Brown carbon (BrC) plays an essential impact on radiative forcing
due to its ability to absorb sunlight. In this study, the optical properties
and molecular characteristics of water-soluble and methanol-soluble organic
carbon (OC; MSOC) emitted from the simulated combustion of biomass and coal
fuels and vehicle emissions were investigated using ultraviolet–visible (UV–vis)
spectroscopy, excitation–emission matrix (EEM) spectroscopy, and
Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS)
coupled with electrospray ionization (ESI). The results showed that these
smoke aerosol samples from biomass burning (BB) and coal combustion (CC) had
a higher mass absorption efficiency at 365 nm (MAE
Carbonaceous aerosols play an important role in the Earth's radiative balance. One such aerosol, black carbon (BC), absorbs significant amounts of light and exerts a warming effect, while organic carbon (OC) was initially thought to only scatter solar radiation (Wong et al., 2017; Mo et al., 2017; Saleh et al., 2014). However, recent studies show that there are certain types of OC that absorb radiation efficiently in the near-ultraviolet (UV) (300–400 nm) and visible ranges, which are called brown carbon (BrC). They can positively shift the net direct radiation forcing (DRF) (Saleh et al., 2014; Laskin et al., 2015; Kirchstetter and Thatcher, 2012). According to a simulation model, the inclusion of BrC may enhance total aerosol absorption by 7 %–19 % (Feng et al., 2013). According to previous studies, BrC in atmospheric aerosols mainly originates from emissions from biomass burning (BB) and coal combustion (CC), vehicle exhaust, and the formation of secondary organic aerosol (SOA) (Zhu et al., 2018; Laskin et al., 2015; Xie et al., 2017; Kumar et al., 2018). Among them, primary emissions contribute significantly to BrC absorption (Fan et al., 2012; Yan et al., 2015; Zhang et al., 2011). Recently, many studies have investigated the optical properties and molecular characteristics of BrC in laboratory-simulated combustion (Budisulistiorini et al., 2017; Lin et al., 2018, 2016; Song et al., 2019) and their light absorption in controlled vehicle emissions (Xie et al., 2017). However, there were no available studies on the comprehensive characteristics of BrC in various sources and their variations in optical and chemical information impacted by these sources. Therefore, investigating BrC in different sources would improve our understanding of the evolution of BrC absorption.
Excitation–emission matrix (EEM) spectroscopy can provide structural
information of chromophores and thus has been widely applied to identify the
sources and chemical nature of chromophoric dissolved organic matter (CDOM)
in aquatic environments since the 1990s (Shimabuku et al., 2017; Wells et
al., 2017; Bhattacharya and Osburn, 2017; Coble, 1996). As the optical
properties of chromophoric water-soluble organic carbon (WSOC) in the
atmosphere were similar to CDOM in aquatic environments (Qin et al.,
2018; Fu et al., 2015; Graber and Rudich, 2006), this technique could extend
to atmospheric research. It has to be mentioned that fluorescence is a
radiative process that occurs between two energy levels of the same
multiplicity (Andrade-Eiroa et al., 2013). Generally, compounds
with rigid planar structures and highly conjugated systems have intrinsic
fluorescence emission characteristics and are important BrC chromophores,
such as aromatic acids, phenols, nitroaromatics, polycyclic aromatic
hydrocarbons (PAHs), quinones, and so on (Lin et al., 2018; Zhang et al.,
2013). In addition, chromophores in fluorescence spectra could be considered
as a “fingerprinting” tool, especially when combining it with parallel
factor (PARAFAC) analysis, which can decompose EEM signals into their
underlying chemical components (Murphy et al., 2013). For
instance, Chen et al. (2016b) observed that the water-extracted
chromophores identified by PARAFAC from the urban, forest, and marine
aerosols were varied with the sampling sites and periods and were affected
by oxidative and functional groups. Lee et al. (2013) illustrated
that SOA derived from the oxidation of limonene and decene with
Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) coupled with electrospray ionization (ESI) is a powerful platform for investigating the detailed characteristics of organic material at the molecular level. With the advantage of ultrahigh resolution, the accuracy of mass measurements, and high sensitivity (Feng et al., 2016), FT-ICR MS has been successfully used to characterize organic aerosols (Jiang et al., 2016; Song et al., 2018; Mo et al., 2018), cloud water (Zhao et al., 2013), and natural organic matter (Sleighter et al., 2012; Feng et al., 2016). For example, a previous study has determined their molecular families of dissolved organic matter (DOM) associated with fluorescent components by using FT-ICR MS (Stubbins et al., 2014), which could provide more chemical information about chromophores.
