Source-specific optical properties of light-absorbing
carbonaceous (LAC) aerosols in the atmosphere are poorly understood because
they are generated by various sources. In this study, a receptor model
combining multi-wavelength absorption and chemical species was used to
explore the source-specific optical properties of LAC aerosols in a tropical
marine monsoon climate zone. The results showed that biomass burning and
ship emissions were the dominant contributors to average aerosol light
absorption. The source-specific absorption Ångström
exponent (AAE) indicated that black carbon (BC) was the dominant LAC aerosol
in ship and motor vehicle emissions. Moreover, brown carbon (BrC) was
present in biomass-burning emissions. The source-specific mass absorption
cross section (MAC) showed that BC from ship emissions had a stronger
light-absorbing capacity compared to emissions from biomass burning and
motor vehicles. The BrC MAC derived from biomass burning was also smaller
than the BC MAC and was highly dependent on wavelength. Furthermore,
radiative effect assessment indicated a comparable atmospheric forcing and
heating capacity of LAC aerosols between biomass burning and ship emissions.
This study provides insights into the optical properties of LAC aerosols
from various sources. It also sheds more light on the radiative effects of
LAC aerosols generated by ship emissions.
Introduction
Carbonaceous aerosols are abundant in PM2.5 (particulate matter with an
aerodynamic diameter ≤ 2.5 µm) (e.g., 20 %–50 % of PM2.5
mass; Putaud et al., 2010; Tao et al., 2017) and have extensively been
explored due to their implications on global climate forcing (IPCC, 2013).
Among the complex carbonaceous compounds are the light-absorbing
carbonaceous (LAC) aerosols which are mainly associated with absorption of
light. LAC aerosols consist of black carbon (BC) and brown carbon (BrC). BC
is a short-lived climate forcer with a strong ability to absorb sunlight.
Moreover, it is regarded as the second largest contributor of positive
anthropogenic climate forcing after carbon dioxide (Bond et al., 2013). On
the other hand, BrC refers to a class of light-absorbing organic compounds
with enhanced light absorption at short wavelengths (e.g., near-ultraviolet
region). Therefore, it is a potential contributor to atmospheric heating at
both global and regional scales (Laskin et al., 2015).
The optical properties of LAC aerosols are closely related to their sources
as well as atmospheric conditions and secondary processing. However,
distinguishing source-specific light absorption by LAC from a mixture of
aerosols in the atmosphere is still a challenge. It is possible to use
multi-wavelength light absorption data to identify optical source
apportionment based on the Beer–Lambert law (e.g., Aethalometer model and
multi-wavelength absorption analyzer model; Sandradewi et al., 2008;
Massabò et al., 2015), which can typically explain two different types
of sources (e.g., fossil fuels versus biomass burning). Results from this
method are highly dependent on the use of the source-specific absorption
Ångström exponent (AAE). However, due to lack of
source-specific AAE data, most studies use empirical values reported in
previous literature (e.g., Healy et al., 2017; Küpper et al., 2018;
Zheng et al., 2019). This may create inconsistencies in the reported results
because the source-specific AAE varies with the type of fuels and their
burning efficiencies (Tian et al., 2019).
In addition, optical source apportionment can be obtained using receptor
models (e.g., positive matrix factorization (PMF) and Multilinear Engine
(ME-2)). Several studies have utilized receptor models to identify sources,
first based on the sole chemical species or mass spectra information.
Thereafter, a multiple linear regression model is used to apportion the
contribution of each source to the optical parameters of an aerosol (Qin et
al., 2018; Tian et al., 2020). This method may be referred to as indirect
optical source apportionment. In contrast, Forello et al. (2019) coupled
chemical species with multi-wavelength absorption in ME-2 to directly perform
optical source apportionment. Compared to the indirect approach, the
additional optical data in receptor models can improve the performance of
source apportionment because each source has its own optical features.
Furthermore, it may eliminate potential uncertainties caused by multiple
operations in the indirect approach. However, the application of direct
optical source apportionment is scarce at the moment.
Alternatively, laboratory studies may effectively be used to explore the
optical properties of LAC from a specific source (e.g., vehicle engine
exhaust, coal combustion, and biomass burning) (Tian et al., 2019; Xie et
al., 2017). However, the optical properties of LAC may significantly change
due to the complex atmospheric processes that they undergo after emission
into the atmosphere. Therefore, it is critical to identify LAC aerosols from
different sources in the atmosphere using specific methods in order to
obtain their optical properties. Furthermore, to the best of our knowledge,
there is no study focusing on the optical properties of ship-exhaust-related
LAC aerosols in the atmosphere. This presents a challenge to our
understanding of the role of ship emissions in the climate considering that
it is a significant part of discharge from the transport sector.
In this study, multi-wavelength aerosol light absorption and chemical
species were measured in Sanya, a coastal city in China. This was done to
investigate the optical properties of LAC aerosols from ship emissions and
other sources. A dataset combining optical and chemically speciated data was
used simultaneously in a receptor model to obtain the optical source
apportionment. Afterwards, the source-specific optical properties of LAC
aerosols were determined and characterized. Finally, the impact of radiative
effect induced by LAC aerosols from different sources was evaluated. This
study provides insights into the source-specific optical properties of LAC
aerosols from various sources. Additionally, it reinforces knowledge on the
radiative effects of LAC aerosols.
