Brown carbon (BrC) is a special type of organic aerosol (OA), capable of
absorbing solar radiation from near-ultraviolet (UV) to visible wavelengths,
which may lead to an increased aerosol radiative effect in the atmosphere.
While high concentrations of OAs have been observed in the Pearl River Delta
(PRD) region of China, the optical properties and corresponding radiative
forcing of BrC in the PRD are still not well understood. In this work, we
conducted a set of comprehensive measurements of atmospheric particulate
matter from 29 November 2014 to 2 January 2015 to investigate aerosol
compositions, optical properties, source origins, and radiative forcing
effects at a suburban station in Guangzhou. The particle absorption
Ångström exponent (AAE) was deduced and utilized to distinguish
light absorption by BrC from that by black carbon (BC). The results showed
that the average absorption contributions of BrC were 34.1±8.0 % at
370 nm, 23.7±7.3 % at 470 nm, 16.0±6.7 % at 520 nm,
13.0±5.4 % at 590 nm, and 8.7±4.3 % at 660 nm. A
sensitivity analysis of the evaluation of the absorption Ångström
exponent of BC (AAEBC) was conducted based on the Mie theory
calculation assuming that the BC-containing aerosol was mixed with the
core–shell and external configurations. The corresponding uncertainty in
AAEBC was acquired. We found that variations in the imaginary
refractive index (RI) of the BC core can significantly affect the estimation
of AAEBC. However, AAEBC was relatively less sensitive to the real
part of the RI of the BC core and was least sensitive to the real part of
the RI of the non-light-absorbing shell. BrC absorption was closely related
to aerosol potassium cation content (K+), a common tracer of biomass
burning emissions, which was most likely associated with straw burning in
the rural area of the western PRD. Diurnal variation in BrC absorption
revealed that primary organic aerosols had a larger BrC absorption capacity
than secondary organic aerosols (SOAs). Radiative transfer simulations
showed that BrC absorption may cause 2.3±1.8 W m-2 radiative
forcing at the top of the atmosphere (TOA) and contribute to 15.8±4.4 % of the aerosol warming effect. A chart was constructed to
conveniently assess the BrC radiative forcing efficiency in the studied area
with reference to certain aerosol single-scattering albedo (SSA) and BrC
absorption contributions at various wavelengths. Evidently, the BrC
radiative forcing efficiency was higher at shorter wavelengths.
Introduction
Black carbon (BC) and organic carbon (OC) are dominant carbonaceous aerosol
components that mainly originate from biomass burning on a global scale
(Bond et al., 2004) and have caused great environmental concern in
rapidly developing regions. Carbonaceous aerosols can not only exert adverse
impacts on public health, similar to other particulate matters, but also
significantly affect the terrestrial radiation balance with enormous
uncertainties. In previous studies, BC was often considered to be the only
light-absorbing species (Andreae and Gelencser, 2006), and OC was
believed to only be able to scatter light, i.e., causing a cooling effect
(Bond et al., 2011). Nevertheless, it has been reported that
a fraction of organic aerosols (OAs) may also specifically contribute to
light absorption from the near-ultraviolet (UV) to visible wavelength range,
which is referred to as brown carbon (BrC) (Kirchstetter et al.,
2004). BrC optical properties are strongly affected by its chemical
composition and physical structure, which are related to different BrC
sources. BrC can originate not only from direct emissions, including
smoldering, biomass burning, or any type of incomplete fuel combustion
process (Cheng et al., 2011; T. C. Bond et al., 1999), but also from
secondary organic aerosol formation processes, such as aqueous phase
reactions in acidic solutions (Desyaterik et al., 2013) or volatile
organic compound (VOC) oxidation (Laskin et al., 2015; Sareen et al.,
2010). In addition, BrC could have a complicated molecular composition and
intermix with other substances, such as BC, non-absorbing OAs, and other
inorganic materials, making it complicated to investigate BrC optical
properties.
BC absorption is commonly assumed to cover the full wavelength range.
However, the light absorption property of BrC is believed to be more
wavelength-dependent, which can be represented by distinct absorption
Ångström exponent (AAE) values, i.e., the power exponent of the
light absorption coefficient. A typical threshold for the AAE of BC
(AAEBC) of 1.6 has been recommended to distinguish BrC from BC
(Lack and Cappa, 2010), and the AAE of BrC has been reported as
having a wider range (2 to 7) (Hoffer et al., 2006). Based on the
difference in the wavelength dependence of light absorption between BC and
BrC, previous studies have applied the AAE method to differentiate light
absorption by BrC through multiwavelength optical measuring apparatus, such
as a three-wavelength photoacoustic soot spectrometer (PASS-3) (Lack and
Langridge, 2013) and a multiwavelength Aethalometer (Olson et al., 2015). Based on the AAE method, the BrC absorption contribution has been
estimated to be approximately 6 % to 41 % of total aerosol light absorption
at short wavelengths, e.g., at 370 and 405 nm (Washenfelder et
al., 2015). A uniform AAEBC from ∼300 nm up to
∼700 nm (Moosmüller et al., 2011) is commonly
used when evaluating the BrC absorption contribution using the AAE method.
However, it has been reported that the AAEBC can be influenced by the
mixing state, BC core size, and morphology (Lack and Cappa, 2010).
The lensing effect of the coating shell may enhance BC light absorption, the
magnitude of which may also depend on wavelength and can alter the value of
AAEBC (Liu et al., 2018). Moreover, different values of AAEBC
have been found in the near-infrared and UV ranges (Wang et al., 2018).
Therefore, using the default value of AAEBC=1 may lead to
uncertainty in BrC absorption coefficient estimation.
Quantifying BrC optical absorption accurately is essential to interpret
aerosol optical depth (AOD), and the corresponding aerosol direct radiative
forcing (DRF) on the atmosphere can also be evaluated if the
single-scattering albedo (SSA) and extinction coefficient of aerosols are
known. The estimation of the DRF of BrC has shown a distinct seasonal
variation, indicating the influence of different absorption properties of
BrC (Arola et al., 2015). A global simulation study
indicated that the average warming effect at the top of the atmosphere (TOA) caused by BrC
absorption can be up to 0.11 W m-2, corresponding to ∼25 % of that predicted from BC absorption only (Feng et al., 2013).
During the last 3 decades, rapid economic development has led to severe
air pollution problems in the Pearl River Delta (PRD) region (Chan and Yao, 2008).
With rapid increases in the automobile population and factories, high
loadings of secondary organic aerosols (SOAs) have often been observed (Tan et al.,
2016b). Biofuel usage may also play a significant role during wintertime air
pollution events in the PRD, indicating that the contribution from BrC light
absorption cannot be ignored (Wu et al., 2018). Recently, BrC light
absorption has been quantified by Qin et al. (2018) using the AAE
method in the PRD region. OA chemical composition was simultaneously
measured by a high-resolution time-of-flight aerosol mass spectrometer, and
it was found that organic aerosols originating from biomass burning
possessed the most intense absorption capability and were largely
responsible for BrC absorption. Qin et al. (2018) also suggested that
correlations between OA chemical compositions and BrC absorption were
wavelength-dependent.
In this paper, we applied the homologous AAE differentiation method to
quantify the fraction of aerosol light absorption by BrC using the
measurements from a seven-wavelength Aethalometer. The potential error
incurred with this methodology was determined using Mie theory simulations,
especially for various complex refractive indexes of the BC core and the
coating material. The correlation between BrC light absorption and
water-soluble ions, which is used as the source tracer, was employed to
identify potential BrC sources. An atmospheric radiative transfer model has
also been applied to evaluate the impact of BrC on direct radiative forcing
using surface-based aerosol optical properties and satellite-based
surface-albedo data. The magnitudes of aerosol radiative forcing at the top
of the atmosphere due to BC and BrC were also individually quantified.