Residential CC and BB emissions and motor vehicle emissions are significant anthropogenic sources of air pollutants, especially fine particulate matter (PM) on urban and regional scales (Gentner et al., 2017; Yan et al., 2015; Zhang et al., 2018; Chen et al., 2015). In this study, to obtain a comprehensive understanding of BrC originating from various sources, ultraviolet–visible (UV–vis), EEM, and FT-ICR MS analyses were performed for WSOC and methanol-soluble organic carbon (MSOC) from the smoke particles of simulated combustion of biomass fuels and coals and vehicle emission aerosols. Statistical analysis of PARAFAC was applied to EEM spectra to resolve the fluorescent compounds. All of these and unique molecular characteristics of WSOC and MSOC were analyzed and discussed on the basis of FT-ICR MS. Relationships between optical properties and chemical structures were discussed by using a linear regression coefficient.
The smoke particles were collected by the instrument coupled with a dilution
channel which was designed to simulate fire emissions representative of
“real-world” open BB and household CC activities (Fig. S1 in the Supplement). In the
present study, a total of 27 BB samples (IDs1–27) were collected at
Xishuangbanna, Yunnan, from 20 May to 3 June 2016, and the
detailed sampling process was described in our previous article
(Cui et al., 2018). Briefly, raw fuels (rough
The smoke particles of CC (IDs 34–50) were collected in the same way as that of the BB
experiment, but they used a stove in Guangzhou, Guangdong, from
18 November 2017 to 23 January 2018. The tested stove is a technically
improved stove (named Jin-Yin stove). Due to the difficulty of ignition of
coal, we used smokeless charcoal to ignite one third (about 300 g) of the
raw-coal chunk (2–5 cm in size) in the stove, removed the charcoal after
ignition, and then added the remaining raw-coal chunk (about 700 g) to start
to collect the smoke particles. Every coal was also burned three times at
about 1 kg of fuel per burn. Every combustion process lasted for about
40–150 min. The collection flow rate and average dilution ratio were
150 L min
Tunnel aerosols (total eight samples; IDs 51–58) were collected at the Siping
tunnel from 1 to 2 November 2017 and the Xiaoyang Shan tunnel from 1 to 2 December 2017, in Shanghai and two vehicle exhaust particles
(IDs 59–60) were collected from the direct emission of two different trucks
(more fresh aerosols). With no other instructions, we used “vehicle
emissions” to represent all tunnel aerosols and vehicle exhaust particle
samples. These filters were wrapped in aluminum foil and pre-baked at 450
WSOC for UV–vis absorption and EEM analysis were extracted with purified
water (resistivity of
We measured both OC and elemental carbon (EC) using an aerosol carbon
analyzer (Sunset Laboratory, Inc., USA), following the National Institute for Occupational Safety and Health (NIOSH) thermal–optical
transmittance (TOT) standard method (Mo et al., 2017), and the emission
factors (EFs) of PM, OC, and EC were calculated, and detailed information was
presented in the Supplement. We also analyzed the elements of biomass (C, H, O,
and N) and coal (C, H, O, N, and S) using an elemental analyzer (Vario EL
cube, Elementar, Germany), and the results were listed in Tables S1 and S2.
The carbon content of WSOC was measured using total organic carbon analysis
(Vario TOC cube, Elementar, Germany) before acidification with phosphoric acid to remove
inorganic carbon, while the concentration of MSOC was assessed using the
method developed by a previous study (Chen et al., 2017b).
Briefly, the extracted MSOC was dried gently under nitrogen and then
re-dissolved in 500
Comparison of MAE
The UV–vis absorption and EEM spectra of WSOC and MSOC were analyzed using a
UV–vis spectrophotometer (UV-4802, Unico, China) and an Aqualog fluorometer
(HORIBA Scientific, USA), respectively. The wavelengths used to characterize
the UV–vis spectra were between 200 to 800 nm at a step size of 2 nm.
Purified water and methanol were used as a baseline correction for WSOC and
MSOC before measurement, respectively. Mass absorption efficiency (MAE; m
The emission and excitation wavelengths of the fluorescence spectra were from 245 to 580 nm and 240 to 500 nm, respectively. The wavelength increments of the emission and excitation scans were 4.66 and 3 nm, respectively. Further, the contributions of solvents to the fluorescence spectra were subtracted.
