In recent years, the Indian capital city of Delhi has
been impacted by very high levels of air pollution, especially during
winter. Comprehensive knowledge of the composition and sources of the
organic aerosol (OA), which constitutes a substantial fraction of total
particulate mass (PM) in Delhi, is central to formulating effective public
health policies. Previous source apportionment studies in Delhi identified
key sources of primary OA (POA) and showed that secondary OA (SOA) played a
major role but were unable to resolve specific SOA sources. We address the
latter through the first field deployment of an extractive electrospray
ionization time-of-flight mass spectrometer (EESI-TOF) in Delhi, together
with a high-resolution aerosol mass spectrometer (AMS). Measurements were
conducted during the winter of 2018/19, and positive matrix factorization
(PMF) was used separately on AMS and EESI-TOF datasets to apportion the
sources of OA. AMS PMF analysis yielded three primary and two secondary
factors which were attributed to hydrocarbon-like OA (HOA), biomass burning
OA (BBOA-1 and BBOA-2), more oxidized oxygenated OA (MO-OOA), and less oxidized
oxygenated OA (LO-OOA). On average, 40 % of the total OA mass was
apportioned to the secondary factors. The SOA contribution to total OA mass
varied greatly between the daytime (76.8 %, 10:00–16:00 local time (LT)) and
nighttime (31.0 %, 21:00–04:00 LT). The higher chemical
resolution of EESI-TOF data allowed identification of individual SOA
sources. The EESI-TOF PMF analysis in total yielded six factors, two of
which were primary factors (primary biomass burning and cooking-related OA).
The remaining four factors were predominantly of secondary origin: aromatic
SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the
uncertainties in the EESI-TOF ion sensitivities, mass concentrations of
EESI-TOF SOA-dominated factors were related to the total AMS SOA (i.e.
MO-OOA + LO-OOA) by multiple linear regression (MLR). Aromatic SOA was the
major SOA component during the daytime, with a 55.2 % contribution to
total SOA mass (42.4 % contribution to total OA). Its contribution to
total SOA, however, decreased to 25.4 % (7.9 % of total OA) during the
nighttime. This factor was attributed to the oxidation of light aromatic
compounds emitted mostly from traffic. Biogenic SOA accounted for 18.4 %
of total SOA mass (14.2 % of total OA) during the daytime and 36.1 % of
total SOA mass (11.2 % of total OA) during the nighttime. Aged biomass
burning and mixed urban SOA accounted for 15.2 % and 11.0 % of total
SOA mass (11.7 % and 8.5 % of total OA mass), respectively, during the daytime and 15.4 % and 22.9 % of total SOA mass (4.8 % and 7.1 % of total OA mass), respectively, during the nighttime. A simple
dilution–partitioning model was applied on all EESI-TOF factors to estimate
the fraction of observed daytime concentrations resulting from local
photochemical production (SOA) or emissions (POA). Aromatic SOA, aged
biomass burning, and mixed urban SOA were all found to be dominated by local
photochemical production, likely from the oxidation of locally emitted volatile organic compounds (VOCs).
In contrast, biogenic SOA was related to the oxidation of diffuse regional
emissions of isoprene and monoterpenes. The findings of this study show that
in Delhi, the nighttime high concentrations are caused by POA emissions led
by traffic and biomass burning and the daytime OA is dominated by SOA, with
aromatic SOA accounting for the largest fraction. Because aromatic SOA is
possibly more toxic than biogenic SOA and primary OA, its dominance during
the daytime suggests an increased OA toxicity and health-related
consequences for the general public.
Introduction
Atmospheric aerosols are suspensions of tiny solid or liquid particles in
the air, ranging from a few nanometres (nm) to tens of micrometres (µm) in size. Aerosols can affect climate directly by scattering (including
reflection) and absorbing solar radiation, thereby altering the radiative
balance of the earth–atmosphere system, and indirectly by acting as cloud
condensation nuclei (CCN), thereby affecting the number and lifetime of
clouds (Forster et al., 2007). Aerosol particulate matter with
an aerodynamic diameter less than or equal to 2.5 µm (PM2.5)
can easily be deposited deep into human lungs and induce oxidative stress
and inflammation, leading to various cardiovascular and respiratory diseases (Pope et al., 2009; Salvi, 2007;
Shiraiwa et al., 2017). Aerosols can be composed of various species such as
mineral dust and soluble inorganic species such as nitrate, sulfate, ammonium,
and chloride, as well as organic and elemental carbon. It is estimated that
organic aerosols (OAs) can account for 20 % to 90 % of the total fine
particulate mass (Jimenez et
al., 2009). OA is classified as either primary OA (POA), which is directly
emitted into the atmosphere, or secondary OA (SOA), which is produced in the
atmosphere by the oxidation of volatile organic compounds (VOCs) emitted
from anthropogenic or natural processes, producing lower-volatility products
which form new particles or condense onto the pre-existing aerosols. In many
areas, SOA accounts for a substantial portion of total OA mass (Jimenez et al., 2009). However, despite SOA being an
important fraction of total OA and its toxicity (Daellenbach et al., 2020), our understanding of
sources and formation processes of SOA in the atmosphere remains incomplete (Hallquist
et al., 2009; Shrivastava et al., 2017). This limits our ability to
accurately constrain SOA contributions in global climate models and regional
air quality models and impedes efforts to understand SOA health effects.
Delhi, the capital city of India, is a growing megapolis with a population of
about 17 million and a population density of 11 320 persons km-2 as per the most recent census, conducted in 2011 (Planning Department; Government of NCT of Delhi, 2021). It
experiences high levels of air pollution and is amongst the most polluted
cities in the world, with an annual mean PM2.5 concentration of
∼140µg m-3 (World Health Organization, 2018), which
is much higher than the Indian National Ambient Air Quality Standard (NAAQS) of
40 µg m-3 for annual PM2.5 concentration (Central Pollution Control Board, 2009). During the
post-monsoon season, severe air pollution events are frequent, with
PM2.5 levels often reaching as high as 1000 µg m-3 (Sembhi et al., 2020). Several recent studies have
investigated the composition and sources of non-refractory (NR) OA in Delhi
using highly time-resolved online measurements by an aerosol mass
spectrometer (AMS) or an aerosol chemical speciation monitor (ACSM) (Bhandari
et al., 2020; Gani et al., 2019; Lalchandani et al., 2021; Tobler et al.,
2020). These studies were able to quantitatively resolve the most dominant
POA sources, i.e. traffic-related, hydrocarbon-like organic aerosol (HOA)
and biomass burning organic aerosol (BBOA) (Bhandari
et al., 2020; Lalchandani et al., 2021; Tobler et al., 2020). However, they
were not able to assign specific sources to the oxygenated organic aerosol
(OOA), due to the use of thermal volatilization (∼600∘C) in combination with harsh electron impact ionization (EI, ∼70 eV) for ion generation in the AMS and ACSM, which results in substantial fragmentation of analyte molecules and loss of molecular information.
Generally, while AMS and ACSM datasets provide quantitative estimates of
individual POA factors and total SOA contribution, they are able to describe
SOA only in terms of bulk descriptors such as the level of oxygenation (i.e.
bulk O:C ratio).