The sampling site is located in Sanya, a small city (an area of 1921.5 km2 and a total population of 0.59 million as at 2017) in
southernmost tip of the Hainan Island in southern China (Fig. 1).
Comprehensive measurements were taken in spring from 12 April to
14 May 2017 on the rooftop of a teaching building (about 20 m above
ground level) in Hainan Tropical Ocean University (18.30∘ N,
109.52∘ E). The sampling site is predominantly an educational and
residential area with typical urban sources of emissions including vehicles
and cooking appliances. Sanya lies within a tropical marine monsoon climate
zone; therefore the weather was warm (temperature = 28 ± 3 ∘C) and wet (relative humidity (RH) = 81 ± 12 %) during the study.
Online and offline measurements
A model AE33 Aethalometer (Magee Scientific, Berkeley, CA, USA) was used to
determine the light absorption coefficients of the aerosols at
multiple wavelengths (Abs(λ); λ is wavelength) with a
PM2.5 cyclone (SCC 1.829, BGI Inc. USA). Briefly, the collected
particles were desiccated (RH = 22 ± 7 %) using a
Nafion® dryer (MD-700-24S-3; Perma Pure, Inc.,
Lakewood, NJ, USA) before measurement with the AE33 Aethalometer. As shown
in Fig. S1, the loss of Abs(λ) caused by the dryer was ignored.
Afterwards, seven light-emitting diodes (λ= 370, 470, 520, 590,
660, 880, and 950 nm) in the AE33 Aethalometer were used to irradiate the
filter deposition spot to obtain light attenuation as previously described
(Drinovec et al., 2015). Since the AE33 Aethalometer records the BC mass
concentrations, the Abs(λ) values at each wavelength were retrieved by
getting the product of BC mass concentration ([BC]) and mass absorption
cross section (MAC) used in the instrument (Abs(λ)= [BC] × MAC) (Drinovec et al., 2015). One of the advantages of the AE33
Aethalometer is that it resolves the filter loading effect using a dual-spot
compensation technique. Further details regarding the principles of
operation of the AE33 Aethalometer have been outlined by Drinovec et al. (2015).
In addition, a photoacoustic extinctiometer (PAX, Droplet Measurement
Technologies, Boulder, CO, USA) was used to directly measure aerosol light
absorption at λ= 532 nm. It was set in parallel with the AE33
Aethalometer using the same PM2.5 cyclone and
Nafion® dryer. Briefly, the PAX adopts an
intracavity photoacoustic technique, with a modulated laser beam heating up
the sampled particles in an acoustic chamber. The pressure wave generated
from heating is then detected by a sensitive microphone. Moreover, aerosol
light scattering can be measured using a wide-angle integrating reciprocal
nephelometer in a scattering chamber. In this study, different concentration
gradients of ammonium sulfate and freshly generated propane soot were used
to calibrate light-scattering and absorption measurements, respectively. The
calibration procedure was described in detail by Q. Wang et al. (2018a).
The PM2.5 quartz-fiber filters (8×10 inch) (QM/A; GE
Healthcare, Chicago, IL, USA) were collected during the day (from 08:00 to
20:00) and at night (from 20:00 to 08:00 the next day) using a high-volume
air sampler (Tisch Environmental, Inc., USA) with a flow rate of 1.13 m3 min-1. Before sampling, the blank quartz-fiber filters were heated in a
muffle furnace at 805 ∘C for 3 h to remove possible impurities.
After sampling, the quartz-fiber filters were saved in a freezer at about
-20 ∘C to minimize evaporation of volatile material before
chemical analyses. Finally, field blanks were collected and analyzed to
eliminate potential background artifacts.
The collected quartz-fiber filters were used to analyze inorganic elements,
carbonaceous matter, water-soluble ions, and organics. An energy-dispersive
X-ray fluorescence (ED-XRF) spectrometer (Epsilon 5 ED-XRF, PANalytical
B.V., Netherlands) was used to determine the titanium (Ti), vanadium (V),
manganese (Mn), ferrum (Fe), nickel (Ni), copper (Cu), zinc (Zn) and bromine
(Br) quantities. A detailed description of the principles of ED-XRF has been
highlighted by Xu et al. (2012). Moreover, a thermal–optical carbon analyzer
(Desert Research Institute model 2001, Atmoslytic Inc., Calabasas, CA, USA)
was used to analyze organic carbon (OC) and elemental carbon (EC). A
detailed analytical procedure has been described elsewhere (Chow et al.,
2007). An ion chromatograph (IC, Dionex 600; Dionex Corporation, Sunnyvale,
CA, USA) was also used to quantify the water-soluble cations (i.e.,
Na+, K+, Mg2+, Ca2+ and NH4+) and anions
(i.e., Cl-, NO3- and SO42-) as described by Zhang
et al. (2011). Finally, an in-injection port thermal desorption (TD) instrument coupled
with an Agilent 7890/5975C gas chromatographer–mass spectrometer (GC–MS)
(Agilent Technologies, Santa Clara, CA, USA) was used to determine the
hopanes using a protocol described by J. Wang et al. (2016).