MethodologySampling site
Field observations were conducted at the Panyu station (23∘00′ N, 113∘21′ E), which is a
monitoring site of the Chinese Meteorological Administration (CMA)
Atmospheric Watch Network (CAWNET) that is located on the summit of
Dazhengang Mountain (approximately 150 m above sea level) in Guangzhou,
China. Figure 1 shows the location of the Panyu site, which is situated at
the center of the PRD and is separated from residential areas by at least
500 m. Some agricultural fields can be found to the west of the site.
Although there were no significant pollution sources nearby, this suburban
site was strongly affected by pollutants transported from the urban area of
Guangzhou and crop residual fires transported from the rural area of the
PRD. The field campaign was conducted from 29 November 2014 to 2 January 2015. During the measurement period, aerosol light scattering and
extinction, BC concentration, particle number size distribution (PNSD), OC
concentration, and the water-soluble ion concentrations of PM2.5 were
continuously monitored.
All instruments were housed inside the second-floor measurement room of a
∼5 m tall, two-story building. The ambient sample was taken on
the roof by a 2 m long, 12.7 mm OD stainless steel inlet, and a PM2.5
cyclone sampler was also used. The metal tubing was thermally insulated and
maintained at a constant temperature of ∼25∘C. A
diffusion drier was also used in-line to dry the relative humidity (RH) of
the air sample to below 30 % before further analysis.
Measurements of relevant species
A TSI-3936 scanning mobility particle sizer (SMPS) and a TSI-3321
aerodynamic particle sizer (APS) were utilized to measure the 10 to 500 nm
mobility diameter and 0.5 to 2.5 µm aerodynamic diameter of the PNSD, respectively. The aerodynamic diameters of the APS data were converted into
mobility diameters using a material density of 1.7 g cm-3. A detailed
data merging method has been described by Cheng et al. (2006).
Furthermore, the pipe diffusion loss of SMPS has been corrected using the
empirical formula proposed by Kulkarni et al. (1996).
An AE-33 Aethalometer (Magee Scientific Inc.) was utilized for BC mass
concentration measurement, which was derived from optical attenuation using
a mass absorption cross section (MAC) of 7.77 m2 g-1 at 880 nm.
The sensitivity of AE-33 was approximately 0.03 µg m-3 for a 1 min
time resolution and a 5.0 liter per minute (L min-1) sample flow rate.
The PM2.5 mass concentration was measured by an environmental dust
monitor (model EDM180, GRIMM Inc.), which monitored the mass concentration
of PM2.5 and PM10 simultaneously.
Water-soluble ions (potassium (K+), calcium (Ca2+), magnesium
(Mg2+), chloride (Cl-), sulfate (SO42-), nitrate
(NO3-), and ammonium (NH4+) were measured with the
Monitor for AeRosols and Gases in Air (MARGA) (Model ADI2080, Metrohm Inc.),
which is an online analyzer for semicontinuous measurements of gases and
water-soluble ions in aerosols (Li et al., 2010). The MARGA was
automatically calibrated with standard internal solutions during field
measurement. The MARGA utilized its own PM2.5 sampling system provided
by the manufacturer.
The OC and elemental carbon (EC) were measured by a Sunset online OC/EC analyzer (Model RT-4) with a laser transmittance-based charring correction (Wu
et al., 2018, 2019). The sample flow rate of the OC/EC analyzer
was maintained at 8 L min-1. For each measurement cycle (1 h), samples were
collected onto a quartz filter within the first 45 min and then
thermal–optically analyzed during the remaining 15 min. First, OC was
completely volatized in oxygen-free helium with a temperature ramped stepwise (600 and 840 ∘C). In the second stage,
the temperature was reduced to 550 ∘C, and then EC and pyrolyzed
carbon (PC) were combusted in an oxidizing atmosphere (10 % oxygen in
helium), while the temperature was increased up to 870 ∘C step by
step. The CO2 converted from all of the carbon components was then
quantified by a nondispersive infrared absorption CO2 sensor
(Lin et al., 2009). An internal calibration peak made by 5 %
methane in helium was applied to quantify OC and EC. To correct the PC
converted from OC to EC, a tunable pulsed diode laser beam was used to
monitor the laser transmittance through the quartz filter throughout the
thermal–optical analysis (Bauer et al., 2012).
Measurements of optical properties
Light extinction by aerosols at 532 nm was detected using a cavity ring-down
aerosol extinction spectrometer (CRDS) (Model XG-1000, Hexin Inc.) by
measuring the decay times of laser intensity through the aerosol-containing
sample and the filtered background air sample under the same conditions. The
extinction coefficient (σext) was calculated using the
procedure described by Khalizov et al. (2009).
Aerosol total scattering (σsp) was measured by a TSI-3563
integrated nephelometer at three wavelengths (i.e., 450, 550, and 700 nm) and was calibrated with CO2 following the manual instructions.
Particle-free air was used to check the nephelometer background signal once
every 2 h. The scattering coefficients at other wavelengths were
extrapolated using the following equations:
SAE=-lnσscat,λ0-lnσscat,550nmlnλ0-ln(550),σscat(λ)=σscat(550)⋅(λ550)-SAE,
where λ0=450 nm is for wavelengths less than 550 nm and
λ0=700 nm is for wavelengths greater than 550 nm. The
corresponding time series of extinction coefficients, scattering
coefficients, and SSA at 532 nm are displayed in Fig. S1 in the Supplement.
The Aethalometer was also used for multi-wavelength light absorption
measurements in this study. The seven-wavelength aerosol light attenuation
coefficients (σATN) were converted into aerosol light
absorption coefficients (σabs) using Eq. ()
(Coen et al., 2010), where k is the parameter that accounts
for the loading effect, ATN is the light attenuation through the filter with
sample loading, and Cref is a fixed multiple scattering parameter.
σabs=σATN(1-k⋅ATN)⋅Cref
The real-time k value was retrieved using the dual-spot loading correction
algorithm developed by Drinovec et al. (2015). The
detailed formula of ATN can also be found in Drinovec et al. (2015). Cref is considered a constant that strongly depends on the
filter matrix effect. However, some studies have suggested that Cref may
vary with wavelength (Arnott et al., 2005; Segura et al., 2014). For
internal combustion engines and biomass burning, Cref at 370 nm was
expected to be approximately 12 % and 18 % less than Cref at 532 nm
for the aerosol component, respectively (Schmid et al., 2006). Different ambient
observations also showed that Cref may have regional specificity, even
though it was retrieved by the same methodology (Coen et
al., 2010). In this study, Cref=3.29 was used in Eq. () at each
wavelength, and this value was derived from the slope of σATN
measured by the Aethalometer vs. σabs, which was deduced from
the CRDS and nephelometer measurements. This Cref was also very similar
to the Cref of 3.48 determined from an intercomparison study between an
Aethalometer and a photoacoustic soot spectrometer during a field campaign
conducted in the PRD region in 2004 (Wu et al., 2009).
The BC light absorption at certain wavelengths was derived from the
absorption coefficient σabs according to Beer–Lambert's law,
and its variation between different pairs of wavelengths (i.e., σabs,BC,λ) is denoted by the absorption AAE equation developed by Ångström (1929):
σabs,BC,λ=σabs,BC,λ0×(λ0/λ)AAEBC.
It has been suggested that the AAE of BC may vary between short and long
wavelength ranges (Lack and Cappa, 2010); hence, applying a
wavelength-independent AAEBC may lead to uncertainties in the BC
absorption calculation from one wavelength to another. In this work, the
light absorptions of BC at various wavelengths were retrieved by a modified
wavelength-dependent AAE differentiation method conducted by Wang et al. (2018):
σabs,BC,λ1=σabs,BC,880nm×(880λ1)AAEBC,520-880nm,σabs,BC,λ2=σabs,BC,880nm×(880520)AAEBC,520-880nm×(520λ2)AAEBC,370-520nm.