To overcome these limitations on fragmentation and thermal decomposition,
several offline continuous and semi-continuous instruments have been
developed. The offline techniques provide a high degree of chemically
specific information with the possibility of molecular identification as
well. They, however, have low time resolution (typically hours to
∼1 d) and include possible artefacts from reactions or
partitioning on the surface (Pospisilova et al., 2020; Zhao et al.,
2018). Among the continuous and semi-continuous techniques, for example, the
chemical analysis of aerosol online particle inlet coupled to a proton
transfer reaction time-of-flight mass spectrometer (CHARON-PTR-MS) employs
an aerodynamic particle lens for sampling and a thermal desorption unit for
volatilization of aerosol constituents, which are then ionized using a
proton transfer reaction (PTR) ionization scheme and analysed by a mass
spectrometer. Although a proton transfer reaction mass spectrometer (PTR-MS) has a softer ionization scheme than EI, the
energy remains high enough to yield substantial fragmentation of organic
analytes (Eichler et al., 2015; Müller et al.,
2017). The Filter Inlet for Gases and AEROsols (FIGAERO) coupled to a
high-resolution time-of-flight chemical ionization mass spectrometer
(HR-TOF-CIMS) is a semi-continuous system where aerosol particles are first
collected onto a polytetrafluoroethylene filter. The particle-laden filter
is then analysed periodically by passing heated ultra-high-purity nitrogen gas
through the filter. The resulting vapours are ionized by chemical ionization
mass spectrometry, e.g. with iodide adducts (Lopez-Hilfiker
et al., 2014). Although FIGAERO uses a soft ionization technique, some
compounds are still affected by thermal decomposition (Stark et al., 2017) and reactions of analytes on the
filter (Kristensen et al., 2016). Moreover, it has a lower
time resolution (>30 min) compared to online techniques (≤ 1 min).
Finally, a novel extractive electrospray ionization (EESI) interface was
coupled to a portable high-resolution time-of-flight mass spectrometer
(EESI-TOF) (Lopez-Hilfiker et al., 2019). The EESI-TOF
enables highly time-resolved measurements of a wide range of
atmospherically relevant oxygenated compounds, including sugars, alcohols,
acids, and organonitrates (Lopez-Hilfiker et al.,
2019; Stefenelli et al., 2019) with detection limits on the order of 1–10 ng m-3. The EESI-TOF detection limits are sufficient to measure these
compounds with a 5 s time resolution under typical ambient conditions with
negligible thermal decomposition, ionization-induced fragmentation, or
matrix effects. The EESI-TOF provides the near-molecular-level information
(i.e. molecular formula) with a lack of direct structural information. This
is a clear limitation of 1-D mass spectrometric techniques such as the EESI-TOF, AMS, and
CHARON-PTR, as opposed to the chromatographic separation and tandem MS
approaches possible in the offline analysis. In addition to that, different
molecules exhibit different relative sensitivities in systems like the
EESI-TOF (Lopez-Hilfiker et al., 2019; Wang et al.,
2021). Recent field studies in Europe and China have demonstrated the
advantage of the chemical resolution of the EESI-TOF for source apportionment of
ambient OA (Qi et al., 2019; Stefenelli et
al., 2019; Tong et al., 2021).
Here, we deployed an AMS and an EESI-TOF for 2 weeks in Delhi and report
comprehensive source apportionment results from the AMS and EESI-TOF
datasets with a time resolution of ∼10 min. We utilized the
quantitative power of the AMS and the higher chemical resolution of the EESI-TOF
to derive quantitative estimates of individual sources of SOA and report the
results from the first-ever deployment of the EESI-TOF in Delhi. Having a
quantitative estimate of individual SOA contributing factors is a valuable
advancement in understanding and predicting the SOA health effects and its
formation mechanisms and devising effective mitigation policies.
MethodologyCampaign overview and sampling site
To understand and analyse the chemical composition and sources of various
components of submicron PM in Delhi, we conducted a wintertime campaign in
South Delhi at the Indian Institute of Technology Delhi (IITD) campus
(28.54∘ N, 77.19∘ E) from 31 December 2018 to 14 January
2019. A suite of particle- and gas-phase instrumentation was deployed which
included an extractive electrospray ionization time-of-flight mass
spectrometer (EESI-TOF) for conducting time-resolved measurements of the
organic aerosol molecular ions; a high-resolution aerosol mass spectrometer
(HR-AMS) for measuring non-refractory PM1 (NR-PM1) composition; a scanning
mobility particle sizer (SMPS), consisting of a model 3080 differential mobility analyser (DMA) and model 3022 condensation particle counter (CPC) (TSI, Inc., Shoreview, MN, USA) to measure the particle size
distribution from 15.7 to 850.5 nm; an Aethalometer (model AE33, Magee
Scientific, Ljubljana, Slovenia) to measure the equivalent black carbon
(eBC) concentration; and an Xact 625i Ambient Metals Monitor (Cooper
Environmental Services LLC, Tigard, OR, USA) to measure the mass of 35
different elements in PM10 and PM2.5 separately (Rai
et al., 2020).
All instruments were housed in a temperature-controlled laboratory (22 ∘C) on the top floor of a four-storey building (∼12 m high) housing other laboratories and faculty offices. Aerosol sampling
was performed through stainless-steel tubing (6 mm i.d., 8 mm o.d.) of ∼3 m
length. A PM2.5 cyclone (BGI, Mesa Labs, Inc.) was installed at the
inlet of the sampling line to remove larger particles. Here, we have used
the co-located measurements by the HR-AMS and the EESI-TOF with the
supporting data from other instruments as required. More details on the
operation of the EESI-TOF and the HR-AMS are given in the following
sections.
The sampling site, i.e. IITD's location, is representative of the Delhi urban
area. The nearest source of local emissions is an arterial road located
∼150 m away from the building, and there are also emissions from residential use of
solid fuels for cooking and heating and biomass burning in the nearby areas.
A detailed description of this site's location as well as its demographic
and geographic details is provided elsewhere (Lalchandani
et al., 2021; Rai et al., 2020; Singh et al., 2021; Wang et al., 2020).
InstrumentationExtractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF)
The use of the EESI-TOF allows continuous and highly time-resolved measurements
of organic aerosol composition on a near-molecular level (i.e. chemical
formulae of molecular ions) with negligible thermal decomposition or
ionization-induced fragmentation. A detailed description of the EESI-TOF and
its operating principles has been provided elsewhere (Lopez-Hilfiker et al., 2019). Briefly, the sampled aerosols
first pass through a multi-channel charcoal denuder to strip gaseous
components. Breakthrough of gas-phase species can cause high background
signals, as previously observed in Beijing (Tong et al.,
2021). In that study, a small denuder (diameter of 4 mm and length of 30 to
40 mm) was used, whereas in the present study we used a larger denuder
(i.e. with 69 channels, outer diameter 8.5 mm, length 60 mm). The denuder
was exchanged every 48 h and regenerated by baking at ∼200∘C for
12 h. The denuder was positioned ∼20 cm upstream of the
electrospray and mass spectrometer inlet to avoid decreased transmission of
larger particles (Tong et al., 2021). After passing through
the denuder, the sampled particles intersect with a plume of electrospray
(ES) droplets generated by a commercial electrospray probe and delivered
through a fused silica capillary with precut tips with an inner diameter of
50 µm (BGB Analytik AG). The ES solution used in this study consisted
of a 1:1 water / acetonitrile (v/v) mixture doped with 100 ppm NaI. The ES
solution was charged by applying a high potential difference
(∼2.5–2.7 kV) at the ES capillary tip. The flow of ES
solution through the silica capillary is controlled by a high-accuracy
microfluidic flow controller (Fluigent GmbH). Upon intersection with ES
droplets, the soluble fraction of analyte aerosol is extracted into the
droplets. The analyte-laden droplets then pass through a heated
stainless-steel capillary (∼250∘C), wherein the
electrospray solvent evaporates, and analyte ions are generated. Due to the
short residence time (∼1 ms) in the capillary, heat transfer
to the particles is limited, and negligible thermal decomposition is
observed. Finally, the ions are analysed by a high-resolution
long-time-of-flight mass spectrometer (LTOF-MS, Tofwerk AG, Switzerland)
configured for positive ion detection. In the configuration of the mass
spectrometer and ionization scheme used in this study, one can detect a wide
range of molecules present in the organic aerosols, including sugars,
alcohols, acids, and organonitrates. The detected molecular classes include
nearly all the compounds present in the secondary organic aerosol (SOA),
with notable exceptions of organosulfates, which are typically detected as
negative ions, and non-oxygenated species such as alkanes and alkenes. The
mass resolution (M/ΔM) achieved by the mass analyser in this study was ∼8000.
This resolution is enough to separate isobars (compounds with same nominal
mass) and determine the molecular formula. Due to the lack of direct
structural information, the molecular assignments given here are, however,
tentative and may include contributions from multiple isomers. The ions were
observed predominantly as adducts with Na+, i.e. [M]Na+, but for
simplicity are denoted herein using the neutral formula (M). For example, an
ion observed as [C6H10O5]Na+ is reported as
C6H10O5.
The EESI-TOF sampled continuously at a flow rate of ∼1 L min-1, alternating between direct ambient sampling (5 min) and sampling
through a particle filter (3 min) for measurement of the instrument
background. This measurement–filter cycle is shorter than those used in
previous ambient measurements to avoid clogging of the EESI capillary and to
increase the spray stability. The EESI-TOF data analysis was
performed using Tofware version 2.5.7 (Tofwerk AG, Switzerland). The
original data were acquired at a time resolution of 1 Hz, and high-resolution
(HR) peak fitting was applied to data averaged to 10 s for the
mass-to-charge ratio (m/z) range 145–350. In total, 1030 ion formulae were
fitted. The ambient aerosol composition (Mdiff) was calculated
by subtracting background spectra obtained during particle filter sampling
(Mfilter) from the mass spectra obtained during direct ambient
sampling (Mtotal). The background spectra
(Mfilter) were calculated by interpolating the average between
adjacent background spectra (i.e. particle filter measurements before and
after an ambient sampling period). This methodology is similar to the one
reported in previous EESI-TOF studies (Qi et
al., 2019; Stefenelli et al., 2019; Tong et al., 2021). After obtaining the
ambient aerosol composition, the ambient aerosol spectra
(Mdiff) were averaged to 10 min for further processing.
The error matrix (σdiff) corresponding to
Mdiff values was calculated from Poisson ion counting
statistics (Allan et al., 2003) from
ambient sampling σtotal(i,j) and filter sampling periods
σfilter(i,j), added in quadrature as follows:
σdiffi,j=σtotal2i,j+σfilter2i,j.
As a final step, data were filtered to remove ions whose total signal and/or
variability was either dominated by the background or too noisy for
meaningful interpretation. Ions that met both of the following criteria were
accepted for further analysis:
The ratio of signal to uncertainty, i.e. Mdiff/σdiff, was considered, where σdiff represents the
precision-based uncertainties as calculated by using Eq. (1). Ions with a
median Mdiff/σdiff<0.2 were removed from further analysis (Paatero and Hopke, 2003).
The ratio of signal to background, i.e. Mdiff/Mfilter, was considered. This identifies ions whose time series is dominated
by instabilities in the spray and/or background drifts due to
adsorption/desorption of semi-volatile compounds. Ions with a median ratio of
Mdiff/Mfilter<0.1 were
removed.
In the end, 641 ions between m/z ranges of 150–350 were retained for further
analysis.
High-resolution time-of-flight aerosol mass spectrometer
(HR-AMS)
The HR-AMS (Aerodyne Research Inc., Billerica, MA, USA) was equipped with a
PM1 aerodynamic lens and measured the composition of NR-PM1. A
detailed description is given elsewhere (DeCarlo et al.,
2006; Canagaratna et al., 2007).
Briefly, ambient air is sampled continuously through a critical orifice into
a PM1 aerodynamic lens, which focuses the particles into a narrow beam
and accelerates them to a velocity that is inversely related to their vacuum
aerodynamic diameter (Williams et al., 2013). The particles
then impact a resistively heated surface (∼600 ∘C) and
flash vaporize. The resulting gas is ionized by electron impact ionization (EI, 70 eV) and detected by a time-of-flight mass spectrometer. The detected ion
rate measured at a specific mass-to-charge ratio (m/z) is then converted to
mass concentration in µg m-3 (Jimenez et al., 2003).
In this study, two ionization efficiency (IE) calibrations of the instrument
were performed (one before the commencement of the campaign and the other at
the end) using 300 nm NH4NO3 particles. More details on the
instrument operation during this campaign have been reported elsewhere (Singh
et al., 2021, 2019). The instrument was operated in V mode at a time resolution of
2 min. Every 30 s, it switched between mass spectra (MS) and
particle time-of-flight (PToF) mode, completing two cycles within each
integration period. The mass spectra of an ensemble of particles are
measured in MS mode, whereas in the PToF mode, the particle beam is
modulated by a chopper spinning at 130 Hz, resulting in the size-resolved
mass spectra. Unit mass resolution (UMR) data were analysed using the
SQUIRREL data analysis toolkit (version 1.59) programmed in the Igor Pro
6.37 software environment (WaveMetrics, Inc., Portland, OR, USA).
High-resolution peak fitting analysis was conducted using PIKA (version
1.19) (DeCarlo et al., 2006) for m/z 12 to 120. The collection
efficiency was estimated using the composition-dependent algorithm of Middlebrook et al. (2012) implemented in SQUIRREL. The Pieber correction was applied according to the method recommended by
Pieber et al. (2016).
