ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-19-1115-2019Molecular characterization of organic aerosol in the Himalayas: insight from
ultra-high-resolution mass spectrometryMolecular characterization of organic aerosol in HimalayasAnYanqingXuJianzhongjzxu@lzb.ac.cnFengLinZhangXinghuaLiuYanmeiKangShichangJiangBinLiaoYuhongState Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000,
ChinaState Key Laboratory of Organic Geochemistry, Guangzhou Institute of
Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, ChinaUniversity of Chinese Academy of Sciences (UCAS), Beijing 100049,
ChinaJianzhong Xu (jzxu@lzb.ac.cn)29January2019192111511287July201820August201828November20188January2019This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/19/1115/2019/acp-19-1115-2019.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/19/1115/2019/acp-19-1115-2019.pdf
An increased trend in aerosol concentration has been observed in the
Himalayas in recent years, but the understanding of the chemical composition
and sources of aerosol remains poorly understood. In this study, molecular
chemical composition of water-soluble organic matter (WSOM) from two filter
samples collected during two high aerosol loading periods (denoted as P1 and
P2) at a high-altitude station (Qomolangma Station, QOMS;
4276 m a.s.l.) in the northern Himalayas was identified using
electrospray ionization Fourier transform ion cyclotron resonance mass
spectrometry (ESI-FTICR MS). More than 4000 molecular formulas were
identified in each filter sample which were classified into two compound
groups (CHO and CHON) based on their elemental composition, with both
accounting for nearly equal contributions in number (45 %–55 %).
The relative abundance weighted mole ratio of O/Cw for P1 and P2
was 0.43 and 0.39, respectively, and the weighted double bond equivalents
(DBEw), an index for the saturation of organic molecules, were 7.12 and
7.87, respectively. Although the O/Cw mole ratio was comparable for
CHO and CHON compounds, the DBEw was significantly higher in CHON
compounds than CHO compounds. More than 50 % molecular formulas in the Van
Krevelen (VK) diagram (H/C vs. O/C) were located in 1–1.5 (H/C) and 0.2–0.6
(O/C) regions, suggesting potential lignin-like compounds. The distributions
of CHO and CHON compounds in the VK diagram, DBE vs. number of C atoms, and other
diagnostic diagrams showed high similarities among each other, suggesting
their similar source and/or atmospheric processes. Many formulas formed from
biogenic volatile organic compounds (e.g., ozonolysis of α-pinene
products) and biomass-burning-emitted compounds (e.g., phenolic compounds)
were found in the WSOM, suggesting the important contribution of these two
sources in the Himalayas. The high DBE and high fraction of nitrogen-containing aerosol can potentially impact aerosol light absorption in this
remote region. Further comprehensive study is needed due to the complexity of
organic aerosol and limited molecular number identified in this study.
Introduction
Relatively high aerosol concentration events have been frequently observed
over the Himalayas during the pre-monsoon period (March to June)
(Bonasoni et al., 2010). The
aerosol plumes are originated from the southern regions of the Himalayas such
as northwestern India and/or the Indo-Gangetic Plain based on air mass back
trajectory analysis and satellite observation (Liu et al., 2008; Lu et
al., 2012; Lüthi et al., 2015). Due to increased consumption on fuels
(including biofuels and fossil fuels) by industry and residents in recent
decades, air pollution has been a serious issue in south Asia
(Gustafsson et al., 2009). Under favorable atmospheric
circulation, air pollutants emitted or formed in these regions can be
transported fast to the Himalayas and Tibetan Plateau (HTP; Xia et
al., 2011).
Enhanced aerosol concentration for the remote region of the HTP is thought
to have many negative climate and environment effects. For example, the
transported aerosol could heat the air in the higher layer of the troposphere
over the HTP and impact the monsoon system of south Asia and accelerate
the melting of glaciers in the Himalayas (Lau et al., 2006; Ramanathan et
al., 2007). This heating effect is predominantly from light-absorbing
particular aerosol (LAPA) such as black carbon (BC) and brown carbon which
are types of organic aerosol (OA; Ram et al., 2010; Zhang et al., 2015, 2017). BC is from incomplete combustion and dominates the
absorption of LAPA; brown carbon can originate from primary emission and/or
secondary processes, and has made an increasing contribution (up to
∼20 %) to light absorption in recent years
(Laskin et al., 2015, and reference therein). Due to the light
absorption of brown carbon being strongly dependent on their molecular
structure, light-absorbing compounds at a molecular level have been explored during
recent years, and it was found that nitrogen-containing compositions are important
brown carbon compounds (Lin et al., 2016, 2017). Many studies
show that biomass burning is an important source of brown carbon (e.g.,
Saleh et al., 2014; Washenfelder et al., 2015), which is very common in
developing regions in the southern Himalayas. In comparison with other regions,
high elevation and mixed biomass fuels in the southern Himalayas could make
the evolution and chemical composition of OA from biomass burning emission
more complicated (Stockwell et al., 2016; Fleming et al., 2018;
Jayarathne et al., 2018).