The WSOC and MSOC of six selected samples including two BB aerosols (
We used the analysis method of FT-ICR MS described in detail in our previous
study (Mo et al., 2018). Briefly, ultrahigh-resolution mass spectra were
obtained using a solariX XR FT-ICR MS (Bruker Daltonics GmbH, Bremen,
Germany) equipped with a 9.4 T superconducting magnet and an ESI ion source.
The system was operated in negative-ionization mode. The ion accumulation
time was set to 0.6 s. The lower and upper mass limit was set to a mass-to-charge ratio (
PARAFAC analysis with non-negativity constraints was used to explore the
fluorescent components in dissolved BrC based on the method established by
the previous studies (Murphy et al., 2013; Andersson and Bro, 2000), which
was performed using the decomposition routines for Excitation Emission Matrices (drEEM) toolbox version 2.0 using MATLAB
(
The PM, OC, and EC EFs of 27 biomass and 17 coal combustion experiments were
summarized in Table S3. The relevant EFs of some biomass species have been
previously reported (Cui et al., 2018). In this experiment,
the EFs of PM, OC, and EC from 27 types of biomass burning were
MAE can be used to characterize the efficiency of solar energy absorption,
which is represented by the degree of conjugation and the amount of electron
delocalization in molecules (Chen et al., 2016a). As
shown in Fig. 1 and Table S4, MAE at 365 nm (MAE
The MAE
Methanol has a lower polarity than water and can extract the water-insoluble
compounds that are generally stronger chromophores. Chen et al. (2017b) extracted organic matters in aerosols using different polar
solutions, and they found water-insoluble organic matter (WIOM) had a
higher MAE value than the water-soluble organic matter (WSOM), consistent
with our result for the BB and bituminous CC aerosols. Vehicle emission
aerosols generally had a lower MAE value such as methanol-soluble BrC (
Fluorescence spectra was used to characterize the organic chromophores of
different sources. We applied the PARAFAC model (Murphy et al.,
2013) to determine the underlying chromophore components of the 60 source
samples. Six typically independent components (abbreviated P1–6) in WSOC
were resolved, as shown in the top of Fig. 2 and Table 1. Compared to the
previous studies, the fluorescence of P1 and P6 were similar to those of
7CM–C1 (the C1 component of a seven-component model) and 7CM–C3, named
humic-like substances (HULIS-1) (Chen et al., 2016b). Further, there were
peaks in the emission wavelengths (
The maximum excitation and emission wavelengths of the PARAFAC components in WSOC and MSOC extracted from the three origins.
The EEM components identified by PARAFAC of WSOC (P1–6) and MSOC (C1–6) from the three origins. Em.: emission wavelength. Ex.: excitation wavelength.
The results from the six-component model (abbreviated C1–6) of MSOC identified by PARAFAC, as described in the bottom of Table 1 and Fig. 2, were different from those observed in WSOC, indicating MSOC contained different compound types from WSOC after water extraction. The peak of the C1 component was similar to that of the P3 component of WSOC, but the excitation wavelength was higher than that of the P3 component. The higher excitation wavelength indicated the presence of conjugated unsaturated bond systems shifting towards the high wavelengths of the C1 component (Matos et al., 2015). Moreover, as reported, the C3 component was similar to component 2 of urban alkaline-soluble organic matter (ASOM) collected from the city of Aveiro, Portugal (Matos et al., 2015).
The maximum fluorescence intensity (
The relative contributions of each PARAFAC component of WSOC
The relative intensities of fluorescent components in MSOC exhibited similar
characteristics to WSOC (Fig. 3b). The C1 component was the most intense substance in the case of BB aerosols (
The molecular compositions of WSOC and MSOC extracted from BB and CC aerosols and vehicle emissions were determined by negative ESI-FT-ICR MS. ESI is a soft ionization method, and it can only ionize polar organic compounds and hydrophilic molecules (Wozniak et al., 2008), but nonpolar or less polar compounds such as polycyclic aromatic hydrocarbons and saturated hydrocarbons are not easily ionized by ESI (Lin et al., 2018). In addition, ESI- cannot detect the N-heterocyclic alkaloid compounds (Laskin et al., 2009). Thus, this study mainly discussed these readily ionizable polar organic compounds by ESI-.