Source apportionmentPositive matrix factorization
Source apportionment was performed separately on the AMS OA and EESI-TOF
datasets using the positive matrix factorization (PMF) algorithm (Paatero and Tapper, 1994)
implemented within the multilinear engine (ME-2; Paatero, 1999). In this study, the
Source Finder Professional (SoFi Pro 6.8, Datalystica Ltd.) interface was used for model
configuration and post-analysis (Canonaco et al., 2013). PMF
is a bilinear model that represents the sample matrix X with
dimensions of m×n, representing m measurements of n variables as a product of two
matrices G (dimensions of m×p) and F (dimensions of p×n). The
number of columns in the modelled matrix G and rows in the modelled
matrix F are equal to the number of factors p, i.e. individual
sources chosen to describe the dataset. The PMF model operates under
non-negativity constraints; i.e. negative values are not permitted in
G or F. The PMF model is expressed as
X=G×F+E.
Here G represents the time-dependent factor concentrations (i.e. time series) and F represents the chemical composition (i.e. mass spectrum) of the resolved factors. Model residuals are contained in
E.
The PMF model solves Eq. (2) using a least-squares algorithm that iteratively
minimizes the objective function Q, defined as
Q=∑i=1n∑j=1meijσij2.
In Eq. (3), eij represents elements of the residual matrix and
σij represents the measurement uncertainties
corresponding to the input point xij, where i and j are the
indices representing measurement time and variable (or integer m/z),
respectively. The theoretical value of Q, denoted Qexp, can be estimated
as
Qexp=mn-pm+n.
PMF is subject to rotational ambiguity, meaning that different combinations
of G and F matrices exist that can yield the same or
similar Q values. Some of these combinations may represent environmentally
unreasonable representations of the dataset. To direct the model towards
interpretable rotations, a priori information can be introduced by constraining
selected factor time series or mass spectra using an a-value approach
(Canonaco et al., 2013; Crippa et al., 2014). In
this method, one or more factor profiles and/or time series are constrained
to resemble reference profiles and/or time series, with the scalar a (0≤a≤1) determining the tightness of constraint. As an example, if
constraints are applied to mass spectra, the a value determines the extent to
which a factor mass spectrum in the final solution
(fj,solution) is allowed to deviate from the anchor
mass spectrum (fj) provided to the model as the initial starting point.
fj,solution=fj±a⋅fj
As an example, if an a value of 0.1 is used, all the variables in the
resulting mass spectrum can vary between ± 10 % of the input
constraining the mass spectrum. Note that post-PMF normalization of factor
profiles may cause the final values to slightly exceed the limits defined by
Eq. (5).
Source apportionment of AMS dataset
The AMS OA matrix XAMS consisted of organic ion time series
derived from high-resolution (HR) peak fitting for m/z 12 to 120 and the
integrated signal across integer m/z (unit mass resolution or UMR) for m/z 121 to
300. A total of 507 variables were used in the sample matrix
XAMS, 332 of which had chemical formulae assigned to
them through HR fitting. The remaining 175 variables were UMR species. For
the UMR data, we excluded m/z 149 due to interference from phthalic acid
emitted by the servo housing, as well as m/z 183, 184, and 186 due to the
interference from the tungsten filaments. Uncertainties were calculated
according to the method by Allan et
al. (2003), which accounts for counting statistics of the individual ions as
well as the uncertainty in the detector response to individual ions.
Variables with a signal-to-noise ratio (SNR) <0.2 were
downweighted by a factor of 10, whereas those with an SNR <2 were
downweighted by a factor of 2 (Paatero and
Hopke, 2003). Further, ions calculated from the CO2+ signal (i.e. O+, OH+, H2O+, and CO+) were removed from
XAMS prior to PMF analysis to avoid overweighting
CO2+ intensity (Ulbrich et al., 2009) and were
recalculated from CO2+ during post-analysis. Note that this
remove-and-reinsert strategy is preferable to downweighting of
CO2+-dependent ions as it avoids the potential for small biases
induced by the combination of AMS minimum errors and dynamic downweighting
in “robust mode” operation of the PMF.
As a first step, we ran the PMF in unconstrained mode with the number of
factors ranging from 3 to 8. Each solution was inspected based on its
Q/Qexp value and physical interpretation of individual factors. Large
decreases in the Q/Qexp values were observed when the number of factors
increased from 3 to 5, while small incremental changes were observed when
the number of factors increased beyond 5. Further, solutions with more than
5 factors yielded only additional biomass-burning-related factors, the
differences between which could not be physically interpreted. Hence we
chose a 5-factor solution as the best representation of the data. The
unconstrained PMF resulted in an HOA factor with a high degree of
oxygenation, i.e. O:C ratio ∼ 0.15, which is a factor of ∼3 higher than the HOA factor obtained at the same site in a recent study (Lalchandani
et al., 2021). To obtain a cleaner HOA profile, we took the HOA factor profile
from an unconstrained 8-factor solution and used it in SoFi to constrain the
HOA factor in the final 5-factor solution. We explored the PMF solutions
with higher numbers of factors, but the O:C ratio of the HOA profile did not
show a significant decrease for solutions with more than 8 factors.
The factors obtained from the AMS source apportionment were identified based
on their correlations with external measurements, mass spectral features,
diurnal trends, and relationship to anthropogenic activities as well as
meteorological and environmental conditions (e.g. temperature, expected
trends in human activities). The interpretation of the final 5-factor
solution is discussed in Sect. 3.1.
Source apportionment of EESI-TOF dataset
A total of 641 ion formulae from m/z 140–350 were used in the final PMF input
matrix XEESI of the EESI-TOF data. The initial PMF model was
run without constraints for 6 to 15 factors, and each solution was checked
for the interpretability of the results. The 6-factor solution yielded a
factor identified as primary biomass burning (characterized by
∼90 % of factor profile signal from
C6H10O5, which is likely dominated by levoglucosan, a biomass
burning tracer), and 5 other factors related to primary cooking emissions,
aged biomass burning, and 3 SOA factors (described in Sect. 3.2). Although
the main spectral and temporal features of these factors were not consistent
with primary biomass burning, they nonetheless contained significant signals
from C6H10O5 (comprising 10 %–15 % of the factor
profiles), consistent with mathematical mixing of biomass burning into these
factors. Increasing the number of factors from 6 to 10 decreased the
contribution of C6H10O5 in the aged biomass burning factor to
∼12 %, consistent with similar factors observed in previous
studies (Qi et al., 2019; Tong et al., 2021). For
the non-biomass-burning factors, the contribution of C6H10O5
to the factor profiles decreased to <2.5 %, while key spectral
and temporal features were retained. As the number of factors increased, the
newly added factor profiles all had high (>20 %) contributions
from C6H10O5, which is characteristic of primary biomass
burning. However, these new factors could not be physically interpreted and
were therefore considered to result from mathematical splitting. Increasing
the number of factors to 11–15 yielded only further splitting of the primary
biomass burning profiles and no longer affected the C6H10O5
contributions to the non-primary biomass burning factors.
This preliminary analysis suggested that the variability in the dataset is
optimally represented by 6 factors. However, because the unconstrained
6-factor solution did not provide unmixed factors (as described above), we
constructed an unmixed 6-factor solution by constraining profiles for
primary cooking, aged biomass burning, and the 3 SOA factors. The reference
profiles used in SoFi for these 5 factors were taken from the unconstrained
10-factor solution. The remaining five profiles (from the unconstrained
10-factor solution) were combined on a mass-weighted basis to form a single
primary biomass burning profile. This 6-factor solution is referred to as
the “base case” hereafter.