The details on the molecular composition of OA are important for
understanding the sources and chemical evolution of OA (Laskin
et al., 2018). Previous studies conducted in the HTP have focused on a
limited number of molecular markers such as organic acids, which are closely
related to biomass burning emission (Cong et al., 2015), and
some toxicology species such as polycyclic aromatic hydrocarbons (PAHs) and
persistent organic pollutants (POPs), which are related to anthropogenic
activities (Wang et al., 2015, 2016). In addition, online
measurement using the Aerodyne high-resolution time-of-flight aerosol mass
spectrometer (HR-ToF-AMS) has provided more details on the chemistry and
sources of OA with a high time resolution (Xu et al., 2018).
However, different instruments have limitations on OA detection, and
an ultra-high mass resolution of mass spectrometry which can identify many
molecular formulas is lacking.
Fourier transform-ion cyclotron resonance mass spectrometry (FTICR MS)
coupled with a soft ionization source, such as electrospray ionization (ESI),
can be used to identify the individual molecular formula of extremely
complex mixtures because of its ultra-high resolution and mass accuracy
(Mazzoleni et al., 2010). Similar methods have been used
for the identification of components in aqueous secondary OA (SOA) and in
ambient samples, and allow the identification and separation of thousands of
compounds in a sample (e.g., Mazzoleni et al., 2010; Altieri et al.,
2012; Mead et al., 2013). Types of methods such as double bond equivalents
(DBEs), elemental ratios, and Kendrick mass defects (KMDs) can be applied to
deduce the chemical characterization of obtained molecular formulas. In this study,
we focus on the molecular composition of water-soluble organic compound in
fine particle aerosol in the Himalayas using ESI-FTICR MS and evaluate the
sources, chemical processing, and potential impact of aerosol in this
region.
MethodologyAerosol sampling
A field study was conducted at the Qomolangma Station (QOMS; 28.36∘ N,
86.95∘ E; 4276 m a.s.l.) located at the toe of Mt. Qomolangma
from 12 April to 12 May 2016 using a suite of online instruments
(X. Zhang et al., 2018), including a HR-ToF-AMS (Aerodyne
Research Inc., Billerica, MA, USA) for 5 min size-resolved chemical
compositions (organics, sulfate, nitrate, ammonium, and chloride) of
non-refractory submicron particulate matter (NR-PM1) and a
photoacoustic extinctiometer (PAX; DMT Inc., Boulder, CO, USA) for BC mass
concentration. In addition, a low-volume (16.7 Lmin-1) particular
matter (PM) sampler (BGI, USA, model PQ 200) with an Aerodyne diameter
cutoff of 2.5 µm at the inlet was used to collect PM2.5 filter
samples on pre-baked quartz fiber filters (47 mm, Pall Life Science, NY,
USA). Due to the low aerosol loading at this remote region, a 2-day sampling strategy was adapted for each filter
collection from 08:00 to 07:45 the following day (local time). A total of 18 filter
samples were collected during the field study with three procedure blanks
which were used to assess potential contamination during sampling and
transportation. The sampling air volume ranged from 35.1 to 48.1 m3 at
ambient conditions. Two samples collected during 25–27 April (P1) and 29 April–1 May (P2), respectively, were used in this study due to the
relatively higher aerosol loading based on HR-ToF-AMS results (Sect. 3.1)
and distinct particulate matter on the filter. One procedure blank was also
adopted in this study like that of aerosol samples to subtract the
potential background.
Chemical analysis
For FTICR MS analysis, filter samples were extracted in 20 mL Milli-Q water
in an ultrasonic bath for 30 min and filtered using 0.45 µm pore-size
Acrodisc syringe filters to remove water-insoluble matter (Pall Science,
USA). The sample tubes were immersed in the mixture of ice–water during
ultrasonic extraction to prevent potential chemical reaction. Prior to FTICR
MS analysis, the extraction was concentrated and purified using PPL
(Agilent Bond Elut-PPL cartridges, 500 mg, 6 mL) solid-phase extraction
(SPE) cartridges for water-soluble organic matter (WSOM) to avoid possible
ESI artifacts. PPL cartridges generally have the best properties for WSOM
enrichment for subsequent FTICR MS analysis (Raeke et al., 2016). In
addition, we control the concentration of SPE effluent to be ∼0.2mgmL-1, which was not too concentrated for artifact adducts. Note that
through SPE cartridges, the most hydrophilic compounds, such as inorganic
ions, and low-molecular-weight organic molecules, such as organic acids and
sugars, were removed, whereas the relatively hydrophobic fraction was
retained. Details on the SPE method using PPL cartridges and analysis using FT-ICR MS can be found in our previous paper (Feng et al.,
2016). Briefly, the mass spectrometry analyses of these samples were
performed using a SolariX XR FTICR MS (Bruker Daltonik GmbH, Bremen,
Germany) equipped with a 9.4 T refrigerated actively shielded
superconducting magnet (Bruker Biospin, Wissembourg, France) and a Paracell
analyzer cell. The samples were ionized in positive ion modes using the ESI
ion source (Bruker Daltonik GmbH, Bremen, Germany). A typical mass-resolving
power of >400000 was achieved at m/z 400, with an absolute mass
error of <0.5ppm. The ions detected in filter blank were
subtracted and molecular formulas in the samples were assigned to all ions
with signal-to-noise ratios of greater than 10, with a mass tolerance of
±1.5ppm, using custom software. Molecular formulas with their maximum
numbers of atoms were defined as 30 12C, 60 1H, 20 16O, 3
14N, 1 32S, 1 13C, 1 18O, and 1 34S. Identified
formulas with H/C, O/C, N/C, S/C, and DBE/C, ranging 0.3–3.0, 0–3, 0–0.5, 0–0.2, and 0–1, were selected, and formulas containing
isotopomers (i.e., 13C, 18O, or 34S) were not considered.