Here, σabs,BC,λ1 represents the absorption coefficient
due to only BC greater than 520 nm, and σabs,BC,λ2
represents the absorption coefficient of BC less than 520 nm. AAEBC,λi-λi+1 (i=1, 2, and 3) represents the AAE of BC
between a longer and shorter wavelength at λi=880, 520, and
370 nm and was calculated as
AAEBC,λi-λi+1=-ln(σabs,BC,λi)-ln(σabs,BC,λi+1)lnλi-ln(λi+1).
Accordingly, BrC absorption at a certain wavelength λ (σabs,BrC,λ) was equal to the value of total aerosol
absorption (σabs,λ) minus BC absorption (σabs,BC,λ):
σabs,BrC,λ=σabs,λ-σabs,BC,λ.
The light absorption data at 880 nm (σabs,880nm) were selected
to represent BC absorption (σabs,BC,880nm), which should not be
affected by BrC (Drinovec et al., 2015). It has been
reported that the dust-related contributions of PM2.5 were normally
less than 5 % in wintertime in Guangzhou; therefore, the influence from
dust could be negligible in this study (Huang et al., 2014).
Estimation of AAEBC
Traditionally, AAEBC was believed to be close to 1.0 (Bodhaine,
1995), which has been commonly used for BC measurements (Olson et
al., 2015). However, studies have demonstrated that AAEBC can be
affected by the refractive index of coating materials, mixing state,
morphology, and BC core size (Liu et al., 2015). Therefore, using the
default AAEBC=1 may lead to uncertainty in BrC absorption
estimation. To obtain the correct AAEBC, a series of Mie theory
calculations were conducted using a simplified core–shell model (Bohren
and Huffman, 1983; Wang et al., 2018). We used a modified BHCOAT code and
BHMIE code to calculate the aerosol optical properties of the core–shell and
external mixture at different wavelengths (Cheng et al., 2006). In
the Mie theory, a particle is taken as a perfect homogeneous sphere, and its
extinction and scattering efficiencies, Qext,Mie,λ and
Qscat,Mie,λ, respectively, are expressed as (Mie, 1908;
Seinfeld and Pandis, 1998)
8αQext,Mie,λ=2λ2∑n=1∞[2n+1Rean+bn],9αQscat,Mie,λ=2λ2∑n=1∞[(2n+1)(an2+bn2)],
where α=πDp/λ is the size parameter; an
and bn are functions of the complex refractive index (RI) and α in the Riccati–Bessel form, respectively. Re in Eq. (8) denotes that only
the real part of RI is taken. The absorption efficiency (Qabs,Mie,λ) is thus the difference between the extinction and scattering
efficiencies:
Qabs,Mie,λ=Qext,Mie,λ-Qscat,Mie,λ.
Then, the absorption coefficient σabs,Mie,λ was obtained
by the following (Bricaud and Morel, 1986):
σabs,Mie,λ=∫Qabs,Mie,λ⋅(π4Dp2)⋅N(logDp)⋅dlogDp,
where N(logDp) is the PNSD function. A two-component parameterization
of dry particles, i.e., the BC core and the non-light-absorbing species, was
applied to calculate aerosol optical properties here (Wex et al., 2002).
m̃core represents the RI of the BC core, and m̃non
represents the RI of non-light-absorbing particles.
In a realistic atmosphere, some non-light-absorbing particles may exist
independently without BC (Liu et al., 2013; Cheung et al., 2016). In this
work, the portion of non-light-absorbing particles at a certain size
(Dp) was determined by our previous measurements at the same site using
a volatility tandem differential mobility analyzer (V-TDMA), during which
completely vaporized (CV) particles at 300 ∘C were referred to as
non-light-absorbing particles that externally mixed with other BC-containing
particles. Thus, the PNSD of CV particles (N(logDp)CV) and
BC-containing particles (N(logDp)BC) can be given by the following
equations:
12N(logDp)CV=N(logDp)measure⋅Φ(Dp)N,CV,13N(logDp)BC=N(logDp)measure⋅(1-Φ(Dp)N,CV),
where N(logDp)measure is the PNSD of the measured particles from
SMPS and APS. Φ(Dp)N,CV was the number fraction of CV
particles in different size bins.
A previous study applied three kinds of BC mixture models to calculate the
aerosol optical properties, including external, homogeneously internal, and
core–shell mixtures (Bohren and Huffman, 2007; Seinfeld and
Pandis, 1998). To quantify the mixing state of BC, rext was defined as
the mass fraction of externally mixed BC (Mext) in total BC (MBC):
rext=MextMBC.
Tan et al. (2016a) suggested that two extreme conditions of external and
core–shell mixtures comprised the actual mixing state of BC in the PRD.
Hence, we simply divided the PNSD of BC into the PNSD from an external
mixture of BC and a core–shell mixture of BC. The PNSDs of externally mixed
BC particles and core–shell mixed BC particles were referred to by the
following equations with a given rext.
15N(logDp)ext=N(logDp)BC⋅fBC⋅rext16N(logDp)core–shell=N(logDp)BC⋅(1-fBC⋅rext)fBC was defined as the BC volume fraction in the BC-containing particle
volume, which can be converted from the BC mass concentration:
fBC=MBCρBC⋅∑DPN(logDp)BC⋅(π6⋅Dp3),
where ρBC is the density of BC and is assumed to be 1.5 g cm-3 (Ma et al., 2012);
MBC is the BC mass concentration derived from the multi-angle absorption
photometer (MAAP), which was obtained by an empirical formula from the
Aethalometer that measured the BC concentration (MBC,AE), as proposed by
Wu et al. (2009):
MBC=0.897⋅MBC,AE-0.062.
The PNSDs of externally mixed non-light-absorbing particles and externally
mixed BC particles were input into the BHMIE code, and the PNSD of the
core–shell mixed particles was imported into the BHCOAT code. Another
critical parameter for the core–shell model was the diameter of the BC core.
For the simplified core–shell model we applied, the visualization was that a
homogeneous BC core sphere was encapsulated in a shell of non-absorbing
coating (Bohren and Huffman, 2007). Without size-resolved coating
thickness measurements, core–shell mixed particles simply assumed that cores
with the same diameter had the same coating thickness. Therefore, the
diameter of the BC core was calculated as follows:
Dcore=Dp⋅(fBC-fBC⋅rext1-fBC⋅rext)13.Dcore and Dp are inputted as parameters into an and bn,
respectively, which was described by Bohren and Huffman (2007).
The corresponding time series of size distribution of the derived external
BC and internal BC core are illustrated in Fig. S2. Thus, the σabs,BC,Mie,λi values of all six wavelengths were calculated
through the Mie model, and then the AAEBC values of those five
wavelengths were obtained using Eq. (). The performance of this empirically
determined calculation method has been compared with other possible BC
mixing schemes in detail (see Table 1).
Intercomparison of the performance of various Mie-calculation
schemes. Base Case is based on the empirical distribution function and
mixing states of BC particles obtained from previous field measurements at
the same site. ΦN,CV denotes the portion of non-BC particles, and
rext is the mass portion of externally mixed BC with respect to total
BC. AAEBC is the absorption Ångström exponent of BC, and the
subscript represents the wavelength range. AbsBrC,370 % and
AbsBrC,520 % are the BrC absorption contributions at 370 and 520 nm, respectively. Calcabs880 is the calculated absorption at 880 nm
using the Mie model. Measabs880 is the measured absorption by an
Aethalometer at 880 nm. b is the intercept of the regression analysis
between Measabs880 and Calcabs880, i.e., Calcabs880=b*
Measabs880. R2 is the correlation coefficient of the equation. The
refractive index of BC core (m̃core) and non-light-absorbing
particles (m̃non) is set to be 1.80–0.54i and 1.55–10-7i,
respectively (Tan et al., 2016a).