The statistical stability of and uncertainties in the base case were accessed
by a combined bootstrap analysis–randomized a-value selection (i.e.
sensitivity test of the tightness of constraint). Bootstrapping was
implemented by random resampling of the rows of the original data matrix and
corresponding entries of the error matrix, such that in each bootstrap
iteration some rows were sampled multiple times while others were not
sampled at all, thus creating new matrices in each iteration of the
bootstrap analysis that were of the same dimensions as the original input
matrices (Davison and Hinkley, 1997; Paatero et al.,
2014). Simultaneously the a values of the 5 constrained factors (primary
cooking-related, aged biomass burning, and 3 unique SOA factors) were
randomly selected from within predefined limits chosen to maximize
exploration of the solution space while maintaining computational
efficiency. The bootstrap–a-value analysis was conducted in two stages: (1) an exploratory analysis on a small number of runs that was used to determine
the a-value limits and (2) the final analysis on 1000 bootstrap runs with
a-value randomization occurring within these limits.
The a-value limits for the combined bootstrap–a-value randomization analysis
were selected after an exploratory analysis of 250 bootstrap runs in which
the a values of every constrained factor were allowed to vary over the full
range (0 to 1), with a step size of 0.1. The 250 individual solutions were
analysed and classified as “good” or “mixed” following the method of Stefenelli et al. (2019), which consists of the following
steps: (1) calculation of the Spearman correlation coefficients between the
time series of each factor from the base case and a bootstrap solution,
yielding a correlation matrix for each bootstrap run with the correlation
values between bootstrap factors and corresponding base case factors on the
matrix diagonal; (2) requirement that the correlation coefficient on the
matrix diagonal was higher than those on the intersecting row and column by
a statistically significant margin (based on a preselected significance
level p from a t test). Solutions satisfying this requirement were classified
as good solutions, whereas those failing this test were classified as
mixed solutions. From visual analysis of ∼50 randomly
selected solutions, we selected p=0.3 as the appropriate confidence
level. We then assessed the acceptance probability as a function of
a value, selecting the a-value upper boundary to be the value above which 75 % of solutions were classified as mixed. The ranges of a values selected
for cooking-related OA and 4 SOA factors are given in Supplementary Table S1.
The a-value limits obtained above were utilized in a final combined
bootstrap–a-value randomization analysis, consisting of 1000 runs. Solutions
resulting from this 1000-run bootstrap were separated into good and
mixed solutions using the same acceptance/rejection criteria as used in
the exploratory bootstrap. The final bootstrap analysis resulted in 835
good solutions out of 1000 that were kept for further analysis. The
solution presented in Sect. 3 is the average of these 835 solutions.
Estimation of the fraction attributable to local production or emissions
during daytime
In order to isolate the effects of boundary layer dynamics and gas–particle
partitioning from those of photochemical production, we modelled the average
concentration of all EESI-TOF factors during the daytime (averaged between
10:00 to 16:00 local time (LT)) (denoted Cmodel) based on the average
concentration of the previous night (averaged between 21:00 and 04:00 LT),
assuming that all changes were driven by partitioning and/or boundary layer
expansion. The dilution and partitioning effects on the SOA factors were
calculated by attributing each factor to a distinct organic species with
bulk properties as given in the Supplementary Table S2. The relative
difference between measured (Cmeasured) and modelled average
daytime concentrations (Cmodel) is attributed to local
photochemical production for SOA factors and local emissions for POA
factors. This analysis was applied to each factor on a day-by-day basis.
The modelled daytime concentration of a particular factor on a day i, Cmodel,i, was calculated by combining the effects of both
dilution and partitioning on the average nighttime concentration,
Cnighttime,i-1, of that factor observed during the previous
night (i.e. day i- 1):
Cmodel,i=Cnighttime,i-1×Df,i×Pf,i,
where Df is the dilution factor, i.e. the fractional
change in nighttime concentrations due to dilution, and Pf is the partitioning factor, i.e. the fractional change in the nighttime
concentrations due to gas–particle partitioning.
The dilution factor for each day was calculated using the ratios of
planetary boundary layer heights (PBLHs) during the nighttime and daytime. PBLH data were obtained from the Real-time Environmental Applications and
Display sYstem (READY; Rolph et al., 2017)
website. The PBLH data were available at a 3 h resolution; hence single
values of PBLH obtained at 00:00 during the nighttime and 12:00 during the daytime
were used for each day.
Df,i=PBLHnight,i/PBLHday,i
The partitioning coefficient ξp for each factor p was
calculated using basic partitioning theory:
ξp=1+c∗COA-1,
where COA is the mass concentration of organic aerosols
(measured OA mass by AMS in this study) and c∗ is the effective
saturation vapour concentration of each factor. The activity coefficient was
assumed to be 1 (Donahue et al., 2006). The saturation vapour
concentration at room temperature, c∗ (298 K), was estimated using
the molecular corridor approach (Li et al., 2016), based on
the framework developed originally for the two-dimensional volatility basis
set (Donahue et al., 2011):
log10c∗(298K)=nC0-ncbc-nObO-2nCnOnc+nObCO,
where nc0 is the reference carbon number;
nC and nO are the number of carbon and oxygen atoms, respectively,
which are given in supplementary Table S2; bC and bO are the
corresponding parameterization values for each class of compounds (i.e. CH
and CHO); and bCO is the coefficient of carbon–oxygen non-ideality,
ncnO/(nc+nO), hereafter referred to as NICO. The nC0, bC, bO, and bCO values used were 25,
0.475, 0.2, and 0.9, respectively (Mohr
et al., 2019; Tröstl et al., 2016; Pankow and Asher 2008). The
temperature-dependent effective saturation concentration c∗T was calculated using the Clausius–Clapeyron
equation (Li et al., 2016; Donahue et al.,
2006). ξp was calculated for each factor on an
hourly basis and was later averaged to obtain single daytime and nighttime
values for each day. The partitioning factor Pf was
calculated by using Eq. (9):
Pf,i=ξday,i/ξnighttime,i.
Based on modelled daytime concentration (Cmodel) and
observed daytime concentration (Cmeasured), the fraction of
daytime concentrations attributed to local photochemical production for SOA
factors or to direct emissions for POA factors was calculated using the
following equation:
fraction attributable to local production or emissions=Cmeasured-Cmodel/Cmeasured.
Results and discussionsAMS source apportionment results
From the AMS source apportionment, we identified three primary factors,
namely hydrocarbon-like OA (HOA) and biomass burning OA (BBOA-1 and BBOA-2),
and two secondary factors, denoted more oxidized oxygenated OA (MO-OOA) and
less oxidized oxygenated OA (LO-OOA). Figure 1 shows the factor mass spectra
(Fig. 1a), time series (Fig. 1b), and diurnal trends (Fig. 1c) of all
factors. The relative contributions of these factors to total OA mass on a
24 h basis, during the daytime and during the nighttime, are shown by means of pie
charts in Fig. 1d.