Compounds were detected as either sodium adducts, [M+Na]+, or
protonated species, [M+H]+. Although ammonium could also readily be
adduct to assist in the formation of positive ions, this possibility in our sample
was because of the low concentration of ammonium (Fig. 1). We report all
detected compounds as neutral species, unless stated otherwise. Note that
the ESI+ mode more easily detects basic functional group compounds,
and the reported organic molecules here are only part of organic aerosol
which are biased ionized in ESI+ mode. In addition, highly functionalized
compounds could be detected in both positive and negative modes
(Lin et al., 2012).
The average mass concentration (mass avg.) and chemical composition
of PM1 during P1 (a) and P2 (b) periods, respectively, measured
by the HR-ToF-AMS and PAX. Note that the compounds of PM1 include sulfate,
nitrate, ammonium, chloride, BC (black carbon), organics, BBOA (biomass-burning-emitted OA), NOA (nitrogen-containing OA), and OOA (oxidized
oxygenated OA).
Data processing
The assigned molecular formulas were examined using the Van Krevelen diagram
(Wu et al., 2004), DBEs, KMD series, and aromatic indices
(AImod). The O/C and H/C ratios were calculated by dividing the number
of O and H atoms, respectively, by the number of C atoms in a formula. DBE
analysis was used to determine the number of rings and double bonds in a
molecule. The DBE was calculated using Eq. (1):
DBE=1+c-h/2+n/2,
where c, h, and n are the numbers of C, H, and N atoms, respectively, in the
formula.
The weighted DBE (DBEw), O/C (O/Cw), and H/C (H/Cw) were
calculated using Eq. (2):
Xw=∑(wi⋅Xi)/∑wi,
where Xi and wi are the parameters above and the relative intensity
(RI) is for each individual formula, i.
The Kendrick mass (KM) and KMD for CH2 series, used to search for
potential oligomeric units (Hughey et al., 2001), were
calculated using Eqs. (3) and (4):
KM=observed mass×14/14.01565,KMD=NM-KM,
where 14 is the nominal mass (NM) of CH2, 14.01565 is the exact mass of
CH2, and NM is KM rounded to the nearest integer.
Furthermore, a second-order mass defect analysis using the base units of
CH2 and H2 was applied following the method described in
Roach et al. (2011), which greatly simplified the visualization
of complex mass spectra.
AImod is a measure of the probable aromaticity of a molecule assuming
that half the O atoms are double bonded and half have only σ bonds
(Koch and Dittmar, 2006). AImod was calculated using
Eq. (5):
AImod=(1+c-0.5o-0.5h-0.5n)/(c-0.5o-n),
where c, o, and h are the numbers of C, O, H, and N atoms in the formula.
AImod ranges from 0 for a purely aliphatic compound to higher values
for compounds with more double bonds and that are more aromatic.
The air mass back trajectory analysis using the HYSPLIT model
(Draxler and Hess, 1998) during (a) P1 and (b) P2. The air mass
trajectories were recovered back to 72 at a 1 h interval from the sampling
site (QOMS) at 1000 m above ground level using a 1∘ resolution
Global Data Assimilation System (GDAS) dataset
(https://ready.arl.noaa.gov/gdas1.php; last access: 10 September 2017). The cluster analysis for these
trajectories was completed based on the directions of the trajectories
(angle distance) and colored according to air pressure (vertical profile).
The firespot observed from MODIS (https://firms.modaps.eosdis.nasa.gov; last access: 10 September 2017) and
the average wind rose plot colored by wind speed (WS) for during each filter
sampling period are also shown. The firespot is sized by fire radiative
power.
Results and discussionsChemical characterization of PM1 during P1 and P2 measured by
the HR-ToF-AMS
The average mass concentration and chemical composition measured by
the HR-ToF-AMS during P1 and P2 periods are shown in Fig. 1. The mass
concentrations of PM1 were 9.2 and 10.6 µgm-3,
respectively, which were at the high range of all filters (1.3–10.6 µgm-3)
because of a continuous long-range transport event at
the QOMS (X. Zhang et al., 2018). Due to our sample processing
error, the mass concentration of filter measured gravimetrically could not
be used, and thus the fractions of PM1 to PM2.5 are not available.
