sensitivity,
shown as the retention time in each grid at 0–500 m contributing to the
observed elemental carbon.
In addition, there was a notable cluster originating from the coastal site
of eastern China and mainly passing through Shanghai and Jiangsu, where no
fire signals were found. But this cluster had a relatively high mean
levoglucosan concentration (673 ng m-3). According to the research by
Zhou et al. (2017) on the biomass burning emission inventory, domestic
straw burning was responsible for ∼42 % of total biomass
burning in China in 2012, and ∼1.5 Gg and ∼80 Gg PM2.5 came from domestic burning in Shanghai and Jiangsu,
respectively. It illustrated the significant impact of indoor biomass
burning which was invisible in MODIS fire spot map because satellites can
only detect open biomass burning. Therefore, the levoglucosan enrichment of
the cluster from coastal area might be due to domestic biomass burning. The
air mass frequency of this cluster was 32.5 %, the largest one among that
for all clusters, which means domestic biomass burning in the eastern China
much influenced aerosols in Nanjing. Domestic biomass burning should be
taken as an important factor to study aerosols of eastern China in further
studies, even though open biomass burning from long-range transport played a
significant role in this study.
Light-absorbing property
The light absorption coefficient of WSOC at 365 nm is used to represent
water-soluble BrC, which is organic carbon with a capacity of absorbing
radiation (Yan et al., 2015; Hecobian et al., 2010; J. Wang et al., 2018).
The mean babs value was 9.4±4.8 M m-1, higher than the
summertime value but lower than the wintertime value in Beijing and
Guangzhou (Yan et al., 2015; Qin et al., 2018). Meanwhile, it had a large
span from 1.6 to 30.0 M m-1, partially attributed to its dependence on
biomass burning intensity. As shown in Fig. 9, babs is significantly
correlated with biomass burning tracers (R2>0.50, p<0.01), indicating that a considerable quantity of BrC was
associated with biomass burning emissions in this episode. According to the
analysis in Sect. 3.4, the robust correlations between WSOC and biomass
burning tracers (levoglucosan, mannosan, galactosan or nss-K+) imply
that biomass burning had contributed to WSOC. With respect to this point,
the correlation between babs and WSOC (R2=0.76, P<0.01) and that between babs and WSOC from biomass burning, calculated
with WSOC multiplied by the BB-WSOC fraction (R2=0.55, P<0.01), are presented in Fig. 10. Both correlations are significantly
positive at the 1 % possibility level. But babs has a relatively higher
R2 with WSOC than WSOC from BB, indicating that other sources also
contributed to water-soluble BrC in the episode. Other researchers pointed
out that secondary photochemical reactions in the atmosphere can produce
water-soluble BrC as well (Sareen et al., 2013; Liu et al., 2016).
Correlations of water-soluble BrC carbon absorption
coefficient (babs) vs. levoglucosan (lev), mannosan
(man), galactosan (gal) and non-sea-salt potassium
(nss-K+) in PM2.5 from 14 to
29 January 2015 in Nanjing.
It is evident that the absorption coefficient of WSOC shows a relationship
with wavelength, which can be described by the absorption Ångström exponent. AAE
is also used to distinguish the BrC emission types due to its dependence on
particle size and composition. Generally, the Ångström exponent for
compounds from biomass burning and biofuel emissions is larger than 6
(Hoffer et al., 2006; Chen and Bond, 2010; Hecobian et al., 2010;
Bikkina, 2014). The AAE mean in this study was 6.6±1.3, ranging from
4.1 to 10.3, and more than 68 % of samples (71 out of 104) had an
AAE above 6 (Fig. 13). Our result is consistent with those for the
Indo-Gangetic Plain, where biomass burning emission has been proven to be the
predominant aerosol source (Bikkina, 2014). If light absorption is
dominated by elemental carbon, AAE is reported to be ∼1
(Bond, 2001; Kirchstetter et al., 2004). For fresh and aged SOA it should
be ∼7 and ∼4.7, respectively
(Bones et al., 2010).
Mass absorption efficiency characterizes the efficiency of light absorption
by WSOC. As shown in Fig. 11, the MAE mean in this study was 1.0±0.2 m2 g-1, with a range of 0.5–1.6, higher than that of some US
cities. Beijing (summer) and the Indo-Gangetic Plain were effected by biomass
burning, but to a lesser extent than some Indian areas and Beijing,
China, during wintertime. The MAE was lower than 1.79 m2 g-1 in
Beijing during winter. As shown in Fig. 11, MAE has poor correlation with
AAE (Yan et al., 2015; Kirillova et al., 2014b; Kim et al., 2016; Du et
al., 2014b; Hecobian et al., 2010; Yan et al., 2017; Cheng et al., 2011, 2016;
Zhang et al., 2013; Bikkina, 2014; Srinivas et al., 2016).