(a) Mass spectra from AMS PMF factors. The mass spectra
are divided into two regions, i.e. one from m/z 12 to 120 (individual ions from
HR peak fitting) and one from 121 to 300 (integer m/z integration). The mass
spectra are coloured according to different families as mentioned in the
legend. (b) Factor time series from AMS PMF results,
together with selected reference species. (c) Diurnal trends with
interquartile ranges (shaded areas) of the AMS factors. These are drawn at
an hourly time resolution. (d) Pie charts showing fractional
contributions of AMS factors as the 24 h average, as well as for daytime
(10:00–16:00 LT) and nighttime (21:00–04:00 LT).
The HOA mass spectrum contains prominent contributions from
CxHy+ fragments (e.g. C3H5+,
C3H7+, C4H7+,
C3H9+). This is consistent with saturated and
unsaturated hydrocarbons, which are major constituents of fossil fuels.
Similar factors have been observed in many previous studies and are
typically associated with traffic emissions (Lanz et al., 2007; Zhang et al.,
2011). The diurnal pattern (Fig. 1c) shows a small peak during the morning
rush hour (07:00 LT) and a larger one during the evening rush hour
(18:00–22:00 LT). The morning peak is partially obscured by the decreasing
concentrations due to dilution caused by a rising boundary layer. As a
result, the HOA factor reaches its minimum during midday hours (12:00–16:00 LT). Such strong boundary layer cycling is a known characteristic of Delhi
and affects nearly all primary species (Gani
et al., 2019; Lalchandani et al., 2021; Tobler et al., 2020). The low
temperatures during the evening hours reduce the boundary layer height,
resulting in an accumulation of species. The HOA time series is well
correlated with NOx, further supporting the assignment of this factor
to traffic-related sources, as shown in Fig. 1b.
The mass spectra of both BBOA-1 and BBOA-2 have strong signals from
C2H4O2+ (m/z 60) and
C3H5O2+ (m/z 73) fragments, which are characteristic
fragments of anhydrosugars like levoglucosan (Aiken et al.,
2009), a product of cellulose pyrolysis (Hoffmann et al., 2010; Simoneit et
al., 1999). The high abundances of these fragments in BBOA mass spectra have
been reported in earlier studies (Crippa et al., 2013; Zhang et
al., 2011). BBOA-1 has about 1.5 % and 0.8 % of its total signal
attributed to C2H4O2+ and
C3H5O2+, respectively, compared to 3.9 % and
1.7 %, respectively, for BBOA-2. All other factors have lower contributions
from these fragments; HOA, MO-OOA, and LO-OOA have 0.3 %, 0.2 %, and
0.8 % of their total signal, respectively, attributed to
C2H4O2+, and 0.2 %, 0.1 % and 0.4 % of their total signal, respectively, attributed to
C3H5O2+. BBOA-1 and BBOA-2 explain 21.6 % and 36.8 % of the temporal variability in C2H4O2+, while
LO-OOA, MO-OOA, and HOA explain 20.3 %, 11.6 %, and 3.1 %, respectively, of its temporal variability. Similarly, for
C3H5O2+, BBOA-1 and BBOA-2 explain 20.9 % and 31.6 % of its temporal variability, respectively, while LO-OOA, MO-OOA, and HOA
explain 20.2 %, 17.8 %, and 4.3 % of its temporal variability,
respectively. The rest is unexplained variability. The BBOA factors
also have higher contributions (relative to other factors) from high m/z
species, e.g. 116, 118, 202, which were previously associated with
polycyclic aromatic hydrocarbons (PAHs) (Bruns et al., 2015; Dzepina
et al., 2007).
The bulk O:C, H:C, and N:C ratios of BBOA-1 are 0.37, 1.8, and 0.05,
respectively, compared to 0.47, 1.84, and 0.019 for BBOA-2. The N:C value is
almost 2.5 times higher for BBOA-1 compared to BBOA-2. A nitrogen-rich
solid-fuel combustion factor was identified in a previous study at the same
site and was attributed to biomass combustion with possible mixing of coal
and other solid fuels (Lalchandani
et al., 2021). Both BBOA-1 and BBOA-2 show similar diurnal trends with an
evening time increase, indicating increased emissions as well as a
reduction in boundary layer height due to decreasing temperature (Gani
et al., 2019; Lalchandani et al., 2021), and very low values during daytime
hours. A steep decline during midday hours of both BBOA-1 and BBOA-2 is
attributed to less intense source contributions and an increase in boundary
layer height as well as the increased volatilization of semi-volatile
components. A contrasting feature in diurnal trends of BBOA-1 and BBOA-2,
however, is the extent to which both these factors increase in the evening hours
as compared to their average daytime values. While BBOA-1 increases by a
factor of ∼2–3 during the evening hours, BBOA-2 increases by a factor of ∼10
during the same time. The differences in bulk elemental ratios and diurnal
patterns of the BBOA factors support their treatment as separate factors.
The primary biomass burning and aged biomass burning factors from the
EESI-TOF (see Sect. 3.2) also show a similar trend, with the diurnal pattern
of the primary biomass burning factor from the EESI-TOF showing an increase of a factor of
∼50 during the evening rush hours, whereas the oxidized biomass burning
factor only exhibits a ∼2–3-fold increase during the same time (Fig. S1 in the Supplement).
From the two retrieved SOA factors, MO-OOA is more oxygenated with a bulk
O:C ratio of 0.99, which is the highest among all the factors (>2
times higher than that of LO-OOA and BBOAs and ∼12 times
higher than HOA). The mass spectrum of MO-OOA contains large contributions
from CO2+(m/z=44), consistent with OOA factors described
in other studies (Ng et al., 2010). The CO2+ fragment usually arises from carboxylic acid groups in diacids or
multifunctional acidic compounds (Duplissy et al., 2011). The
high degree of oxygenation suggests its secondary origin (Jimenez et al., 2009;
Zhang et al., 2011). Despite the aforementioned boundary layer effects, the
diurnal trend of MO-OOA shows an increase during the day, implying formation
occurs as a result of daytime photochemical reactions, although the
sources or precursors cannot be inferred from the AMS factor spectrum. Overall,
MO-OOA correlates well with SO42- measured by the AMS.
LO-OOA contains a lower contribution from CO2+ (though still
higher than any of the POA factors) and higher contributions from less
oxygenated species. The bulk O:C ratio of this factor is 0.46. Due to the
lower oxygenation and presumably higher volatility of LO-OOA, its
partitioning behaviour between the gas and particle phase is more sensitive
to the ambient temperature and the total OA concentration than MO-OOA. As a
result, LO-OOA exhibits increased concentrations at night (lower
temperature, higher total OA).
The PMF analyses on the AMS dataset as discussed above show the relative
importance of primary and secondary sources (Fig. 1d) with traffic and
primary biomass burning as major contributors to the primary organic
aerosol. However, while the AMS can quantify total SOA and delineate it by
the extent of oxygenation and/or volatility, it does not provide
source-specific information. In the next section, we report the source
apportionment results from EESI-TOF data and investigate the individual
sources that could contribute to the SOA factors.