However, most of WSOM in PM2.5 is in the accumulation size mode (less than
1 µm) which could be detected by the HR-ToF-AMS (Zhang et
al., 2005). The chemical composition of PM1 during P1 and P2 was all
dominated by OA (55 % and 57 %), followed by BC (26 % and 22 %),
sulfate (7 % and 8 %), nitrate (5 % and 6 %), and ammonium (5 %
and 6 %). The OA was comprised of biomass-burning-emitted OA (BBOA),
nitrogen-containing OA (NOA), and more oxidized oxygenated OA (MO-OOA)
decomposed using positive matrix factorization (PMF) analysis (Fig. 1). Details on PMF analysis can be found in X. Zhang et al. (2018).
The mass contribution of BBOA was higher during P2 than P1 (32 % vs. 22 %), whereas the contribution of MO-OOA was higher during P1 (24 % vs. 16 %). The mass spectra of OA for these two filter periods were closely
similar to a Poisson correlation efficiency (r) of 0.9. The elemental
ratios of oxygen (O) to carbon (C) of OA were 1.04 and 0.97 for P1 and P2
periods (Improved Ambient method; Canagaratna et al., 2015), respectively, and the ratios of
hydrogen (H) to C were 1.26 and 1.32 accordingly. These suggest that the OA during P2
was relatively less oxidized than that during P1 (t test, p<0.05).
The six category ions (CxHy+, CxHyO2+,
CxHyO1+, CxHyN+,
CxHyOzN+, and HO+) detected by the HR-ToF-AMS for these
two filter periods were all dominated by CxHyO2+,
following by CxHy+, CxHyO1+,
CxHyN+, CxHyOzN+, and HO+. The air
mass trajectory analyses using the hybrid single particle Lagrangian
integrated trajectory (HYSPLIT) model for P1 and P2 periods show air mass
mainly originated from west and southwest of the QOMS across north and
northwest India where there were many firespots during these two periods
(Fig. 2). The air mass during P2 was partly (13 %) transported with a low
wind speed and across a short distance (less than 100 km), and could contain some
fresh OA as illustrated by the higher fraction of BBOA.
Chemical characterization of all molecular assignments detected in
WSOM for P1, P2, and common ions. Relative intensity weighted (“w”) for each data
subset (O/C, H/C, OM/OC, DBE, and DBE/C) is given.
AllCHOCHONP1O/Cw0.430.440.42H/Cw1.361.371.34OM/OCw1.721.701.76DBEw7.126.747.79DBE/Cw0.390.370.43Percentage (%) of aliphatic (AImod=0)18.49.49.0Olefinic (0.5>AImod>0)73.939.734.2Aromatic (AImod≥0.5)7.74.13.5P2O/Cw0.390.390.39H/Cw1.311.321.31OM/OCw1.681.631.73DBEw7.877.388.32DBE/Cw0.420.400.44Percentage (%) of aliphatic (AImod=0)14.76.08.7Olefinic (0.5>AImod>0)75.034.340.7Aromatic (AImod≥0.5)10.34.95.4Common ionsO/Cw0.420.430.42H/Cw1.351.361.33OM/OCw1.721.691.76DBEw7.186.757.79DBE/Cw0.400.380.43Percentage (%) of aliphatic (AImod=0)15.66.78.9Olefinic (0.5>AImod>0)76.539.736.8Aromatic (AImod≥0.5)7.84.33.6
Chemical characterization of the molecular assignments detected in
aerosol samples from selected studies (adapted and modified from Table 3 in
Dzepina et al., 2015). Note that all values
are presented as arithmetic means, which are given for convenience of comparison. The
data for our study are the arithmetic means for P1 and P2.