Correlations of water-soluble BrC absorption coefficient
(babs) and WSOC or WSOC from biomass burning (WSOC
from BB) in PM2.5 from 14 to 29 January 2015 in Nanjing.
WSOC from BB is calculated with WSOC multiplied by BB-WSOC.
Comparison of mean mass absorbance efficiency (MAE) and
absorption Ångström exponent (AAE) of WSOC in PM2.5
in this study with those reported in the literature.
As shown in Fig. 13, the solar energy absorption of WSOC (EWSOC) in short
wavelengths (300–400 nm) had a mean value of 0.8±0.4 W m-2,
ranging from 0.2 to 2.3 W m-2. The counterpart contributed by biomass
burning (EWSOC_BB), derived from multiplying EWSOC
by BB-WSOC, had a mean value of 0.2±0.1 W m-2, ranging from 0.0
to 0.9 W m-2. Solar energy absorption of EC (EEC) in the
wavelength range of 300–400 nm had a mean value of 3.4±1.7 W m-2, ranging from 0.4 to 9.9 W m-2. The WSOC to EC absorption
percentage (EWSOC/EEC) was 23.8±8.7 % and could be up
to 73.0 % at the short wavelengths (300–400 nm), which is much higher
than that for Beijing winter (42 %) (Yan et al., 2015). The
map in Fig. 12 specifies the level of PSCF values related to the
carbonaceous compounds (levoglucosan, WSOC and EC) and their radiative
absorption properties (babs, EWSOC, EEC, EWSOC/EEC)
in terms of a color bar. The areas with high PSCF values were interpreted
as the potential areas loading the carbonaceous compounds or better light
absorption. As shown in Fig. 12, the water-soluble carbonaceous aerosols
transported to Nanjing over a long distance had a stronger light absorption
capacity, compared to those aerosols from local emissions. It is evident
that WSOC would enhance its light absorption capacity after aging along the
long-range air mass transport, and the enhancement is much more than that for
EC (Kirillova et al., 2014a). It means that the
carbonaceous emissions over long-range transport can affect the local atmospheric solar radiation balance more. In addition, the
levoglucosan has a similar PSCF distribution with WSOC and EC, illustrating
that long-range-transported biomass burning emissions profoundly impacted the
carbonaceous aerosols in Nanjing during this episode. The similar PSCF
distributions between WSOC and babs or EWSOC can be explained by
the fact that light absorption of WSOC is usually related to WSOC levels.
The similarity of PSCF distributions between EC and EEC can be
explained in the same way. However, the PSCF distribution of
EWSOC/EEC is different from EEC or EWSOC in Fig. 12,
indicating that southeastern China is potentially the area loading the aerosols
with a higher light-absorbing contribution by WSOC. It is consistent with
the fire spot distribution in Fig. 7, which implies that the long-range
transported biomass burning emission significantly impacted the solar energy
absorption of carbonaceous aerosols in Nanjing during this episode.
The PSCF map for levoglucosan (lev), water-soluble BrC
absorption coefficient (babs), WSOC, EC,
light absorption of WSOC (EWSOC) and EC
(EEC) and the WSOC to EC light-absorption ratio
(EWSOC/EEC) for
PM2.5 from 14 to 29 January 2015 in Nanjing
(black point).
Time series of water-soluble BrC absorption coefficient
(babs), mass absorbance efficiency (MAE), absorption
Ångström exponent (AAE), light absorption of WSOC
(EWSOC) and EC (EEC) and the WSOC to
EC light-absorption ratio
(EWSOC/EEC) for
PM2.5 from 14 to 29 January 2015 in Nanjing.
In Table 3, we show the optical properties (MAE*, SSA* and
g*) for EC and WSOC (at RH =0.7) in the band 26 (22 650–29 000 cm-1, 345–442 nm) as provided in the RRTMG_SW model. Based
on the surface mass concentration of EC and WSOC measured in this study,
RRTMG_SW simulated the respective absorptive properties in
the first layer of the model (the lowest layer above the ground), which
accounts for the aerosol absorption in the radiative transfer calculation.
To obtain the absorptive radiative forcing (ARF) of EC or WSOC,
RRTMG_SW was run twice by including and excluding EC or WSOC
in the model. The differences in the radiative flux between the two runs
indicate the absorptive radiative effect of EC or WSOC in the atmosphere.
Optics for EC and WSOC (RH =0.7) in the shortwave
band 26 (22 650–29 000 cm-1, 345–442 nm) provided in
RRTMG_SW.