EESI source apportionment results
The EESI-TOF source apportionment results yielded six factors. Of these,
biomass burning and cooking-related OA were attributed to primary aerosol.
The remaining four factors were attributed to secondary sources and denoted
aromatic SOA, biogenic SOA, aged biomass burning, and mixed urban SOA. These
EESI-TOF factors can be qualitatively related to the AMS, with EESI-TOF
primary biomass burning corresponding to AMS BBOA and the four EESI-TOF
secondary factors providing a more source-specific representation of AMS OOA
(i.e. MO-OOA + LO-OOA). Note that cooking-related OA was retrieved only by
the EESI-TOF and not the AMS (see discussion below), while as expected the
EESI-TOF did not retrieve HOA due to its insensitivity to alkanes and
alkenes (see Sect. 2.2.1). Related AMS and EESI-TOF factors are compared
below as appropriate.
Primary factorsPrimary biomass burning
The mass spectrum of primary biomass burning is dominated by
C6H10O5, likely associated with anhydrosugars such as
levoglucosan, mannosan, and galactosan. C6H10O5 constitutes
81.1 % of the total mass spectral signal in this factor (Fig. 2a). The
second-highest contribution to the mass spectrum comes from the ion
C8H12O6 (2.1 % of the total signal), which could
possibly be a derivative of syringol, a prominent compound found in
wood-burning smoke (Yee et al., 2013). These features are
qualitatively similar to primary biomass burning mass spectra observed by
EESI-TOFs in previous studies (Qi et al.,
2019; Stefenelli et al., 2019; Tong et al., 2021). The next three highest
contributing ions with 0.47 %, 0.44 %, and 0.33 % of total signal
are C11H14O4, C6H12O5, and
C6H10O4, respectively. C11H14O4 could be
tentatively assigned to syringyl ethanone, whereas C6H10O4
may be associated with methylglutaric acid. Both of these compounds have
previously been found in biomass burning smoke (Bertrand et al., 2018; Qi et al., 2019).
(a) Factor mass spectra of EESI-TOF PMF analysis. The
green-to-blue colour gradient represents CxHyOz compounds
classified by their H:C ratio as mentioned in the legend, while red denotes
CxHyOzN1-2 compounds. (b) Factor time
series, together with selected external species for comparison.
(c) Diurnal variations in the EESI-TOF factors with an hourly
resolution. Shaded areas show interquartile ranges.
The time series of the primary biomass burning factor observed by the
EESI-TOF (Fig. 2b) correlates strongly with the summed time series of the
two BBOA factors from AMS (r=0.85). The diurnal trend of this factor shows
a distinct peak during the evening rush hours between 18:00–22:00 LT
and thereafter a steady decline throughout the night with an early morning
rise starting between 05:00–06:00 LT and peaking at 08:00 LT before
decreasing to low values during midday hours (12:00–16:00 LT) (Fig. 2c). The diurnal trend is also qualitatively similar to the AMS HOA and BBOA
factors, in that the time of early morning rise coincides with an increase
in anthropogenic activities. Another observation is that during the daytime
(12:00–16:00 LT) the primary biomass burning declines to less than 4 % of
its average concentrations from the previous night. Three effects might
drive these very low daytime concentrations of primary biomass burning:
first, the decline in source intensities; second, the strong dilution
effects from boundary layer expansion; and third, increased evaporation of
semi-volatile constituents due to higher temperature and the aforementioned
dilution. The primary biomass burning factor constitutes on average around
70 % of the total EESI-TOF OA signal, whereas the AMS BBOA factors
contribute on average 44.7 % to the total organic mass measured by the
AMS. This is due to the higher sensitivity of the EESI-TOF towards
levoglucosan as compared to most other classes of compounds, as consistently
observed in laboratory and field studies (Lopez-Hilfiker et al., 2019; Stefenelli et
al., 2019; Tong et al., 2021). (Note that the EESI-TOF insensitivity to HOA
cannot explain this discrepancy, as AMS BBOA contributes only 50.9 % of
the non-HOA organic mass, i.e. BBOA/(OA-HOA).)
Cooking-related OA
A notable feature in the mass spectrum of this
factor is that ∼9.2 % of the total signal in this factor
comes from ions with H:C ratios >1.7 and O:C<0.25. The
contribution of such ions to biogenic SOA is 5.0 %, with all other
factors falling below 2.1 %. The carbon number distribution of these ions
is shifted towards higher carbon numbers, consistent with saturated and
non-saturated fatty acids (see Fig. 3). Such molecules are prominent
constituents of cooking oils (Orsavova
et al., 2015). The high relative contributions from such species are
consistent with previous cooking-related factors resolved in EESI-TOF
studies (Tong et al., 2021; Qi et al.,
2019).
Carbon number distribution plots of EESI-TOF factors.
Each carbon number contribution is stacked by contributions from CHON
species (red) and CHO species segregated by their H:C ratio categories. The
green-to-blue colour gradient represents CxHyOz compounds
classified by H:C ratios of H:C< 1.1, 1.1 <H:C< 1.3, 1.3 <H:C< 1.5, 1.5 <H:C< 1.7, and
H:C> 1.7, and red denotes CxHyOzN1-2.
The time series of cooking-related OA explains a large fraction of the
signal from ions consistent with fatty acids, e.g. 32.1 %, 38.8 %,
34.6 %, and 33.9 % of C16H32O2,
C16H30O2, C18H34O2, and
C18H36O2, respectively. The other five factors combined explain
only 15 %–20 % of the variation in these fatty-acid-like compounds, while
the rest remains unexplained; ∼5 %–10 % is explained by residuals and
∼90 %–95 % by noise. Figure S3 shows the fractional contribution of all
EESI-TOF factors to the diurnal trends of two selected fatty-acid-like
compounds, tentatively attributed to oleic acid (C18H34O2)
and stearic acid (C18H36O2). As one can clearly see, the
cooking-related OA factor is the dominant contributor to these species,
regardless of time of day or ion concentration. This observation of the high
contribution of cooking-related factor to observed diurnal patterns of fatty-acid-like compounds is also consistent with previously defined
cooking-related OA factors from the EESI-TOF (Tong et al.,
2021; Qi et al., 2019).
The diurnal trend of cooking-related OA shows qualitatively similar features
to primary biomass burning, in that it decreases during daytime hours and
peaks during the late evening (Fig. 2c). Cooking-related OA increases by a
factor of ∼6 from 16:00–19:00 LT, remaining roughly stable till 00:00
LT, followed by a decline till 06:00 LT, and then remaining approximately
stable until 10:00 LT. During the day, a small peak is observed during
lunchtime (13:00–15:00 LT), which may indicate active sources in the
vicinity of the measurement site. The average day-to-night ratio is
approximately a factor of 10 higher than that of primary biomass burning,
which further supports the possibility of active sources during the day.
We note that no cooking-related factor was identified in the AMS PMF
results. The unconstrained PMF analysis yielded an HOA factor with high
levels of oxygenated fragments, especially the oxygenated fragments at m/z 55
and 57, which might indicate mixing of cooking-related factor into HOA.