Sample typeMeasurement siteInstrumentO/CH/COM/OCDBEDBE/CReferenceAerosolRemoteESI(+)-FTICR MS0.39–0.421.30–1.341.68–1.727.71–8.380.41–0.42This studyAerosolFree troposphereESI(-)-FTICR MS0.42–0.461.17–1.281.67–1.739.4–10.70.42–0.47Dzepina et al. (2015)AerosolRemoteESI(-)-FTICR MS0.53±0.21.48±0.31.91±0.36.18±3.0NAMazzoleni et al. (2012)AerosolRuralESI(-)-FTICR MS0.28–0.321.37–1.461.54–1.606.30–7.450.33–0.38Wozniak et al. (2008)AerosolSuburbanESI(-)-FTICR MS0.461.341.855.30.45Lin et al. (2012)AerosolUrbanESI(+)-FTICR MS0.311.34NA8.680.41Choi et al. (2017)AerosolMarin boundary layerESI(-)-FTICR MS0.351.591.674.370.28Schmitt-Kopplin et al. (2012)AerosolMarine boundary layerESI(-)-FTICR MS0.36–0.421.49–1.561.70–1.745.88–6.760.28–0.32Wozniak et al. (2014)Cloud waterRemoteESI(-)-FTICR MS0.61–0.621.462.06–2.086.29–6.300.38Zhao et al. (2013)Cloud waterRuralESI(-)-FTICR MS0.511.47NA6.03NACook et al. (2017)Fog waterRuralESI(-)-FTICR MS0.431.391.775.60.40Mazzoleni et al. (2010)
NA: not available
The chemical characteristics of WSOM from ESI-FTICR MS
A total of 4295 and 4770 molecular formulas were identified by ESI-FTICT MS
over the mass range of 100–700 Da for P1 and P2, respectively. The
identified molecular formulas were grouped into two subgroups based on their
elemental composition, i.e., CHO and CHON, all of which had equally important
contributions (45 %–55 %) in number (Fig. 3). Note that individual
species in the ESI-FTICR MS mass spectra could have many different isomeric
structures, in which case the percentages only reflect the number of unique molecular
formulas in each category. The mass spectra of these two samples were highly
similar in the distributions of molecular formulas (Fig. 3). The average weighted
element ratios of P1 and P2 were 0.43 vs. 0.39 for O/Cw, 1.36 vs. 1.31
for H/Cw, and 1.72 vs. 1.68 for OM/OCw (Table 1), suggesting a
relatively higher oxidation and saturation degree for P1 than P2. These
trends are consistent with the results of the HR-ToF-AMS, although the elemental
ratios are different between them, which is due to the difference in the
detection range of m/z and the ionization efficiency of different mass
spectrometry (ESI vs. electron impact) (Yu et al.,
2016). The elemental ratios of WSOM from ESI-FTICR MS in our study are
similar to those results observed in aerosol samples in remote sites using
ESI-FTICR MS (e.g., 0.35–0.53 for O/C) (Table 2). The O/C and H/C in Van
Krevelen diagrams (Wu et al., 2004) for these two filters and
the subgroup molecular formulas show similar distributions, and are all concentrated in ranges of 1.2–1.8
for H/C and 0.3–0.7 for O/C (Fig. 3), suggesting their similar
aerosol sources and atmospheric processes. The similar distributions for
these two filters are also observed in plots of KMD vs. KM and DBE vs. C
(Fig. 3).
The combination plot for all molecular formulas of P1, P2, and common ions including
the high-resolution mass spectrum, the Van Krevelen diagram, Kendrick mass defects
(KMDs) vs. Kendrick mass (KM), and double bond equivalents (DBEs) vs. number
of carbon atoms.
Structural information for the assigned molecular formulas is inferred from
the DBEw value which was higher for P2 than for P1 (7.87 vs. 7.12)
(Table 1). Compared with other studies, the DBEw values in our filter
are relatively close to the results from biomass burning aerosol and aerosol
samples from remote sites (Table 2) (Dzepina
et al., 2015). The DBEw values for each molecular subgroup were higher
for CHON than for CHO (Table 1), especially for P2 (8.32 vs. 7.38),
suggesting more rings and double bonds in CHON molecular formulas. The AImod,
reflecting the minimum number of carbon–carbon double bonds and rings
(Koch and Dittmar, 2006), was correspondingly higher in P2 as
illustrated by its higher contribution of olefinic (75.0 % vs. 73.9 %
for P2 and P1) and aromatic compounds (10.3 % vs. 7.7 % for P2 and P1)
(Table 1). For aromatic compounds (AImod≥0.5) in P2,
52 % of them were CHON formulas (45 % for P1). Higher DBE and AImod
values in CHON compounds suggest more unsaturated compounds in them which
could contain a certain number of chromophores. The distribution of DBE vs. the carbon number of two filters showed a systematic increase in a concentrated
region and a high similarity with each other. This similarity further
suggests the consistent source and chemical processes for the aerosol of
these two filters.
There were 3955 common molecular formulas between these two filters with the
number contribution of CHO of 50.7 % and CHON of 49.3 %. These common
molecular formulas accounted for 92.1 % (P1) and 82.9 % (P2) of two
filters, respectively. There were 340 unique molecular formulas in P1, with
73 % being CHO compounds, whereas there were 815 unique molecular formulas
in P2, with 65.3 % being CHON compounds. For more confidence in molecule
assignment, we focus on the common molecular formulas detected in these two
samples in the section below. Note that the mass spectrum of common ions was
calculated from the average RI from two mass spectra and normalized to the
highest peak.
The potential sources and formation processesCHO compounds
CHO compounds have been frequently detected in ambient aerosol samples
(Altieri et al., 2009b; Mazzoleni et al., 2010; Lin et al., 2012; Fleming
et al., 2018), which could comprise high molecular weight humic-like
substances (HULIS) or oligomers, and in primary emission or secondary
formation of different aerosol sources (Mazzoleni et al., 2012; Wozniak
et al., 2014; Lin et al., 2016; Cook et al., 2017). In our samples, the
weighted molecular weight of CHO compounds was 361.9, with an average C atom
of 19.3±5.3 per molecule; the most abundant O atoms were located in the
range 5–10, with an average value of 7.8±2.9 per molecule (Fig. 4a and b).