Segregation of BC and BrC absorption
The Abs(λ) consisted of light absorption from both LAC aerosols (BC
and BrC) and mineral dust (Wang et al., 2013). Therefore, LAC absorption
(AbsLAC(λ)) was calculated as follows:
AbsLAC(λ)=AbsBC(λ)+AbsBrC(λ)=Abs(λ)-Absmineral(λ),
where AbsBC(λ), AbsBrC(λ) and
Absmineral(λ) were absorption of light by BC, BrC and mineral
dust at λ= 370, 470, 520, 590, 660 or 880 nm, respectively (in
units of per million meters). The Absmineral(λ) was retrieved from the
optical source apportionment as discussed in Sect. 3.2. With an assumption
of BC only absorbing at λ= 880 nm, the AbsBC(λ) at
wavelengths of 370, 470, 520, 590 and 660 was extrapolated as follows:
AbsBC(λ)=Abs(880)×λ880-AAEBC,
where AAEBC represents BC AAE, which was assumed to be 1.1 based on a
study by Lack and Langridge (2013). Combining Eqs. (1) and (2) gave the
following equation:
AbsBrCλ=Absλ-Abs(880)×λ880-AAEBC-Absmineralλ.
From the perspective of emission and formation, the Abs(λ) could be
divided into light absorption contributed by primary emissions
(Abspri(λ)) and secondary formation (Abssec(λ)).
Therefore, the Abs(λ) could be calculated as follows:
Abs(λ)=Abspri(λ)+Abssec(λ).
A BC-tracer method was utilized to separate Abssec(λ) from
Abspri(λ), and Eq. (4) could further be developed as
follows (Wang et al., 2019a):
Abssecλ=Absλ-AbsλBCpri×[BC],
where Abs(λ)BCpri described the ratio of
Abs(λ) to BC mass concentration in primary emissions (in units of
square meters per gram) and [BC] denoted the mass concentration of BC in the
atmosphere (in units of micrograms per cubic meter). This was retrieved from the
relationship between Abs(880) measured by the AE33 Aethalometer and EC mass
concentration. Finally, the Abs(λ)BCpri ratio was determined using a
minimum R-squared (MRS) method previously described by Wang et al. (2019a).
Estimation of optical parameters
AAE reflects spectral dependence of aerosol light absorption and can be used
to distinguish the chemical composition of LAC aerosols. For example, LAC
aerosol dominated by BC has an AAE close to 1.0 while the presence of BrC
results in an AAE larger than 1.0 (Andreae and Gelencsér, 2006). As
described previously, AAE could be retrieved using a power-law function as
follows (Andreae and Gelencsér, 2006):
Abs(λ)=C×λ-AAE,
where C is a constant independent of wavelength.
Additionally, MAC could be used to reflect the light absorption capacity of
aerosols. The MACs of BC and BrC at different wavelengths
(MACBC(λ) and MACBrC(λ), respectively) were
calculated with AbsBC(λ) and AbsBrC(λ) divided
by the corresponding mass concentrations of BC and organic matter (OM),
respectively:
7MACBC(λ)=AbsBC(λ)[BC],8MACBrC(λ)=AbsBrC(λ)[OM],
where the mass concentration of OM was estimated by a factor of 1.8 times
that of OC mass concentration (Turpin and Lim, 2001).
Receptor model source apportionment
The PMF version 5.0 (PMF5.0) from the US Environmental Protection Agency
(Norris et al., 2014) was applied to determine the contribution of various
sources to aerosol light absorption. The principle of PMF has been described
elsewhere (Paatero and Tapper, 1994). Briefly, PMF decomposes the initial
dataset into a factor contribution matrix Gik (i×k dimensions) and a factor profile matrix
Fkj (k×j dimensions) and then iteratively
minimizes the object function Q:
9Xij=∑k=1pGikFkj+Eij,10Q=∑i=1m∑j=1nEijσij2,
where Xij was the value of the jth species in the
ith sample, Eij described the model residual and
σij represented uncertainty, which was
calculated as follows:
σij=(errorfraction×concentration)2+(0.5×MDL)2,(concentration>MDL)56×MDL,concentration≤MDL,
where MDL was the method detection limit and the error fraction was set to
10 % (Rai et al., 2020). The uncertainties of the PMF5.0 results were
evaluated by the following analyses: bootstrap (BS), displacement (DISP)
and bootstrap–displacement (BS–DISP). The BS analysis assesses the random
errors in PMF solutions while DISP estimates rotational ambiguity. On the
other hand, BS–DISP estimates both random errors and rotational ambiguity. A
more detailed description of the three error estimation methods has been
provided by Paatero et al. (2014) and Brown et al. (2015).
The Optical Properties of Aerosols and Clouds (OPAC) model
The OPAC model was used to retrieve the following parameters: aerosol
optical depth (AOD), single-scattering albedo (SSA) and asymmetric
parameter (AP). The parameters were important in estimating the radiative
effect of aerosols. A detailed description of the OPAC software package was
given by Hess et al. (1998). The refractive index of BC and other species
used in the OPAC model is shown in Fig. S2. The measured mass
concentrations of OC, EC and water-soluble ions as well as the estimated
mineral dust loading (= [Fe] / 0.035) during the day were used in the OPAC
model to estimate the optical parameters. Moreover, the BC number
concentration in the OPAC model was constrained by the measured EC mass
concentration. Although several water-soluble ions and mineral dust were
obtained, they did not contain all the water-soluble and insoluble material.