Case no.SchemeΦN,CVrextAAE BC,370-520AAE BC,520-880AbsBrC,370 %AbsBrC,520 %Calc abs880Meas abs880bR2Base0.384 to0.580.7230.96234.13 %15.96 %21.8691.0190.9790.1371010.3310.62651.64 %29.57 %15.8320.7470.9682000.8561.12824.76 %8.28 %27.8271.2950.976300.580.7450.97433.22 %15.46 %21.93621.1991.0290.97940.384 to00.8351.11126.01 %9.14 %27.3021.2690.9750.13750.500.7781.04329.96 %12.30 %24.9211.1500.96860.50.580.6740.92836.39 %17.49 %20.8970.9770.975Atmospheric radiative transfer model
In this work, the Santa Barbara DISORT Atmospheric Radiative Transfer
(SBDART) model was employed to estimate the DRF of BrC absorption, i.e., its
effects on the downward and upward fluxes (F in W m-2) of solar
radiation at the TOA. SBDART is a software tool that can be used to compute
plane-parallel radiative transfer under both clear and cloudy conditions
within the atmosphere. More details about this model are found in Ricchiazzi et al. (1998). Both ground measurements and remote-sensing data were used in the simulation. The surface albedo was derived from a 500 m resolution Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) albedo model parameter product (MCD43A3, daily). The MCD43A3 products are the total shortwave broadband black-sky
albedo (αBSA) and white-sky albedo (αWSA), while
the actual surface albedo (α) was computed from a linear combination
of αWSA and αBSA, which were weighted by the
diffuse ratio (rd) and direct ratio (1-rd), respectively:
α=1-rd⋅αBSA+rd⋅αWSA.rd was obtained from an exponential fit of Eq. () based on empirical
observations (Roesch, 2004; Stokes and Schwartz, 1994):
rd=0.122+0.85e-4.8μ0,
where μ0 is the cosine of the zenith angle, which is calculated by
the model for any specified date, time, and latitude and longitude of the
site. The surface-based aerosol optical properties, including the aerosol
light absorption coefficients of both BC and BrC, i.e., differentiated from
each other under the assumption of uniform AAEBC, along with the
nephelometer-measured aerosol scattering coefficients, were used to
calculate the SSA at different wavelengths according to Eq. ():
SSA(λ)=σscat,λσabs,BrC,λ+σabs,BC,λ+σscat,λ.
This was then used in the model calculation. Finally, the AOD and asymmetry
factor (ASY) at 440, 675, and 870 nm were derived from the Aerosol Robotic
Network (AERONET) measurements at the Hong Kong Polytechnic University site
(Holben et al., 1998), which is approximately 115 km to
the southeast of the Panyu site. The tropical atmospheric profile was used
in the SBDART model based on the prevailing weather conditions in the PRD.
The aerosol DRF (ΔF) was calculated as the difference between the
downward and upward radiation fluxes:
ΔF=F↓-F↑.
Results and discussionAerosol light absorption
The AAEBC is widely defined as the uniform representation of the
wavelength dependence of a BC particle (Olson et al., 2015). In
reality, AAEBC may vary significantly with BC-containing aerosols of
different sizes, mixing states, and morphologies (Lack and
Langridge, 2013; Scarnato et al., 2013). In fact, some studies showed that
the AAE of a large size, pure BC core may be less than 1.0 (Liu et al.,
2018) and that the AAE of BC coated with a non-absorbing shell may be larger
than that under uniformity (Lack and Cappa, 2010).
It has been suggested that a significant fraction of smaller size particles
is non-BC-containing (Cheung et al., 2016; Ma et al., 2017). BC and
non-BC materials can also be externally or internally mixed. Although size-resolved BC measurements were not available during this work, we conducted size-resolved V-TDMA measurements at 300 ∘C for 40, 80, 110, 150, 200, and 300 nm during an earlier field campaign (February 2014) at the same
site as in this work. At 300 ∘C, all non-BC particles will be
completely vaporized (CV), and thus the portion of non-BC particles at such
size, denoted as ΦN,CV, can be determined. The average ΦN,CV values were 0.384, 0.181, 0.180, 0.158, 0.143, and 0.137,
corresponding to 40, 80, 110, 150, 200, and 300 nm (see Fig. S3),
respectively (Cheung et al., 2016; Tan et al., 2016a). The ΦN,CV values for other sizes were interpolated linearly from these six
diameters. For particle sizes larger than 300 nm and less than 40 nm, ΦN,CV values were set to 0.137 and 0.384, respectively. Accordingly,
the complete distribution of ΦN,CV for the whole PNSD was
obtained. The mixing states of BC particles were also estimated here, i.e.,
the mass portion of externally mixed BC with respect to total BC, denoted as
rext. The value of rext was taken as 0.58, which was obtained
using an optical closure method during a previous field experiment at this
site (Tan et al., 2016a). During the following Mie theory
calculation, a fixed refractive index (m̃core=1.80–0.54i,
m̃non=1.55–10-7i) was adopted for the whole size range.
Accordingly, the calculated BC absorption at 880 nm (Abs880) was 21.869 Mm-1, which is reasonably close to the measured mean value of 21.199 Mm-1. To further validate our calculation scheme (Base Case), we have
considered several extreme cases.
Case 1: BC is completely externally mixed
with non-BC particles, i.e., ΦN,CV=0 and rext=1;
Case 2: BC is present in every size bin and BC is completely internally
mixed with non-BC material, i.e., ΦN,CV=0 and rext=0;
Case 3: BC is both internally and externally mixed, but there are no non-BC-containing particles, i.e., ΦN,CV=0 and rext=0.58;
Case 4: BC is internally mixed with non-BC material and there are
non-BC particles present, i.e., ΦN,CV ranges from 0.384 to 0.137
and rext=0;
Case 5: the same as Case 4 except assuming a fixed
non-BC to BC ratio of 0.5, i.e., ΦN,CV=0.5, rext=0;
Case 6: the same as Case 5 except that some externally mixed BC is also
present, i.e., ΦN,CV=0.5, rext=0.58.
The calculation
results are listed in Table 1. Evidently, Case 1 (completely externally mixed)
will significantly underestimate the measured Abs880, indicating that
most BC particles were not likely externally mixed at the Panyu site.
The complete internal mixing state (Cases 2, 4, and 5), by contrast, would
substantially overestimate the BC absorption regardless of the form of the BC core
distribution function. However, when the rext was considered (Base Case, Case 3, and Case 6), the calculated Abs880 values were all very close to
the measured value, especially the Base Case.