The oxygen distribution is also evidenced by the longest homologous series
in the second-order mass defect analysis (CH2-H2), which were all
O5-O10 compounds (Fig. 4d). The DBE of CHO increased with
carbon number, with a DBEw value of 6.75 (Fig. 3); the
carbon-normalized DBE (DBE/C) was 0.39±0.14. These two values were
close to the results from biomass burning aerosol samples in other studies
and are in a high range of published data (Table 2) (Lin et al., 2012;
Mazzoleni et al., 2012), suggesting relatively high aromaticity in our
samples. The carbon oxidation state (OSC) values (Kroll et al., 2011),
a useful metric for the degree of oxidation of organic species in the
atmosphere, exhibited between -1 and 0 for high number density formulas, with 25 or fewer carbon atoms,
suggesting that they are semi- and low-volatile organic compounds, corresponding to “fresh” (BBOA) and “aged” (LV-OOA) SOA by multistep
oxidation reactions (Fig. 4c).
The molecular information for common CHO compounds. (a) The number
frequency distribution of carbon (nc) and oxygen (no). (b) The 3-D
plot for no, nc, and double bond equivalents (DBEs) colored by their
relative intensity. (c) Scatter plot of carbon-based oxidation state (OSc)
vs. nc colored by the distribution of number of molecules. (d) The
second-order mass defect analysis (MD2 (CH2, H2) vs. MD1
(CH2)) using the base of CH2 and H2. The longest homologous
series are marked with the group number and formula type.
List of identified BVOC products and biomass-burning-emission-related compounds in this study.
Mass (m/z)Molecular formulaRelative intensityCompound typeReferences358.1622C17H26O833.1 %α-Pinene productsKristensen et al., 2013368.1829C19H28O711.5 %186.0887C9H14O426.0 %168.0417C8H8O44.75 %Monoterpene productsClaeys et al. (2007), Kleindienst et al. (2007), H. Zhang et al. (2018)182.0574C9H10O48.12 %198.0523C9H10O511.6 %231.0737C9H13NO612.7 %BVOC oxidant productsLee et al. (2016), H. Zhang et al. (2018)233.0894C9H15NO614.0 %215.1152C10H17NO410.6 %229.0945C10H15NO516.5 %231.1101C10H17NO516.8 %233.1257C10H19NO59.5 %245.0894C10H15NO626.4 %247.1050C10H17NO616.5 %249.1207C10H19NO69.6 %306.1099C16H18O65.5 %Aqueous-phase oxidation of biomass burning aerosolYu et al. (2014, 2016), Sun et al. (2010)246.0886C14H14O427.5 %166.0624C9H10O34.9 %Cow dung and brush wood burningFleming et al., 2018178.0624C10H10O35.7 %190.0624C11H10O37.7 %192.0781C11H12O37.0 %188.0832C12H12O24.3 %216.0781C13H12O312.5 %218.0937C13H14O316.7 %198.0866C13H14O430.5 %248.1043C14H16O432.2 %232.1094C14H16O321.6 %338.0745C14H14N2O85.7 %Biomass burning aerosolSong et al. (2018)341.1105C15H19N1O827.2 %355.1261C16H21N1O829.4 %369.1418C17H23N1O824.0 %243.0890C14H13N1O39.3 %257.1046C15H15N1O39.5 %235.1203C13H17N1O317.0 %
There are several possible sources and chemical formation pathways for high-oxygen-containing CHO compounds. Ozonolysis of α-pinene has been
found to form highly oxygenated molecules with some important products such
as C17H26O8 (m/z 358) and C19H28O7
(m/z 368), which are possible esterification products of cis-pinic
(C9H14O4; m/z 186) and diaterpenylic acid
(C8H14O5; m/z 190) (Kristensen et
al., 2013). The first three formulas were all found in our common CHO
molecules with high relative abundance (Table 3). The appearance of these
formulas together with high relative abundance gave us more confidence in
the products of ozonolysis of α-pinene. Ozone concentration in the
Himalayas during the pre-monsoon period was the highest based on online
measurement at the Nepal Climate Observatory at Pyramid (NCO-P) during
2006–2008 (61±9ppbv) (Cristofanelli et
al., 2010). Biogenic volatile organic compounds could be transported from
the low elevation regions in subtropical India and biogenic secondary
organic aerosol has been found to be an important source in the Himalayas
(Stone et al., 2012). A number of other previously reported
monoterpene oxidation product formulas were also observed in our study
(Table 3) (Claeys et al., 2007; Kleindienst et al., 2007; H. Zhang et al.,
2018). In addition, some biomass burning aerosol markers were also found in
CHO compounds. Sun et al. (2010) and Yu et al. (2014, 2016) observed that aqueous-phase oxidation of lignin produces phenol
(C6H6O), guaiacol (C7H8O2), and syringol
(C8H10O3), yielding a substantial fraction of dimers and higher
oligomers, with key dimer markers identified as C16H18O6 and
C14H14O4. The dimer markers C16H18O6 and
C14H14O4 were also present in our sample with high RI
(5.5 % and 27.5 %). The high relative intensity of these compounds
indicates that fog and cloud processing of phenolic species (biomass burning
aerosol) could be an important mechanism for the production of
low-volatility SOA in the Himalayas. Compounds observed in biomass burning
emission (cow dung and brush wood) during residential cooking in Nepal were
also found in our samples (Fleming et al.,
2018).