Therefore, based on the measured data, the number concentrations of
water-soluble and insoluble materials were tuned. This was done until the
differences between the OPAC-derived light scattering, light absorption and
SSA versus the corresponding PAX-measured values were within 5 % (Fig. S3). After the aerosol light extinction coefficient (sum of light scattering
and absorption) was obtained, the AOD was estimated as follows (Hess et al.,
1998):
AOD=∑j∫Hj,minHj,maxσe,j(h)dh=∑jσe,j1Nj(0)∫Hj,minHj,maxe-hZjdh,
where Hj,max and Hj,min are the upper and lower boundaries in
layer j, σe,j is the surface aerosol light extinction
coefficient in layer j, h is the layer height, σe,j1
represents the aerosol light extinction coefficient that was normalized to
1 particle cm-3, Nj is the number concentration in layer
j, and Z was the scale height. Furthermore, the OPAC-derived
AODs were tuned to match the satellite-derived AODs
(https://giovanni.gsfc.nasa.gov/giovanni, last access: January 2020) by
altering the scale height in OPAC until the difference between them was
within 5 %. Owing to closure with AOD and anchoring of chemical
composition, the assumptions in the OPAC model did not have a significant
impact on the estimation of radiative effect in subsequent Sect. 2.7
(Satheesh and Srinivasan, 2006).
Estimations of radiative effect and heating rate
The LAC direct radiative effect (DRE) was estimated by the Santa Barbara
DISORT (Discrete Ordinate Radiative Transfer) Atmospheric Radiative Transfer
(SBDART) model in the shortwave spectral region of 0.25–4.0 µm. A
detailed description of the SBDART model was given by Ricchiazzi et al. (1998). The AOD, SSA and AP are essential input parameters in the SBDART
model and were obtained from the OPAC model (see Sect. 2.6). In addition
to these, several other input parameters were included, namely the surface
albedo, solar zenith angle and profiles of atmospheric parameters. The
surface albedo was derived from the Moderate Resolution Imaging
Spectroradiometer (https://modis-atmos.gsfc.nasa.gov/ALBEDO/index.html, last
access: January 2020). On the other hand, the solar zenith angle was
calculated with a specific time and location (i.e., latitude and longitude)
using a small code from the SBDART model. Furthermore, six standard
atmospheric vertical profiles (i.e., tropical, midlatitude summer,
subarctic summer, midlatitude winter, subarctic winter and US62) were
embedded in the SBDART model. They provided vertical distributions of
temperature, pressure, water vapor and ozone density (Ricchiazzi et al.,
1998). In this study, the midlatitude summer was selected to represent the
situation of Sanya based on its classification as a midlatitude region.
Obregón et al. (2015) demonstrated that the SBDART model could provide a
reliable estimation of radiative effect. Moreover, aerosol DRE was defined
as the difference in the radiation flux (F) either at the Earth's
surface or at the top of the atmosphere, respectively with and without the
aerosol in the atmosphere:
DRE=F↓-F↑withaerosol-F↓-F↑withoutaerosol,
where ↓ and ↑ represent the downward and
upward fluxes, respectively. Atmospheric DRE was then estimated by the
difference between the DRE at the top of the atmosphere and the Earth's
surface.
Further, the atmospheric heating rate (∂T∂t, in units of kelvin per day) caused by LAC aerosols was
estimated using the first law of thermodynamics and hydrostatic equilibrium
as follows (Liou, 2002):
∂T∂t=gCp×FP,
where gCp is the lapse rate and
g stands for acceleration due to gravity while
Cp describes the specific heat capacity of air at a
constant pressure (1006 J kg-1 K-1). Additionally, ΔF is the atmospheric DRE contributed by LAC aerosols, and ΔP represents the atmospheric pressure difference (300 hPa).
Summary of light absorption at different wavelengths
(Abs(λ), λ= 370, 470, 520, 590, 660 and 880 nm) and
absorption Ångström exponent (AAE) of different
emission sources.
* AAEtotal represents the AAE caused by total light-absorbing
aerosols while AAEship, AAEbiomass and AAEvehicle are AAE
from ship emissions, biomass burning and motor vehicle emissions,
respectively.
Time series of hourly averaged light absorption at
different wavelengths (Abs(λ), λ= 370, 470, 520, 590,
660 and 880 nm). The different types of horizontal lines represent the four
clusters of air masses.
Results and discussionOverview of Abs(λ)
The AE33 absorption was first corrected using PAX measurement, and a strong
correlation (r=0.96, p< 0.01) between them was found (Fig. S4).
A slope of 2.3 was regarded as the correction factor and was comparable to
the values of 2.0–2.6 reported by previous studies using a similar method
(Qin et al., 2018; Tasoglou et al., 2017; Wang et al., 2019b). This
difference may mainly be related to the matrix scattering and lensing
effects. The time series of corrected Abs(λ) is shown in Fig. 2, and
a statistical summary of the data is presented in Table 1. The average
Abs(λ) values were 15.7 ± 5.3, 11.4 ± 3.7, 9.7 ± 3.0,
8.3 ± 2.6, 7.0 ± 2.2 and 4.9 ± 1.5 M m-1 at 370, 470,
520, 590, 660 and 880 nm, respectively, throughout the study.