When the AAEBC was assumed to be uniform, the campaign-averaged
σBrC values were 17.6±13.7 Mm-1 at 370 nm,
9.7±7.9 Mm-1 at 470 nm, 5.8±5.1 Mm-1 at 520 nm, 4.0±3.5 Mm-1 at 590 nm, and 2.3±2.1 Mm-1 at 660 nm. At the corresponding wavelengths, BrC absorption
contributed 26.2 % ± 8.5 %, 20.0 % ± 7.3 %, 14.3 % ± 6.5 %,
11.7 % ± 5.3 %, and 7.8 % ± 4.1 % to the total aerosol absorptions. When the AAEBC was applied as the result of the Mie model
calculation, the corrected campaign-averaged σabs,BrC values
were 23.5±17.7 Mm-1 at 370 nm, 11.8±9.5 Mm-1 at 470 nm, 6.7±5.7 Mm-1 at 520 nm,
4.6±3.9 Mm-1 at 590 nm, and 2.6±2.3 Mm-1 at
660 nm. At the corresponding wavelengths, BrC absorption contributed
34.1 % ± 8.0 %, 23.7 % ± 7.3 %, 16.0 % ± 6.7 %, 13.0 % ± 5.4 %, and 8.7 % ± 4.3 % to the total aerosol absorption (see Fig. 2). Evidently, aerosol light absorption was predominantly due
to BC; however, BrC also played a significant role, especially at shorter
wavelengths. Table 2 shows the intercomparison of BrC light absorption in
the near UV range between this work and other studies in the East Asian
region. Clearly, the reported values vary substantially, and our result is
toward the lower end of values. Figure S4 displays the time series of
particle AAE measured by the Aethalometer, and AAEBC was derived from
Mie model calculation. The AAEBC was almost always lower than AAE,
indicating appreciable BrC light absorption at the Panyu site.
(a) BC and BrC particle average light absorption coefficients at
different wavelengths under different AAEBC assumptions; the whiskers
represent an error of 1 standard deviation. (b) Contributions of BC and
BrC to the total light absorption coefficient at different wavelengths under
different AAEBC assumptions; the whiskers represent an error of 1 standard deviation.
Observational studies of the BrC light absorption coefficient and
contribution in the near-ultraviolet wavelength range in East Asia.
Mean BrCMean BrCλabsorptionabsorptionPeriodsLocation(nm)coefficientcontributionInstrumentationReferenceNov 2014–Jan 2015Guangzhou (China)37017.6 Mm-126.2 % (AAEBC=1)AethalometerThis study23.5 Mm-134.1 % (corrected)AE-33Jan 2014–Feb 2014;Shenzhen (China)4053.0 Mm-111.7 % (winter)PASS-3Yuan et al. (2016)Sep 2014–Oct 20141.4 Mm-16.3 % (fall)Nov 2014Heshan (China)4053.9 Mm-112.1 %PASS-3Yuan et al. (2016)Nov 2016–Dec 2016Beijing (China)370106.4 Mm-146 % (at the ground)AethalometerXie et al. (2018)93.8 Mm-148 % (at 260 m)AE-33Jun 2013–May 2016Nanjing (China)37035.8 Mm-116.7 %Aethalometer AE-31Wang et al. (2018)Jan 2012Nagoya (Japan)405Not detected11 % (300 ∘C)ThermodenuderNakayama et17 % (400 ∘C)PASS-3al. (2015)Uncertainty in BC and BrC optical differentiation
Theoretically, the magnitude of BC absorptions can be affected by both parts
of the complex RIs; thus, AAEBC may also vary with
the RIs of both the BC core and coating shell. In fact, RI was also one of
the least known properties of BC and other coating materials with negligible
absorbing capabilities. The refractive index of the BC core
(m̃core) displays a wide range of variations (Liu et al.,
2018). Typically, the real and imaginary parts of the RI can vary from 1.5
to 2.0 and 0.5 to 1.1, respectively. In addition, the shell was assumed to
consist of non-absorbing material in the core–shell model; i.e., its
imaginary RI was set to be close to zero (10-7). The real part of the
non-absorbing material RI (m̃non) may vary from 1.35 to 1.6 due
to the presence of OA (Redmond and Thompson, 2011; Zhang et al., 2018)
and inorganic salts (Erlick et al., 2011). Hence, it is necessary to
investigate the uncertainties associated with the variations in AAEBC
by varying the RIs of both the BC core and the non-absorbing materials.
Figure 3 shows the impacts of RI on the evaluations of AAEBC based on
core–shell and external configuration, where the RI of the BC core was set
to be constant, i.e., m̃core=1.80–0.54i, and the real part of
m̃non varied from 1.35 to 1.6 at an interval of 0.05, with the
imaginary part of m̃non set at 10-7. As shown in Fig. 3a,
the calculated AAEBC for the core–shell model was higher than 1.0 at longer wavelengths (520 to 880 nm) and lower than 1.0 at shorter wavelengths
(370 to 520 nm) (the red line in Fig. 3 denotes AAEBC=1). The
averaged AAEBC,370-520nm ranged from 0.84 to 0.87, and the
AAEBC,520-880nm ranged from 1.07 to 1.15, indicating that the
AAEBC,520-880nm appeared to be more sensitive to the shell's real part
than AAEBC,370-520nm. Even if the shell material was assumed to be
non-absorbing, the variation in the real RI of the shell, which was referred
to as the real part of m̃non, still led to changes in the
shell's refractivity and correspondingly altered its lensing effect, causing
a change in AAEBC. Meanwhile, AAEBC,370-520nm and
AAEBC,520-880nm generally increased with an increasing real part of the
shell. In Fig. 3b, under the externally mixed conditions,
AAEBC,370-520nm and AAEBC,520-880nm were both less than 1.0. The
average AAEBC,370-520nm was 0.33, and the average AAEBC,520-880nm
was 0.63. These values were far less than the values under core–shell
mixture conditions. In the external mixture model, the BC core and
non-light-absorbing materials were assumed to exist dependently, and then the
optical properties of these two components were considered separately.
Therefore, altering the real part of the externally mixed non-absorbing
material would not affect the light absorption property of the BC core or AAEBC.
Influence of the wavelength-independent refractive index of the
non-absorbing materials on the (a) AAEs of the core–shell mixture and (b) AAEs of the external mixture with a constant BC core refractive index
(m̃core=1.80–0.54i). The imaginary part of the
non(less)-absorbing shell was set to 10-7, while the real part varied
from 1.35 to 1.60. In each panel, the boundaries of the box represent the
75th and 25th percentiles; the whiskers above and below each box indicate an
error of 1 standard deviation; the black lines in the boxes denote the
average values. In panels (a) and (b), the red line indicates where
AAEBC=1.
The impacts of the BC core on AAEBC are shown in Fig. 4, where the
refractive index of non-light-absorbing materials was assumed to be
m̃non=1.5510-7i and
m̃non was wavelength-independent. Figure 4 was obtained with a
core–shell mixture model (Fig. 4a and b) and an external mixture model
(Fig. 4c and d) by varying the real part of m̃core from 1.5 to
2.0 with a step of 0.05 and varying the imaginary part of the
m̃core from 0.4 to 1.0 with a step of 0.05. As
shown in Fig. 4a and b, for the core–shell mixture, the averaged
AAEBC,370-520nm ranged from 0.55 to 0.99 and the averaged
AAEBC,520-880nm ranged from 0.84 to 1.27. The AAEBC at a certain
wavelength generally increased when increasing the real part of
m̃core but decreased when increasing the imaginary part of
m̃core. The AAEBC appeared to be more sensitive to the
imaginary part of m̃core than the real part of
m̃core because the imaginary part of m̃core was
directly related to the light-absorbing properties of particles. In Fig. 4c
and d, for the external mixture, the averaged AAEBC,370-520nm ranged
from 0.04 to 0.45 and the averaged AAEBC,520-880nm ranged from 0.28 to
0.79, while the averaged AAEBC,370-520nm and AAEBC,520-880nm were
both less than 1.0. Similar to the core–shell mixture, the
AAEBC,520-880nm increased when increasing the real part of
m̃core but decreased when increasing the imaginary part of
m̃core. However, the variation patterns of AAEBC,370-520nm
were different from those of AAEBC,520-880nm. The AAEBC,370-520nm
values were not changed by altering the real part of m̃core
within the low imaginary part of m̃core, whereas the
AAEBC,370-520nm values still increased when increasing the real part of
m̃core within the high imaginary part of m̃core. A
possible explanation was that the externally mixed BC core had weak light
absorption within the low imaginary part of m̃core, causing the
AAEBC,370-520nm values to be insensitive to the real part of
m̃core. The AAEBC,520-880nm values were higher than the
AAEBC,370-520nm values regardless of whether they were for the
core–shell mixture or the external mixture. In addition, the AAEBC values
conducted by the core–shell mixture were higher than those conducted by the
external mixture.