Figure 4 showed the reconstructed negative-ion ESI FT-ICR mass spectra of
WSOC for the six selected samples. Lots of peaks with intensive mass
ranges between
Number of formulas in each compound category and the average values
of elemental ratios, molecular weight (MW), double-bond equivalents (DBE),
and modified aromaticity index (AI
In this study, these identified molecular formulas were classified into four
main compound groups based on their compositions: CHO, CHON, CHOS, and
CHONS. CHO compounds refer to the compounds that contain carbon, hydrogen,
oxygen, and the other compound groups that are defined analogously. The
relative abundances of the four compound groups were calculated by the
magnitude of each peak divided by the sum of magnitudes of all identified
peaks and showed in Fig. 4. CHO was the most abundant component in WSOC,
accounting for 43 %–69 % of the total intensities of BB aerosols,
36 %–37 % of CC aerosols, and 36 %–47 % of vehicle emissions,
respectively. CHO values in BB and CC aerosols were lower than those of mass
spectra from simulated combustion experiments (BB of 53 %–72 % and CC of 43 %; Song et al., 2018). Generally,
CHO formulas were consistent with species reported previously as
lignin-pyrolysis products (Fleming et al., 2017), and they detected
this fraction with
Negative ESI FT-ICR mass spectra of WSOC from the six aerosol samples. Different formula groups were color-coded. The six pie charts showed the relative intensities of different formula groups.
Van Krevelen (VK) diagrams are a useful tool that provides a visual graphic
display of compound distribution, and to some extent, are used to qualitatively
identify different composition domains in organic mixtures (Song et al.,
2018; Lv et al., 2016; Smith et al., 2009). In this study, each source showed
similar VK patterns. As shown in Fig. S9,
The mass spectra of MSOC exhibited differences from WSOC (Fig. S10),
especially in BB aerosols and vehicle emissions that exhibited larger
CHO and CHON were the main components in MSOC, accounting for about 90 % of the total intensities (CHO plus CHON). CHO was the most abundant category observed in BB aerosols (78 %–80 %). The elemental compositions observed in CC aerosols were different between bituminous coal and anthracite combustion. The abundance of CHON in anthracite combustion was higher (73 %), while the CHO in bituminous combustion was higher (60 %), which was consistent with the corresponding WSOC. It might be due to a higher N content and lower O content of anthracite than that of bituminous coal (see Table S2). However, CHON in BB aerosols (18 %–20 %) exhibited lower abundances than those in CC aerosols and vehicle emissions. S-containing compounds were not abundant in MSOC. It may be due to the combination of an S element and an O atom showing higher polarity.
Figure S11 showed the VK diagram of MSOC in the six aerosol samples. More
formulas in BB aerosols exhibited two distinct groups with
Tables 2 and S5 presented the relative abundance of weighted molecular weight
(MW
Figure S12 showed the fraction of AI
For MSOC, the aromatic structure fractions in non-S-containing compounds were higher than those in S-containing compounds, and the aromatic structure fractions in CC aerosols were higher than those in BB aerosols and vehicle emissions (Fig. S13), which was consistent with WSOC. Furthermore, we found that the fraction of the aliphatic structure in MSOC was higher than that in WSOC, indicating more fat-like compounds.
Figure S14 shows the plotted Venn diagram of formulas in WSOC in the six aerosol
samples for determining the unique elements in the mass spectra. The
previous study identified the unique elements of water-soluble HULIS in
simulated BB and CC smokes, which presented different molecular
characteristics between biomasses, as well as between biomass and coal
(Song et al., 2018). In this study, we
combined more formulas of different sources to determine the unique
molecules, and more limitations were set, which would provide more identified
characteristics for each source. A total of 212 molecular formulas were detected
simultaneously in the six aerosol samples. It is worth noting that without any
further information, it is not possible to decide whether these common
formulas represent the same compounds. Of 212, there were 112 unique
molecules of CHO and 98 of CHON but only 2 of CHOS. CHO compounds
were relatively small aromatic compounds with 8–20 C atoms and 3–8 O
atoms with DBE values of 5–13 and multiple acidic polar functional groups (Fig. S15). It is noting that lines in Fig. S15 indicate DBE reference values of
linear conjugated polyenes
There were more observed unique peaks of WSOC in BB aerosols (total of 1947)
compared to CC aerosols (1583) and vehicle emissions (813). However, only
143 and 83 molecular were identified in bituminous CC and vehicle exhaust
particles, respectively. Among the observed compounds, 1353 and 1440 unique
molecular formulas were detected in the combustion of
Figure 6 showed plots of the DBE vs. the number of carbon atoms in the
unique molecular formulas of all aerosol samples. These compounds observed
in BB aerosols were largely CHO and CHON (CHO and CHON of 88 %–93 %)
with C numbers ranging from 6 to 40 and DBE ranging from 0 to 31, with no
regular distribution. S-containing compounds were the important components
in the unique molecular formulas of CC aerosols (CHOS and CHONS of
38 %–75 %) and vehicle emissions (CHOS and CHONS of 41 %–66 %).