However, it is noteworthy that such single-wavelength calibrations may
overestimate Abs(λ) at long wavelengths (i.e., λ= 590,
660 and 880 nm) and underestimate it at short wavelengths (i.e., λ= 370 and 470 nm) owing to the correction factor's dependence on
wavelength (Kim et al., 2019). Compared to previous work, the Abs(λ) values in this study were lower than those obtained from urban areas in China
and Europe (J. Wang et al., 2018; Liakakou et al., 2020). However, they were
comparable to some rural and remote areas where anthropogenic activities
were not intensive (Zanatta et al., 2016; Zhu et al., 2017). This suggests
the possibility of a relatively small LAC burden in the atmosphere at Sanya
during the study.
Additionally, AbsBC(λ) contributed more than 77 % to
Abs(λ), whereas the contribution of AbsBrC(λ) was less
than 17 % (Fig. 3). This was consistent with previous studies showing that
BC was stronger at absorbing light compared to BrC at the near-ultraviolet
to near-infrared wavelengths in the atmosphere (Massabò et al., 2015;
Liakakou et al., 2020). However, laboratory studies reported that
AbsBrC(λ) could exceed AbsBC(λ) at short
wavelengths in fresh smoke from biomass burning, especially in the
smoldering phase (Tian et al., 2019; Chow et al., 2018). Furthermore, the
fraction of AbsBC(λ) increased with an increase in wavelength,
although the fraction of AbsBrC(λ) showed an inverse trend
with a dramatic drop from 17 % at 370 nm to 3 % at 660 nm as shown in
Fig. 3. This suggests a stronger light-absorbing capacity by BrC at short
wavelengths compared to the long ones. With regard to the relationship
between Abs(λ) and carbonaceous composition, the
AbsBC(λ) correlated well with EC mass concentration (r= 0.93, p< 0.01, Fig. S5). However, a weak but significant correlation
was observed between AbsBrC(λ) and OC mass concentration (r= 0.27–0.42, p< 0.05, Fig. S6). The results further confirmed
that BC was the dominant light-absorbing material in LAC aerosols while OC
contained more non-light-absorbing carbon components compared to the
light-absorbing ones.
Light absorption fractions of BC, BrC and MD in the
total light-absorbing aerosols. BC: black carbon; BrC: brown carbon;
MD: mineral dust.
The average mass concentrations of PM2.5,
carbonaceous matter, water-soluble ions, inorganic elements and organics
during the campaign.
To quantify the contributions of various sources to Abs(λ),
chemical species and Abspri(λ) were simultaneously used as
input parameters in the PMF5.0 model. Online Abspri(λ) data
were integrated to match each filter sampling time. The selected chemical
species included carbonaceous matter (i.e., OC and EC), water-soluble
cations (i.e., Na+, K+ and Ca2+), elements (i.e., Ti, V, Mn,
Fe, Ni, Cu, Zn and Br) and hopanes. The mass concentrations of the
chemicals are summarized in Table 2. Based on Eq. (5), Abssec(λ) accounted for less than 5 % of Abs(λ) (Table S1), suggesting a
negligible impact of secondary formation on the light absorption capacity of
aerosols during the study. Therefore, the uncertainty caused by using only
Abspri(λ) in the model could be put to rest in the absence of an effective way to identify the source of secondary BrC.
Moreover, two to seven factors were selected to initiate the PMF5.0 run. Due
to the additional factors, the Q/Qexp ratio decreased with the
increased number of factors as shown in Fig. S7. The decrease in Q/Qexp
was large when the factor number changed from 2 to 3 and 3 to 4 but
stabilized as the factor number grew larger than 4, indicating that four
factors may be the optimal solution. After multiple runs of the PMF5.0
model, four factor sources including ship emissions, motor vehicle
emissions, biomass burning and fugitive dust were finally identified (Fig. 4a). Additionally, the modeled Abspri(λ) at different
wavelengths showed strong correlations with the measured Abs(λ) (r= 0.82–0.89, p< 0.01, Fig. S8). The slopes of 0.92–0.98 were
consistent with the absorption fractions of Abspri(λ)
estimated by the BC-tracer method combined with the MRS approach (Table S1).
The scaled residuals for each species varied between -3 and +3.
The uncertainty of each factor profile was further evaluated using BS, DISP
and BS–DISP. The BS results showed that the reproducibility of each source
factor was larger than 80 % (Table S2), indicating good stability.
Therefore, this suggested that the four source factors were appropriate. No
swaps occurred in DISP, indicating the stability of the selected solution.
Furthermore, all BS–DISP runs were successful. Overall, these results
pointed to the efficiency of the PMF5.0 model in performing optical source
apportionment.
(a) Contributions of the four sources to each species
from the positive matrix factorization model and (b) the light absorption of
primary aerosols from each source at different wavelengths
(Abspri(λ), λ= 370, 470, 520, 590, 660 and 880 nm) during the study.