The molecular information for common CHON compounds. (a) The number
frequency distribution of carbon (nc) and oxygen (no). (b) The 3-D
plot for no, nc, and double bond equivalents (DBEs) colored by their
relative intensity. (c) Scatter plot of carbon-based oxidation state (OSc)
vs. nc colored by the distribution of number of molecules. (d) The Van
Krevelen diagram by H/C vs. N/C colored by number of oxygen (no). The
different sizes of dot marker in (c) and (d) represent the 1N and 2N compounds.
(a) The number frequency distribution of no for 1N and 2N
compounds and (b) the longest 10 CH2 homologous series compounds in 1N
compounds.
CHON compounds
The frequency distribution for no and nc in CHON formulas was shown
in Fig. 5a, which show peaks between 6 and 10 and 15 and 20, respectively. The
DBE of CHON formulas ranged from 4 to 10, with DBEw being 7.79 (Fig. 5b
and Table 1). In the CHON class, compounds contained one or two nitrogen (1N
or 2N) atoms, with 1N compounds accounting for 70.5 % and 2N for 29.5 %,
respectively. Most (93.6 %) 1N compounds contained more than 3 oxygen
atoms and up to 13 oxygen atoms, whereas about 62.5 % of 2N
compounds contained more than 6 oxygen atoms (Fig. 6a). The average O atoms
contained in each molecular formula were therefore higher for 1N compounds
than 2N compounds (8.1±2.9 vs. 6.3±2.3). The ratios of
O/Cw and OSCw for 1N compounds were accordingly higher than those for 2N compounds (0.42 vs. 0.37 for O/Cw and -0.48 vs. -0.54 for
OSCw, respectively), suggesting a higher oxidation state for 1N compounds
(Fig. 5c). In contrast, the DBEw and AImod values for 2N compounds
were higher than for 1N compounds (Table 1). The higher H/Cw for
2N compounds (Fig. 5d) suggests that 2N compounds could contain many
aromatic N-heterocyclic compounds. For 1N compounds, longer and higher
relative intensity CH2 homologous series compounds were found based on
the Kendrick mass defect plot (Fig. 6b); 1073 of the 1373 detected 1N
compounds can be grouped into 145 homologous groups. The abundant long CH2
homologous series in 1N compounds contained 7–10 O atoms, while that in 2N compounds contained 5–8 O
atoms (Fig. 6).
Many CHON compounds could be identified in ESI+ mode, such as reduced N
functional compounds and nitroaromatic compounds (Altieri et al., 2009a;
Laskin et al., 2009; Lin et al., 2012; O'Brien et al., 2013; Wang et al.,
2017). Laskin et al. (2009) identified the amount of
N-heterocyclic alkaloid compounds from types of fresh biomass burning
aerosol. Lin et al. (2012) and Wang et al. (2017) also identified many
CHON compounds in fresh and aged biomass burning aerosol. Oxygenated organic
nitrogen compounds in ambient aerosol
(Dzepina et al., 2015), rainwater
(Altieri et al., 2009a), and fog water
(Mazzoleni et al., 2010) from biomass-burning-emission-influenced regions were also observed. Lin et al. (2017)
found aged biomass burning aerosol in the present urban oxidants (such as
NOx) could result in a higher fraction of CHON compounds compared to the
fresh biomass burning aerosol. Considering the high influence of biomass
burning emission in the Himalayas (X. Zhang et al., 2018), the CHON compounds
in our study were probably related to biomass burning emissions. Recent
studies have proven that burning of mixed biomass fuels in Nepal could emit
amounts of nitrogen species such as NH3, NOx, HCN, benzene, and
organics, and the emission factors for these species are higher than for wood (Stockwell et al., 2016; Jayarathne et al., 2018). In addition, it
is likely that smoldering burning of bio-fuels in high elevation areas is
also responsible for the presence of many nitrogen-containing compounds in
BBOA (Chen et al., 2010). Nitroaromatic compounds
such as methylnitrocatechols (C7H7NO4) are introduced to be
tracers for biomass burning secondary organic aerosols (Iinuma
et al., 2010). Although the C7H7NO4 formula is not found in our
measurement, C14H14N2O8 was found in our measurement,
which was probably its dimer formula. In addition, the homologous series
compounds in which C7H7NO4 serves as the core molecule were also
found in our samples. Some high relative abundance CHON molecular formulas
identified in a recent paper from biomass burning aerosol were also found in
our measurement (Table 3) (Song et al., 2018).