In the wavelength range of 345–442 nm, the change in the net flux at the
surface caused by absorption of EC and WSOC was -3.4±1.6 and -1.4±0.6 W m-2, respectively. The ARF at the top of the atmosphere
(TOA) was 0.8±0.4 W m-2 for EC and -0.2±0.1 W m-2
for WSOC. Therefore, the ARF in the atmosphere caused by absorption of
EC (EEC*) and WSOC (EWSOC*) was 4.2±2.0 and 1.2±0.5 W m-2, respectively, which are slightly
higher than the previously estimated EEC and EWSOC, indicating that
the absorbing aerosols maintain a part of the solar energy in the
atmosphere. Our estimates from the model indicate that the absorption ability of
WSOC was equivalent to about 30 % of EC absorption in this study, which
is comparable to the ratio from previous studies. For example, Lin et al. (2014) simulated the global OC radiative forcing and indicated that
the absorption due to OC (both primary and secondary OC) aerosols is about
27 %–70 % of the EC warming effect in the global atmosphere. Liu et al. (2014b, 2015) investigated the absorption of OC aerosols at different
altitudes over the North American continent and found that OC absorption at
TOA is around 20 % of EC direct effect in the background troposphere. Our
study provides additional evidence and stresses the necessity to account
for the absorption capacity of WSOC in model simulations of the global
energy budget.
Conclusions
In this study, biomass burning tracers (including levoglucosan, mannosan,
galactosan and nss-K+) were used to quantify biomass burning
contributions to the carbonaceous components of PM2.5 in Nanjing during
a wintertime pollution event. The origin of biomass burning and biomass fuel
types were also investigated. Furthermore, solar energy absorption due to
biomass-burning BrC was quantified.
The levoglucosan concentration in PM2.5 in this study was up to 1476 ng m-3, which is significantly higher in comparison with those found in other
studies. Biomass burning contribution to OC and WSOC was, on average, 20.9±9.3 % and 22.3±9.9 %, respectively, and was as high as
53.6 % to OC and 55.4 % to WSOC, reflecting the large contribution of
biomass burning to carbonaceous aerosols in urban atmospheres during winter.
Both of the facts indicate the significant impact of biomass burning in this
atmospheric pollution episode, although no local open biomass burning was
found during the study period. The combination of long-range transport
analysis, fire spot information and the potential emission sensitivity of
BC suggests that the burning of crop residuals in southeastern China was
responsible for the significant biomass burning contribution to the ambient aerosols.
The mass absorption efficiency of water-soluble BrC at the 365 nm wavelength
was averaged to 1.0±0.2 m2 g-1, with a range from 0.5 to 1.6 m2 g-1. Based on the measured light-absorbing properties, solar
energy absorption of WSOC and EC in short wavelengths (300–400 nm) was
calculated to be 0.8±0.4 and 3.4±1.7 W m-2,
respectively, which were slightly lower than 1.2±0.5 W m-2 for
WSOC and 4.2±2.0 W m-2 for EC, the results simulated by a
radiative transfer model (RRTMG_SW). The maximum solar energy
absorption of WSOC due to biomass burning was 0.9 W m-2. It provides
the evidence that carbonaceous components released by biomass burning
transported over long distances absorbed solar energy, especially in the UV
band, influencing the radiation balance of the atmosphere. The PSCF analysis
of the carbonaceous compound concentration and light absorption of carbonaceous
aerosols shows that the regionally transported biomass burning emissions
profoundly impacted the chemical and optical properties of carbonaceous
aerosols in Nanjing during this episode.
Data availability
Data are available from the corresponding author on request (dryanlinzhang@outlook.com, zhangyanlin@nuist.edu.cn).
Author contributions
YZ designed the study. XL and YZ conceived the study. XL, XZ, TH, ZX, MB, WZ, MF carried out the experiments and collected data. XL wrote the paper with YZ, YP, LX and CZ. FC, MH, CY, YC and XuL have contributed to the data interpretation and review of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Regional transport and transformation of air pollution in eastern China”. It is not associated with a conference.
Financial support
This research has been supported by the National Key R&D Program of China (grant no. 2017YFC0210101), the Natural Scientific Foundation of China (grant nos. 91644103, 41977305 and 41761144056), and the Provincial Natural Science Foundation of Jiangsu (grant no. BK20180040). This study has been supported by the funding of Jiangsu Innovation & Entrepreneurship Team. The authors would also like to thank the China Scholarship Council for the support to Xiaoyan Liu.
Review statement
This paper was edited by Yuan Wang and reviewed by three anonymous referees.
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