The first source factor was characterized by large proportions of V, Ni and
hopanes as well as moderate amounts of OC, EC, Na+, K+, Cu and
Abspri(λ) as shown in Fig. 4a. V and Ni were associated with
oil fuel combustion (Moreno et al., 2010) and their ratio (V / Ni) can be used
to further identify ship engine emissions, which has a typical range of
2.5–4.0 (Cesari et al., 2014). The estimated V / Ni ratio was 3.4 in this
source factor, consistent with the previously established range of ship
engine emissions. Since hydrocarbons are the major components of ship engine
oil, hopanes, OC and EC can be produced as byproducts of the combustion
process. Therefore, this source factor was assigned to ship emissions. The
second source factor was associated with large amounts of Cu, Zn and Br as
well as moderate proportions of hopanes, EC, Ti and Abspri(λ). Previous studies confirmed that hopanes, Br and EC were typically
present in vehicle exhaust particles (Huang et al., 1994; Sheesley et al.,
2009). Additionally, Zn and Cu were associated with lubricant and metal
brake wear (Lin et al., 2015). Therefore, this source factor was allocated
to motor vehicle emissions. The third source factor was dominated by high
proportions of K+, OC, EC and Abspri(λ), which was an
obvious feature of biomass burning (Forello et al., 2019). Finally, the
fourth source factor was characterized by large amounts of several crustal
materials such as Ca2+, Ti, Fe and Mn and was identified as fugitive
dust.
Notably, biomass burning occupied the largest proportion of light absorption
(Absbiomass(λ)) at 32 %–44 % of Abspri(λ) as
shown in Fig. 4a. Sanya is a coastal city with heavy maritime traffic (e.g.,
the cargo handling capacity was larger than 5.8 million metric tons in 2017 at
Sanya port, http://tjj.sanya.gov.cn/tjjsite/2019nnj/tjnj.shtml, last access: March 2020, in Chinese);
therefore absorption of ship emissions (Absship(λ)) also had a
significant contribution to Abspri(λ) (30 %–39 %). The
contribution of motor vehicle emissions (Absvehicle(λ)=17 %–24 % of Abspri(λ)) was much lower than that of biomass
burning and ship emissions. Moreover, the absorption of fugitive dust
(Absdust(λ)) occupied less than 10 % of Abspri(λ), consistent with previous reports where it was identified as a minor
contributor in the atmosphere (Yang et al., 2009; Zhao et al., 2019). This
small absorptive fraction may be attributed to the low proportion of
light-absorbing iron oxides in the atmosphere. Furthermore, the
Abspri(λ) values of different sources all decreased with increased
wavelength (Fig. 4b) although their relative contributions displayed
distinct trends (Fig. 4a). The fraction of Absship(λ) and
Absvehicle(λ) increased with an increase in wavelength while a
reverse trend was observed in the Absbiomass(λ) fraction. This
discrepancy can be explained by the large amount of BrC present in
biomass-burning emissions which can result in more light absorption at short
wavelengths relative to the long ones.
The light absorption (Abs(λ)) of light-absorbing
carbonaceous (LAC) aerosols from ship emissions, traffic emissions and
biomass burning. The dashed line is power-law fit.
Source-dependent optical properties of LAC aerosols
According to a power-law function (Fig. 5), the average LAC AAE (1.41, Table 1) was greater than unity during the study, indicating the presence of both
BC and BrC in the atmosphere. In addition, the estimated AAE of motor
vehicle emissions (AAEvehicle) was 0.96. This was close to the
previously reported range of 0.9–1.1 obtained from ambient observations
using the radiocarbon method or vehicle-exhaust-related source experiments
(Sandradewi et al., 2008; Zotter et al., 2017; Chow et al., 2018). This
narrow range of AAEvehicle obtained by various studies suggests that
the spectral dependence of vehicle-exhaust-related LAC absorption was less
affected by atmospheric processes. Furthermore, the AAE of ship emissions
(AAEship=1.06) was similar to that of AAEvehicle. These low
spectral dependences of light absorption indicate that BC was the dominant
compound in LAC aerosols from ship and motor vehicle emissions. Compared to
marine engine emissions, the AAEship obtained in this study was
consistent with the values derived from marine gas oil and diesel fuel
emissions (1.0 ± 0.1) but was lower than heavy fuel oil exhaust (1.7±0.2) (Corbin et al., 2018). This indicates that Sanya may be
influenced more by ships using distillate rather than heavy fuel oil.
The AAE of biomass burning (AAEbiomass=1.75) was larger than that
from ship and motor vehicle emissions. This implied the presence of BrC in
LAC aerosols derived from biomass burning in addition to BC. The observation
corroborated with previous studies which showed that BrC was mainly derived
from biomass burning rather than fossil fuels in the atmosphere (Laskin et
al., 2015). Additionally, chamber studies showed that the AAEs of fresh
smoke from biomass burning varied largely (e.g., 1.64–3.25), depending on
the type of biomass and their burning efficiencies (Tian et al., 2019). The
AAEbiomass from this study was close to those values (1.7–1.9) from the
atmosphere constrained by the radiocarbon method (Sandradewi et al., 2008;
Zotter et al., 2017). Given that the approach used in this study could
retrieve the source-specific AAEs in the atmosphere, it can also improve the
performance of those optical source apportionment models based solely on
optical data.