Figure 4 demonstrated that the variation in the imaginary RI of the BC core
had the most significant impact on the estimated AAEBC, indicating that
the chemical component of BC emitted from different sources led to a large
uncertainty in AAEBC estimation. At the same time, the influence
arising from varying the real RI of the BC core was relatively moderate.
Nevertheless, Fig. 3 shows that change in the real RI of the non-absorbing
materials caused the least or no impact compared to the
variations in the complex RI of the BC core.
It should be pointed out that most BC-containing particles are often
observed as being fractal rather than spherical in shape (Katrinak et al.,
1993). Because the Mie model assumes that all particles are spherical, it
may lead to potential uncertainty for the estimation of AAEBC and BrC
absorption contributions. Moreover, the externally mixed soot aggregates
were “chain-like” or “puff-like” in the PRD dry season (Feng et
al., 2010), in which the fractal dimension (Df) was between 1.5 and
2.0. Coating soot aggregates were likely spheres (Df approaches 3) from
the high-resolution transmission electron microscopy (TEM) measurements
taken in Hong Kong (Zhou et al., 2014). A soot aggregate
sensitivity study with the superposition T-matrix method indicated that
using the assumption of volume-equivalent spheres for the soot aggregates
may result in an overestimation of approximately up to 15 % and an
underestimation of approximately up to 50 % in the predicted 870 nm light
absorption when the Df is between 1.5 and 3.0 (Liu et
al., 2008). However, it should be recognized that the complex shapes or
positions of the BC core inside the particle make it impractical to be
numerically simulated in the exact details. By far the Mie model with a
core–shell configuration would be the most practical and effective
simulation scheme for BC particle optical property simulation.
Furthermore, we have performed Monte Carlo simulations to evaluate the
uncertainties of the Mie calculation performed during this work. In the
simulation, a sequence of random numbers or errors were applied to the input
parameters, and then the corresponding uncertainties of particle light
absorption and AAEBC were computed using the Mie model. Five hundred reiterations were conducted during the simulation such that the random errors
will be normally distributed. The standard deviations (σ) of all
input parameters are listed in Table S1. In order to cover the effect of
extreme value, we used a range of ±3σ, or a confidence level
of 99 %, in the Monte Carlo simulation. Table S2 lists the Monte Carlo
simulation results, i.e., the average relative standard deviations (σMie) of the calculated BC light absorption at 880 nm (Abs880),
AAEBC,370-520, and AAEBC,520-880. The uncertainties of the
calculated Abs880, AAEBC,370-520, and AAEBC,520-880 at two
times of σMie, i.e., at a confidence level of 95 %, were
approximately ±31 %, ±16 %, and ±13 %,
respectively. Figure S5a shows the time series of the uncertainties of
Abs880, AAEBC,370-520, and AAEBC,520-880 from a Monte Carlo
simulation for the campaign period. These uncertainties will certainly be
propagated into the calculated BrC absorption contributions, too. Hence, we
also estimated the corresponding uncertainties in the BrC absorption
contribution results, as shown in Fig. S5b. Accordingly, the averaged lower
limits of BrC absorption contributions were 26.8 % ± 9.1 % at 370
nm, 17.5 % ± 8.1 % at 470 nm, 10.1 % ± 7.3 % at 520 nm,
8.5 % ± 5.8 % at 590 nm, and 5.3 % ± 4.5 % at 660 nm, and the averaged upper limits of BrC absorption contribution
ratios were 40.7 % ± 7.2 % at 370 nm, 29.5 % ± 6.7 % at
470 nm, 21.1 % ± 6.2 % at 520 nm, 17.3 % ± 5.2 % at 590 nm,
and 12.0 % ± 4.1 % at 660 nm.
Influence of the wavelength-independent refractive index of the BC
core on AAEs with a constant shell refractive index
(m̃shell=1.55-10-7i). A core–shell mixture was used for
panels a and b, and an external mixture was used for panels c and d. The
real part of m̃core varied from 1.5 to 2.0, with a step of 0.05,
and the imaginary part of m̃corevaried from 0.4 to 1.0, with a
step of 0.05.
Characteristics of BrC light absorption, water-soluble ions, and OC concentrations
Globally, BrC has been observed to be highly correlated with biomass and
biofuel burning emissions (Laskin et al., 2015). Since
large quantities of sylvite are present in biomass burning particles, the
K+ abundance has often been used as a biomass burning tracer
(Levine, 1991). Figure 5 presents the time series of the OC mass
concentration, K+ concentration, and BrC absorption from 29 November 2014 to 2 January 2015 at the Panyu site. The range of the OC concentrations
obtained from the OC/EC online analyzer was from 1.5 to 65.2 µg cm-3, and the campaign average was 12.5±7.3µg cm-3.
The BrC absorption hourly mean data were between 0.2 and 123.2 Mm-1, and the campaign average was 23.5±17.7 Mm-1. On the other hand,
the average K+ concentration was 1.0±0.7µg cm-3
(ranging from 0 to 5.4 µg cm-3). Clearly, similar trends among OC,
K+, and BrC absorption can be seen during this field campaign (Fig. 5).
Time series of the OC aerosol mass concentration (green line),
water-soluble K+ mass concentration (blue line), and BrC light
absorption (red line).
To investigate the origins of these observed OC, K+, and BrC, wind rose plots (as shown in Fig. 6) were generated for OC, K+, and BrC
absorption, respectively. All three panels of Fig. 6 consistently show that
the three substances were associated with the same wind pattern. For the
entire campaign period, the highest values of OC, K+, and σabs,BrC,370nm were mostly associated with southwesterly winds with a relatively low wind speed (∼2 m s-1). The relatively
higher OC and K+ concentrations were highly related to the seasonal
straw burning in the countryside of the PRD located to the west of the Panyu
station. In contrast, OC and K+ concentrations during periods with
easterly winds were substantially lower than those during periods with
westerly winds. The wind rose plot of σabs,BrC,370nm is shown in Fig. 6c. Similar to OC and K+, σabs,BrC,370nm showed
higher values under weak (<2 m s-1) westerly winds and lower
values from the north and south, indicating that BrC absorption was likely
attributed to local sources and was accumulated under calm wind conditions.
Figure S6 shows the 3 d backward trajectory and the fire counts for 5 to
7 (Fig. S6a), 12 to 14 (Fig. S6b), and 24 to 26 (Fig. S6c) in November 2014,
representing low-loading, moderate-loading, and high-loading period. Clearly,
the high-loading period concurred with stagnant air movement and higher fire
counts, indicating the contribution from open fire burning sources. However,
there was a detectable difference among the three rose plots of Fig. 6 in
the maximum concentration direction. A possible explanation was that
although biomass burning emissions were believed to be the dominant and
primary source of OC, K+, and BrC, their emission ratios were highly
variable and may change with the type of biofuel and burning condition and
may even vary during different stages of burning (Burling et al., 2012).
Although biomass burning emissions contain substantial light-absorbing BrC,
further atmospheric aging processes may significantly reduce its
light-absorbing capability (Satish et al., 2017).
Moreover, secondary formation may also lead to BrC formation inside these
primary aerosols, such as humic-like substances formed through aqueous-phase
reactions, which have been suggested to be an important component of BrC
(Andreae and Gelencser, 2006).