Besides primary emission and/or secondary formation from biomass burning
emission, nitrogen-containing OA could also be formed through other chemical
processes. For example, biogenic volatile organic compounds (BVOCs) can react
with NO3 radicals or RO2+NO into organic nitrate (Ng et al.,
2017). Although organic nitrate is not favored to be ionized in positive
ESI-MS (Wan and Yu, 2006), organic nitrate formed from BVOC
could be highly functionalized (Lee et al., 2016) and ionized in positive
MS through other alkaline functional groups. Recent studies have shown that
BVOCs, including isoprene (C5H8) and monoterpenes
(C10H16), dominate the organic nitrate formation in the
southeastern United States under the condition of the mixed anthropogenic
NOx and BVOCs (Xu et al., 2014; Lee et al., 2016; H. Zhang et al.,
2018). Several molecular formulas formed from monoterpene and NO3
radicals were found in our study (Table 3) (Lee et al., 2016; H. Zhang et
al., 2018).
Implications and limitations
This study analyzed the WSOM using ESI(+) FTICR MS in fine particulate
aerosol from the Himalayas and found that the molecular compositions of WSOM
were mainly comprised of two group compounds (CHO and CHON) with equally
important contributions. The two compound groups could be originated from
biomass burning emission and BVOC oxidation products because many markers
for these two sources were found in these molecular compounds. All our
compounds had relatively high DBE values, suggesting a potential high light
absorption feature. Due to the relative higher mass concentration and higher
contribution of nitrogen-containing compounds (9 % of PM1) during
these two periods based on HR-ToF-AMS results (X. Zhang et al., 2018), it is
believed that aerosol transported to the Himalayas plays an important role in atmospheric radiative forcing.
Ramanathan and Carmichael (2008) found a distinct warming effect of
light-absorbing aerosol over the Himalayas through estimating aerosol
radiative forcing by BC. However, brown carbon had not been considered
before, which could also be an important light-absorbing aerosol due to its high mass
loading (Laskin et al., 2015). Zhang et al. (2017) estimated the light absorption contribution of brown carbon
from the inland Tibetan Plateau, which was up to ∼13 % of that of BC. The
high DBE and nitrogen-containing OA in our study suggested aerosol in the
Himalayas could also contain amounts of light-absorbing organic matter
because the light absorption properties of organic molecules are closely related to the number of double bonds and rings in the molecule and nitrogen
atoms. Many studies have found that the dominating chemical molecules in
brown carbon were related to nitrogen-containing aerosol
(e.g., Lin et al., 2016). This kind of aerosol
combined with BC could have a higher radiative forcing than before in this
area.
More comprehensive methods are needed in the future for identifying BrC in
the Himalayas due to the chemical complexity of BrC. For example, BrC
extraction is highly dependent on the solvent used, and water-insoluble OA
can contribute higher light absorption than water-soluble OA
(Chen and Bond, 2010). In addition,
Budisulistiorini et al. (2017) found that a number of compounds
can dominate the light absorption of BrC, although they make a minor
contribution to the aerosol mass. Therefore, it is important to know the
exact chromophores of BrC which can be obtained by combining light absorption measurement with a
photodiode (PDA) detector and chemical composition with a high-resolution
mass spectrometer with high
performance liquid chromatography (HPLC-PDA-HRMS system) (Lin et al., 2016). For mass
spectrometry analysis, different ionization sources which are also favorable for
different compounds, such as ESI, only detect a part of polar compounds;
non-polar compounds which could dominate the contribution of BrC can be
measured using atmospheric pressure photo ionization (Lin et al.,
2018). A recent study indicated that over 40 % of the solvent-extractable BrC
light absorption is attributed to water-insoluble, non-polar to semi-polar
compounds such as PAHs and their derivatives (Lin et al., 2018). In contrast,
the polar, water-soluble BrC compounds, which are detected in ESI, account
for less than 30 % of light absorption by BrC (Lin et al.,
2018).
The processed FTICR-MS data, AMS data, and meteorological data in this study are available upon
request from the corresponding author (jzxu@lzb.ac.cn).
YA analyzed the data and wrote the manuscript. JX and SK organized the campaign,
analyzed the data, and wrote the manuscript. LF, BJ, and YuL organized the mass spectrometry analysis. XZ analyzed
AMS data and YaL collected the filter.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Study of ozone, aerosols and radiation over the
Tibetan Plateau (SOAR-TP) (ACP/AMT inter-journal SI)”. It is not associated with a
conference.
Acknowledgements
This research was supported by grants from the National Natural Science
Foundation of China (41771079, 41421061), the Key Laboratory of Cryospheric
Sciences Scientific Research Foundation (SKLCS-ZZ-2018), and the Chinese
Academy of Sciences Hundred Talents Program. The authors thank the QOMS for
logistic support.
Edited by: Wenshou Tian
Reviewed by: three anonymous referees
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