The source-specific mass absorption cross section (MAC)
of black carbon (BC) and brown carbon (BrC) at different wavelengths. The
MACs of uncoated BC particles at each wavelength are extrapolated from 7.5 m2 g-1 at 550 nm (Bond and Bergstrom, 2006) by assuming an BC
absorption Ångström exponent of 1.1.
Owing to the dominance of BC in ship and motor vehicle emissions, only
MACBC(λ) was estimated for these two sources. The results of
optical source apportionment revealed that the estimated MACBC(λ) values of motor vehicle emissions (MACBC,vehicle(λ)) were close to
the values of uncoated BC particles at different wavelengths (Fig. 6). This
indicated that vehicle-exhaust-related BC particles were mainly associated
with local emissions and underwent minor atmospheric aging processes. In
contrast, the MACBC(λ) of ship emissions
(MACBC,ship(λ)) was 1.4–1.6 times larger than that of the
uncoated ones (Fig. 6). This implied that ship-exhaust-related BC particles
were prone to substantial aging during transit from the ocean. Freshly
emitted BC particles from fossil fuels tend to mix externally with other
substances and become internally mixed ones after aging (Xing et al., 2020).
It was therefore unexpected for the obtained MACBC,ship(λ) to
have a similar value as that of marine engine emissions reported by Corbin
et al. (2018) (7.8 m2 g-1 at 780 nm, extrapolated to the same
wavelengths in this study by assuming an AAEBC=1.1). Consequently,
more work is needed to understand the large MACBC(λ) values
from marine engine emissions.
Since LAC aerosols derived from biomass burning were comprised of both BC and
BrC, the MACBC(λ) and MACBrC(λ) were retrieved
based on the results of optical source apportionment. The findings revealed
that the MACBC(λ) of biomass burning
(MACBC,biomass(λ)) was larger than MACBC,vehicle(λ) as shown in Fig. 6. This was consistent with previous studies showing a
stronger capacity to absorb light by BC from biomass burning compared to
that from motor vehicle emissions (Qiu et al., 2014; Q. Wang et al., 2018b).
Moreover, the MACBC,biomass(λ) was smaller than
MACBC,ship(λ), suggesting a stronger ability to absorb light
by BC particles from ship exhaust. A broader implication of this observation
is that more focus should be put on BC particles related to ship emissions
due to their impact on climate given the increase in shipping activities
globally.
The MACBrC(λ) of biomass burning (MACBrC,biomass(λ)) was highly dependent on wavelength, with 0.9 m2 g-1 at
λ= 370 nm but dropping close to zero (0.02 m2 g-1) at
λ= 660 nm (Fig. 6). Additionally, the
MACBrC,biomass(λ) was several times to 2 orders of magnitude
lower than MACBC(λ) from different sources, suggesting that BC
had a stronger ability to absorb light compared to BrC. Notably, the
MACBrC(λ) obtained in this study was within the range
reported by previous investigations, although with differences among studies
(Wang et al., 2019b; Cho et al., 2019). The differences in
MACBrC(λ) may partly be related to biomass types and their
burning efficiencies as well as the aging processes of BrC in the
atmosphere. In addition, the use of different BrC substitutes (e.g., OM,
organic aerosol or water-soluble organic carbon) may have impacted the
calculation of MACBrC(λ). Compared to previous laboratory
studies, the MACBrC,biomass(λ) obtained here was smaller than
that of fresh smoke from biomass burning (Zhong and Jang, 2014; Pandey et
al., 2016). Given that photobleaching is an effective way of turning BrC
into a transparent organic substance (Laskin et al., 2015), the smaller
atmospheric MACBrC,biomass(λ) observed in this study may be
attributed to the elimination of organic chromophores induced by the bright
sunlight at Sanya.
The MAC links the mass of a LAC aerosol to its ability to absorb light and
is an important parameter in climate models to evaluate global or regional
radiative effects of LAC aerosols. An equal MAC from different sources is
often assumed in climate models (Bond et al., 2013) because identification
of source-specific MACs in the atmosphere is still a challenge. However,
this assumption can lead to uncertainties due to distinct MACs from various
sources (e.g., MACBC,ship(λ)> MACBC,biomass(λ)> MACBC,vehicle(λ)
in this study). The chemical-composition-based optical source apportionment
approach may provide a potential solution to this challenge. Nonetheless,
more source-specific MACs in different areas and seasons are needed in
future studies to gauge the accuracy of climate models. Moreover, this
approach may minimize the uncertainties of BC source apportionment using the
“Aethalometer model” (Sandradewi et al., 2008; Zotter et al., 2017) due to
the assumption of equal AAE and MAC from different sources.
Direct radiative effect (DRE) of light-absorbing
carbonaceous (LAC) aerosols from biomass burning, ship emissions, and motor
vehicle emissions. The error bar represents 1 standard deviation. ES, TOA,
ATM represent the DRE at the Earth's surface, the top of the atmosphere, and
in the atmosphere, respectively.