Wind rose plots of OC (a), K+(b), and σabs,BrC,370nm(c). In each panel, the black solid lines denote the
frequency of the wind direction. The shaded contour represents the average
values of the corresponding species for that wind speed (radial length) and
wind direction (transverse direction) in polar coordinates.
To further explore the possible sources of BrC optical absorption, the
diurnal variations in OC, K+, σabs,BrC,370nm, and
σabs,BrC,370nm/OC values are plotted in Fig. 7. The diurnal
variation in OC at the Panyu site appeared to be dominated by the
development of the planetary boundary layer (PBL) height; i.e., primary
emissions accumulated at night and were swiftly diluted by vertical mixing
in the morning. The slight increase in OC in the afternoon indicated that
photochemistry may have still weakly contributed to SOA formation. Figure 7b
shows the diurnal variation in K+. Unlike OC, K+ shows a small
peak at approximately 06:00, which was consistent with breakfast time and was
very likely due to cooking activities using biofuel. No lunch and dinnertime K+ peaks were observed. The most likely explanation is that the
boundary layer height is much higher during lunch and dinnertime than in
the early morning, providing a much better atmospheric diffusion condition
for air pollutants. It is still a common practice to collect straw as
biofuel in local rural areas, which can be visually spotted but is not
heavily utilized in the region. However, the diurnal profile of σabs,BrC,370nm (see Fig. 7c) shows the combined features of OC and
K+ since both primary and secondary processes affect its intensity. The
nighttime increasing trend was most likely attributed to straw burning
activities in early winter in nearby rural areas that continued to
accumulate within the shallow PBL (Jiang et al., 2013). σabs,BrC,370nm/OC, i.e., the mass absorption coefficient of BrC
(MACBrC) (Fig. 7d), showed a relatively flat pattern, with a pronounced
dip in the afternoon and higher values at nighttime, which was likely due to
enhanced primary emissions and stable stratification at nighttime. Declining
trends during the late morning and afternoon hours indicated that the aging
process and photochemical production may reduce the light-absorbing capacity
of BrC (Qin et al., 2018).
Box–whisker plots of diurnal trends in the OC concentration (a),
water-soluble K+ concentration (b), σabs,BrC,370nm(c), and
σabs,BrC,370nm/OC (d). The red traces represent the variation
in the average value. The upper and lower boundaries of the box represent
the 75th and 25th percentiles, respectively; the whiskers above and below
each box represent an error of 1 standard deviation.
Furthermore, Fig. 8 shows the linear regression analysis results used to
evaluate the correlations of σabs,BrC,370nm with the OC,
K+, Ca2+, Mg2+, Cl-, SO42-, NO3-,
and NH4+ concentrations. The best correlations can be found
between σabs,BrC,370nm and K+ (R2=0.6148), followed
by those between σabs,BrC,370nm and OC (R2=0.4514),
NO3- (R2=0.4224), and NH4+ (R2=0.4656).
Source apportionment analysis of OA and BrC absorption in Beijing and
Guangzhou illustrated that biomass burning organic aerosols (BBOAs)
correlated well with BrC light absorption (Qin et al., 2018; Xie et al.,
2019). Thus, the significant correlation between BrC absorption and K+
reaffirmed that biomass burning was the crucial emission source of BrC
observed in this work. Although the geographic location of the observation
site was situated in a coastal area and K+ could also be found in sea
salt (Pio et al., 2008), it should be noted that the
prevailing wind direction during winter was from the north (see Fig. S6),
which drives maritime air parcels away from the site. Hence, the effect of
sea salt and crustal materials to K+ was slight, which was demonstrated
in the Supplement as shown in Fig. S7. Other earlier studies
also suggested that the sea salt contribution to the K+ concentrations
of PM2.5 was trivial in the PRD region during the winter
(Lai et al., 2007). Another possible K+ source was coal
combustion. The coal consumption in the PRD region was dominated by
coal-fired power plants. The emission from power plants was usually very
steady and was less likely to affect the diurnal correlation between K+
and BrC absorption. As shown in Fig. S8, the ratios of K+/PM2.5
vary approximately from 0.015 and 0.020 and the diurnal profile of
K+/PM2.5 shows very little variation. Yu et al. (2018)
have suggested that K+ usually accounted for 2.34 %–5.49 % of
PM2.5 in the laboratory biomass burning study. However, K+ was
normally lower than 1 % of coal combustion PM2.5. Therefore, the
ratio range of K+ to PM2.5 observed in this work likely indicated
aged biomass burning particles. Both nitrogen oxides (NOx) and ammonia
(NH3) can be found in biomass burning plumes (Andreae and
Merlet, 2001). For NO3- and NH4+, nitrate can be
converted from NOx through atmospheric reactions, and ammonium may
originate from NH3. However, similar to the diurnal variation in
σabs,BrC,370nm, diurnal variations in NH4+ and
NO3- also increased in the afternoon and appeared at nighttime in
Fig. S8. However, NO3-/PM2.5 and NH4+/PM2.5
reached their peaks at noon, indicating that ammonium nitrate formed from
the secondary reaction at this time. Along with the reduced boundary layer
height and ambient temperature, NO3- was accumulated until the
photochemical reaction stopped at night. The diurnal variation in
NH4+ was similar to that in NO3- due to the acid–base
neutralization reaction. The overlapping of the σabs,BrC,370nm,
NH4+, and NO3- diurnal variations would lead to a
significant correlation between BrC absorption and NO3- or
NH4+. High concentrations of Ca2+ and Mg2+ are often
found in dust-related aerosols (Lee et al., 1999). σabs,BrC,370nm showed poor correlations with both Ca2+ and
Mg2+, indicating that dust-related aerosol components contribute
insignificantly to the total aerosol mass loading and, thus, dust may not
affect the AAE differentiation method used in this work. Although sulfur
dioxide (SO2) may also be emitted by biomass burning, SO42-
is often believed to be secondary in nature, and the presence of other
intense SO2 sources (e.g., automobile and industrial emissions) further
reduces the correlation between BrC and SO42-. Sources of Cl-
include both combustion and sea salt spray (Waldman et al., 1991). Although the prevailing
wintertime wind direction was from the north, sea salt can still be carried
to the site by a weak sea breeze, and thus Cl- may not show
considerable correlation with BrC.
Correlations of the BrC absorption coefficient at 370 nm with OC,
water-soluble K+, Ca2+, Mg2+, Cl-, SO42-,
NO3-, and NH4+ aerosol concentrations.
BrC radiative forcing efficiency
The radiative effects of aerosol scattering, BrC absorption, and BC
absorption were investigated by the SBDART model. For each investigated
variable under cloud-free conditions, we run the model twice to calculate
the DRF at the TOA with and without the investigated variable. Accordingly,
the difference of ΔF between the two simulations was considered as
the radiative effect of the investigated variable. The results showed that
the average radiative forcings at the TOA by scattering, BrC absorption, and
BC absorption were -21.4±5.5 W m-2, 2.3±1.8 W m-2,
and 10.9±5.1 W m-2, respectively. Furthermore, BrC absorption
was attributed to 15.8 % ± 4.4 % of the warming effect caused by
aerosol light absorption, demonstrating the nonnegligible role of BrC in
radiative forcing evaluation.
We also calculated the BrC radiative forcing efficiency (RFE) under various
SSAs (ranging from 0.70 to 0.99) at three wavelengths, i.e., 440, 675, and
870 nm. The RFE was denoted as the radiative forcing normalized by the AOD.
The average AOD and ASY at the three wavelengths were 0.365 and 0.691 at 440 nm, 0.212 and 0.632 at 675 nm, and 0.154 and 0.619 at 870 nm.
A solar zenith angle of 55∘ and an average shortwave broadband
surface albedo (0.119) were used in the calculation. The results were
plotted as a set of RFE lookup charts as a function of the surface BrC
absorption contribution (see Fig. 9).