However, only 7 %–12 % of the total unique molecular formulas were
observed in BB aerosols. As shown in Fig. 6, the region marked by a blue box
denote the high intensities of compounds in unique formulas of each sample.
The high-intensity compounds detected in
A van Krevelen diagram of WSOC
Compared to WSOC, Fig. S16 showed fewer compounds in common in MSOC for
the six aerosol samples. There were only 44 compounds common to the six
aerosol samples. A total of 26 and 14 of the 44 formulas were CHO and CHON,
respectively, but only 4 of the 44 formulas were S-containing compounds. As
shown in Fig. S17, there were only three compounds
(
DBE vs. C number for unique molecular compounds of WSOC from the
six aerosol samples. Lines indicate DBE reference values of linear
conjugated polyenes
These unique molecules in the VK diagram also showed similar results compared
to WSOC (Fig. 5b), further confirming the special characters in
different sources. Expect for tunnel aerosol (about 50 %), these unique
formulas in the BB aerosols, CC aerosols, and vehicle exhaust particle were
dominated by the CHO and CHON groups (Fig. S18). The high-intensity compounds
were
In the above statements, we discussed the light absorption and fluorescence
properties from aerosols in the three different sources. The light
absorption capacity of WSOC and MSOC was essential to assess the evolution
of BrC, and fluorescence spectra were sensitive to different sources and
could help for the source apportionment of BrC. In addition, we evaluated
the molecular compositions of the three sources. Therefore, understanding
the factors affecting the optical properties of BrC is important. It was
reported that the MAE value in the BB experiments depended largely on burning
conditions (Chen and Bond., 2010), and in the CC
experiments it depended on coal maturity (Li et al.,
2018). Chen et al. (2017b) illustrated that the higher light
absorption capacity was associated with the low- and medium-polarity
fractions that contained aromatic and polar functional groups (O or both O
and N atoms). Sources play an important role in light absorption capacity,
consistent with our current study. The MAE
Before discussing their relationship, we firstly determined the compounds
that were potentially able to absorb light radiation based on the above statement
to reduce the influence of non-absorbing substances (Lin et al., 2018).
Mo et al. (2018) reported that MAE
Relationships between DBE and MW of the potential BrC molecules
and the MAE
Fluorescence spectra can provide more information than UV–vis spectra. A red
shift in the excitation–emission maximum could indicate increased
aromaticity and higher molecular weight (Ghidotti et al., 2017). Field
observations have demonstrated that chromophore components were associated
with chemical structures (Chen et al., 2016a, b; Stubbins
et al., 2014). Chen et al. (2016b) illustrated that the fluorescent
components of HULIS-1 and HULIS-2 were correlated positively with
We conducted comprehensive measurements on light absorption, fluorescence,
and molecular compositions of dissolved BrC derived from smoke particles
during the simulated combustion of biomass and coal as well as vehicle
emission aerosols. We observed that BB and CC aerosols had higher MAE
FT-ICR mass spectra showed the
The data used in this study are available upon request. Please contact Gan Zhang (zhanggan@gig.ac.cn) and Jun Li (junli@gig.ac.cn).
The supplement related to this article is available online at:
JT, GZ, JL, and YC designed the experiment. JT and MC carried out the measurements and analyzed the data. JT, TS, YH, and HJ organized and performed the samplings. JT (Jianhui Tang) and BJ supported the fluorescence and FT-ICR MS instruments. JT wrote the paper. JL, YM, JS, PP, and GZ reviewed and commented on the paper.
The authors declare that they have no conflict of interest.
This is contribution no. IS-2811 from GIGCAS.
This research has been supported by the National Natural Science Foundation of China (grant nos. 41430645 and 41773120), the National Key R&D Program of China (grant no. 2017YFC0212000), the International Partnership Program of Chinese Academy of Sciences (grant no. 132744KYSB20170002), and the Guangdong Foundation for Program of Science and Technology Research (grant no. 2017B030314057).
This paper was edited by Alex Lee and reviewed by two anonymous referees.