Impacts of LAC aerosols on radiative effect
Figure 7 shows the source-specific LAC DRE during the study. The LAC DRE
varied from -5.5 to -1.6 W m-2 at the Earth's surface with an average
cooling effect of -3.2± 1.0 W m-2. In contrast, the LAC aerosols
produced a warm effect of +1.5 ± 0.5 W m-2 at the top of the
atmosphere with a range of +0.8 to +2.8 W m-2, suggesting a net
energy gain. The presence of LAC aerosols enhanced aerosol DRE at the top of
the atmosphere by 62 % compared to the results of light scattering by
aerosols only. Moreover, the difference between LAC DRE at the top of the
atmosphere and the Earth's surface gave the atmospheric DRE (a net
atmospheric absorption) of +4.7 ± 1.5 W m-2 and could generate
a heating rate of 0.13 ± 0.04 K d-1.
With regard to LAC absorption sources, biomass burning was the largest
contributor to LAC DRE at -1.5± 0.5 and +0.7 ± 0.2 W m-2 on the surface of the Earth and the top of the atmosphere,
respectively. In addition, BrC from biomass burning had a lower contribution
to LAC DRE compared to BC from the same source. However, the presence of BrC
reinforced the LAC DRE of biomass burning by 21 % as opposed to BC only,
suggesting a substantial radiative effect from BrC aerosol. Additionally,
the LAC DRE values were -1.1± 0.4 and +0.5 ± 0.2 W m-2 for ship emissions and -0.6± 0.2 and +0.3 ± 0.1 W m-2 for motor vehicle emissions on the Earth's surface
and the top of the atmosphere, respectively. The LAC DRE contributed by ship
and motor vehicle emissions was mainly caused by BC aerosol. Although a
larger BC atmospheric DRE was observed for biomass burning, ship emissions
showed an equivalent capacity of radiative effect (0.5 (W m-2) (µg m-3)-1) from per-unit BC mass concentration, generating atmospheric
DRE. In contrast, motor vehicle emissions had a smaller value of 0.3 (W m-2) (µg m-3)-1. Furthermore, the atmospheric heating
rate of LAC aerosols was similar for biomass burning (0.06 ± 0.02 K d-1) and ship emissions (0.05 ± 0.01 K d-1) but larger
than that produced by motor vehicle emissions (0.03 ± 0.01 K d-1). This further highlighted the importance of LAC aerosols from
ship exhaust in atmospheric heating.
Conclusions
In this study, the optical properties and radiative effect of LAC aerosols
in Sanya, a Chinese tropical marine monsoon climate zone, were explored. The
study found that light absorption caused by primary emissions was the main
contributor to LAC absorption while secondary processes played a minor role.
Moreover, BC aerosol (> 77 %) contributed more to
AbsLAC(λ) compared to BrC (< 17 %). Through a
combination of chemical species and multi-wavelength absorption in a
positive matrix factorization model, it was shown that biomass burning had
the highest contribution to Abspri(λ) (32 %–44 %) followed by
ship (30 %–39 %) and motor vehicle emissions (17 %–24 %). Fugitive dust
had the lowest contribution (< 10 %). Moreover, source-specific
AAE showed a similarity between ship and motor vehicle emissions (1.06
versus 0.96). The low spectral dependence of light absorption indicated that
LAC aerosols were dominated by BC in ship and motor vehicle emissions. In
contrast, a large AAE of 1.75 was found in biomass burning, indicating the
presence of both BC and BrC. Additionally, source-specific MAC showed that
BC particles from ship emissions had the strongest light-absorbing capacity,
followed by biomass burning and motor vehicle emissions. Compared to BC MAC,
the BrC MAC of biomass burning was smaller with a value of 0.9 m2 g-1 at λ= 370 nm but dropped to 0.02 m2 g-1 at
λ= 660 nm. The radiative transfer model also showed that the
atmospheric DRE caused by LAC aerosols was +4.7 ± 1.5 W m-2
during the study and corresponded to a heating rate of 0.13 ± 0.04 K d-1. The presence of BrC reinforced the LAC DRE of biomass burning by
21 % compared to BC only. Finally, ship emissions showed an equivalent
capacity to produce radiative effect (0.5 (W m-2) (µg m-3)-1) from per-unit BC mass concentration, generating atmospheric
DRE. In contrast, motor vehicle emissions had a smaller value of 0.3 (W m-2) (µg m-3)-1.
Data availability
All data described in this study are available upon request from the corresponding authors.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-15537-2020-supplement.
Author contributions
QW, JC and YH designed the campaign. PW and YZ provided the observation
site and assisted with field sampling and measurements. WD and TZ conducted
the chemical analyses. HL ran the PMF5.0 and SBDART model. JT performed the
cluster analysis of air-mass trajectories. WZ provided the ArcGIS map. QW
conducted the data analysis and wrote the article with input from all
co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors are grateful to the editor and two anonymous referees for their helpful comments.
Financial support
This research has been supported by the Strategic Priority Research Program of Chinese Academy of Sciences (grant no. XDB40000000), the Youth Innovation Promotion Association of the Chinese Academy of Sciences (grant no. 2019402), the Hainan Natural Science Foundation High-level Talent Project (grant no. 2019RC243) and the Science and Technology Cooperation Project of Sanya (grant nos. 2018YD14 and 2012YD38).
Review statement
This paper was edited by James Allan and reviewed by two anonymous referees.
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