BrC radiative forcing efficiencies, which are defined as the BrC
TOA direct radiative forcing divided by the AOD, as a function of the BrC-to-total-aerosol absorption ratio and SSA measured at the surface. The average AOD of the
three wavelengths, the average ASY of the three wavelengths, a solar zenith
angle of 55∘, and average shortwave broadband surface albedo were
used in the calculation. The red star corresponds to the average SSA and BrC
absorption contributions determined from this campaign.
In general, for any wavelength, the RFE increased with increasing BrC
absorption contribution for a certain SSA, indicating that BrC was a more
efficient radiative forcing agent due to the preferential absorbance of BrC
in a shorter wavelength range. However, for a certain BrC absorption
contribution, the RFE increased with decreasing SSA; i.e., a higher portion of
light-absorbing aerosol components can lead to more efficient radiative
forcing. The trend among panels (a), (b), and (c) in Fig. 9 demonstrated
that the effect of BrC absorption contribution on RFE was
wavelength-dependent; i.e., BrC was a weaker radiative forcing agent at
longer wavelengths, which is also consistent with the wavelength-dependent
light-absorbing property of BrC. The red stars in Fig. 9 denote the average
SSA and BrC absorption contribution conditions during this campaign, i.e.,
0.029 W m-2 per unit AOD at 440 nm (Fig. 9a), 0.007 W m-2 per unit
AOD at 675 nm (Fig. 9b), and 0.0002 W m-2 per unit AOD at 870 nm (Fig. 9c). These results suggested that the average value of RFE decreased
distinctly from 440 to 870 nm not only because of the lower BrC
absorption contribution but also because of the wavelength dependence of the
BrC RFE. It should also be noted that the simulations were based on SSA
measured under dry conditions. Under the typical ambient conditions of the
PRD, the SSA might be markedly enhanced by aerosol water uptake
(Jung et al., 2009), and then the BrC radiative forcing
efficiency might be less. Moreover, Fig. 9 also serves as a lookup table to
conveniently assess the BrC radiative forcing efficiency at different
wavelengths with different BrC absorption contributions for a certain SSA.
Conclusions
In this work, light absorption due to BrC in the PRD region of China was
quantitatively deduced during the winter season of 2014. The AAEs of ambient
particles and BC core were derived from the measurements. For ambient
particles, AAE370-520nm and AAE520-880nm ranged from 0.81 to 2.31
and 0.91 to 2.13, respectively. In the case of BC, AAEBC,370-520nm and
AAEBC,520-880nm ranged from 0.59 to 0.98 and 0.82 to 1.15,
respectively. Using the absorption coefficients of BC calculated according
to the Mie theory and the observed total aerosol absorption coefficients, we
estimated the AAEBC and hence the BrC absorption contribution for the
optically equivalent mixture configuration. The average BrC light absorption
contribution ranged from 8.7 % ± 4.3 % at 660 nm up to 34.1 % ± 8.0 % at 370 nm when AAEBC was set as uniform. The sensitivity of
AAEBC estimation associated with different RI and mixing state
assumptions was further investigated. The results showed that variations in
the real RI of the non-absorbing material (1.35 to 1.6) may increase
AAEBC,370-520nm from 0.84 to 0.87 and AAEBC,520-880nm from 1.07 to
1.15 for core–shell mixtures, with an AAEBC,370-520nm of 0.33 and
AAEBC,520-880nm of 0.63 for external mixtures. Variations in the core's
real RI (1.5 to 2.0) and imaginary RI (1.0 to 0.4) may increase
AAEBC,370-520nm from 0.55 to 0.99 and AAEBC,520-880nm from 0.84 to
1.27 for the core–shell mixture and increase AAEBC,370-520nm from 0.04
to 0.45 and AAEBC,520-880nm from 0.28 to 0.79 for the external mixture.
These results indicate that the optical properties of the BC core and
non-absorbing material can significantly affect the accuracy of AAEBC
and corresponding BrC absorption contribution estimations. Compared to the
values of BrC light absorption coefficient and BrC light absorption
contribution from other similar studies conducted in the East Asia region,
the BrC measured in this work showed relatively lower values of light
absorption coefficient but was found to be responsible for relatively higher
portion of light absorption. It should be noted that the calculated BrC
light absorption may vary exponentially with the value of AAEBC.
According to Monte Carlo simulations under 95 % confidence level, we found
that BrC light absorption contribution ratios in this work can range roughly
from 18 % to 48 % at 370 nm, 10 % to 37 % at 470 nm, 3 % to 27 %
at 520 nm, 3 % to 22 % at 590 nm, and 1 % to 16 % at 660 nm. Therefore, proper values of AAEBC have to be carefully
obtained for a particular study area, especially needing to be constrained by
the BC mass concentration, size distribution, and mixing state measurements.
Additionally, the measurements of major water-soluble inorganic ions
(including K+, NO3-, and NH4+) and particulate OC
showed consistent features with those of σabs,BrC,370nm,
implying that BrC was associated with biomass burning emissions from nearby
rural areas. Moreover, the diurnal trend in σabs,BrC,370nm/OC indicated that primary biomass burning emissions can produce intense
light-absorbing BrC, while the photochemical aging process may weaken the
light-absorbing capability of BrC.
Using a radiative transfer model (i.e., SBDART), we estimated the BrC
effects on aerosol radiative forcing. The average shortwave aerosol direct
radiative forcings due to scattering, BrC absorption, and BC absorption at
the TOA were evaluated to be -21.4±5.5 W m-2, 2.3±1.8 W m-2, and 10.9±5.1 W m-2, respectively. BrC absorption
accounted for 15.8 % ± 4.4 % of the total shortwave solar absorption
warming effect at the TOA, indicating that BrC might be an important climate
forcing agent, which is largely neglected in current climate models. To
facilitate the estimation of the climate effects of BrC, a set of lookup
charts was constructed for the investigated area based on the default
tropical atmosphere profile, average surface albedo, average asymmetry
factor, and surface-measured aerosol properties (i.e., BrC absorption
contribution, SSA, and AOD). Therefore, the role of the BrC radiative forcing
efficiency at three wavelengths can be conveniently assessed for certain
SSA and BrC absorption contributions.
Data availability
The field observation data and modeling parameters used in this study have been given as tables and time series plots in the Supplement. The sun photometer measurement data were acquired from the website of AERONET (https://aeronet.gsfc.nasa.gov/, last access: 3 August 2018; Nichol and Wong, 2005) and the surface albedo data were acquired from the website of MODIS (https://search.earthdata.nasa.gov/, last access: 23 August 2018; LP DAAC, 2000).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-11669-2019-supplement.
Author contributions
HT, JZ, YM, and CC designed the experiments, and ZL, LL, YQ, NW, FL, YL, and
MC carried out the field measurements and data analysis. ZL and YQ performed
Mie theory simulation. ZL, JZ, and HT prepared the paper with comments
from all coauthors.
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 is supported by the National Key Project of MOST (2016YFC0201901,
2016YFC0203305, and 2016YFC0202401), the National Natural Science Foundation
of China (41575122 and 41730106), and the National Research Program for Key
Issues in Air Pollution Control (no. DQGG0103). We are also deeply thankful
for Cheng Wu and the staff at the Hong Kong Polytechnic University site of
AERONET.
Financial support
This research has been supported by the Ministry of Science and Technology of the People's Republic of China (grant nos. 2016YFC0201901, 2016YFC0203305, and 2016YFC0202401), the National Natural Science Foundation of China (grant nos. 41575122 and 41730106), and the National Research Program for Key Issues in Air Pollution Control (no. DQGG0103).
Review statement
This paper was edited by Jingkun Jiang and reviewed by three anonymous referees.
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