However, it is also possible that these oxygenated fragments were
contributed by some other sources (e.g. BBOA or LO-OOA); hence a definite
conclusion on the mixing of the cooking-related factor into unconstrained HOA
could not be drawn. Neither constraining a COA profile from the literature in
the AMS PMF nor increasing the number of factors up to 15 yielded
cooking-related factors. Possible reasons for this may be the similarity of
the cooking-related OA spectrum with the HOA and BBOA spectra in the AMS,
high relative concentrations of the other primary factors, and strong
effects of boundary layer dynamics on the diurnal patterns of all factors
(leading to collinearity among unrelated factors), all of which combine to
make it difficult to separate a relatively minor cooking-related factor
without the specific tracer ions provided by the EESI-TOF.
Secondary factorsAromatic SOA
The mass spectrum of this factor has ∼63.0 % of its total signal contributed by compounds with six to nine carbons
(C6-C9). A large fraction of this comes from molecules with an H:C
ratio <1.5 (27.9 % of the total signal from
C6-9HyOz ions and 7.1 % from
C6-9HyOzN1-2 ions) (Fig. 3). As a comparison, biogenic
SOA, aged biomass burning, and mixed urban SOA have an 11.7 %, 27.2 %, and
18.3 % contribution from C6-9HyOz ions and 4.0 %, 2.9 %, and 3.7 % contribution from C6-9HyOzN1-2 ions
with H:C ratios <1.5, respectively. The low H:C ratios are
associated with aromatic systems, as the precursor gases are highly
unsaturated and contain fewer hydrogen atoms than more saturated
straight-chain or ring-containing compounds. In a recent study on the source
apportionment of VOCs at the same site, it was found that
aromatic C6-9Hy VOCs constitute 45.4 % of total VOC loading
and are emitted into the atmosphere predominantly from anthropogenic
activities, of which traffic constituted the highest fraction during the
daytime (Wang et al., 2020). Oxidation of these aromatic VOCs
is most likely the dominant process leading to the formation of this factor.
In order to substantiate the claim that the major ions contributing to the ambient
aromatic SOA factor are indeed formed by oxidation of aromatic VOCs, we
conducted a chamber experiment in the Paul Scherrer Institute (PSI) smog
chamber using a mixture of aromatic compounds consisting of benzene,
toluene, ethylbenzene, and trimethylbenzene (Kumar et
al., 2022). These compounds are well-established constituents of
vehicular emissions (Cao
et al., 2016; Yao et al., 2015) especially for gasoline vehicles during the
cold start phase (Platt et al., 2017).
OH radicals were produced in the chamber and reacted with the VOCs,
resulting in the formation of SOA, whose chemical composition was
subsequently compared with the aromatic SOA and other SOA factors obtained
in this study.
Figure S2 shows the mass spectrum of the ambient aromatic SOA factor
colour-coded by ions that were found in chamber SOA formed from oxidation of
aromatics. Approximately 32.0 % of the EESI-TOF signal contained in the
aromatic SOA factor overlapped with ions identified in the chamber
experiment. This signal fraction is considerably higher than for other SOA
factors, i.e. biogenic SOA, aged biomass burning, and mixed SOA had signal
contributions from chamber SOA ions of 16.0 %, 26.0 %, and 20.0 %,
respectively.
Despite the strong dilution of the boundary layer during the day, there is
little variation in the aromatic SOA concentration during the day. This
points to a strong local daytime source, such that the production of
aromatic SOA is fast enough to offset the strong dilution effects of the
expanding boundary layer as discussed further in Sect. 3.5.
Biogenic SOA
The mass spectrum of biogenic SOA is shown in Fig. 2a. It contains high contributions from ions such as C9H16O5,
C8H14O6, and C9H14O4, which have been previously
identified in EESI-TOF factors representing biogenic oxidation products (Qi et al., 2020; Stefenelli et al., 2019).
Compounds with H:C>1.5 constitute nearly 50 % of the signal
of this factor, with 2.9 %, 5.0 %, 6.8 %, and 2 % of signal
resulting from C7H10-14O4-8, C8H12-16O4-8,
C9H14-18O4-8, and C10H16Oz, respectively. These
compounds have been previously attributed to monoterpene oxidation products
in Zurich, Switzerland (Stefenelli et al., 2019). There are,
however, some notable differences from the Zurich study. Specifically, the
present study shows a much smaller contribution from the
C10H16Ozcompounds (6.2 % in Zurich vs. 2 % in this
study), while CxHyOzN1 compounds comprise 26.7% of
the factor profile (vs. ∼13% in Zurich). These differences
might arise because of two different reasons. One is the probable
contribution of not only monoterpene oxidation products (which dominate in
Zurich) but also isoprene oxidation products, e.g. C5H10Ox
and C5H9NOx (Chen et al., 2020), to
the biogenic factor retrieved in this study. This is because of Delhi's
location near the tropics, which results in a large contribution of isoprene
to the total biogenic VOCs. Model estimates predict isoprene emission
fluxes to be a factor of ∼20 higher than α-pinene in
tropical regions of India, whereas in Europe the emission fluxes of isoprene
and monoterpenes are similar (Guenther
et al., 2012; Sindelarova et al., 2014). Zurich and Delhi also differ in
terms of atmospheric conditions, in particular the much higher NOx
levels in Delhi as compared to Zurich, consistent with the higher
CxHyOzN1 fraction. Biogenic VOCs such as monoterpenes
and isoprene are susceptible to oxidation by NO3 radicals, which can
result in large amounts of biogenic SOA production. In Delhi, however, due
to large concentrations of NO (∼200–300 ppbv) during the nighttime, the
production of NO3 radicals is suppressed and the diurnal cycle of
NO3 is actually inverted with the majority of available NO3
radicals actually present during the daytime (Haslett et al., 2022).
The diurnal trend of this factor resembles that of the EESI-PMF primary
factors in that the concentration is highest overnight and a strong decrease
is seen during daytime hours. A possible explanation of this behaviour could
be more regional sources of the biogenic VOCs scattered over a large area.
This means that the biogenic SOA factor most likely has only a small
daytime source in the vicinity of the site.
Aged biomass burning
The fractional contribution of levoglucosan
(C6H10O5) to the aged biomass burning factor mass spectrum is
∼10 %, which is a factor of 8 lower than for the primary biomass
burning factor. The lower levoglucosan content in the aged biomass burning
mass spectrum as compared to the primary biomass burning mass spectrum is
consistent with observations of similar factors in Zurich during winter (Qi et al., 2019). Additionally, chamber studies have shown
that the levoglucosan concentration decreases in aged biomass burning
particles (Bertrand et al., 2018), while the concentrations of
secondary species increase, consistent with observations in this study.
The mass spectrum of this factor also has ∼2 %
contributions each from two key ions, C6H8O6 and
C7H8O7. These are most likely oxidation products of phenols
and methoxy-phenols, which are abundant secondary compounds formed during
the ageing of biomass burning emissions (Yee et al., 2013)
and are important precursors of biomass burning SOA. The aged biomass
burning factor contains dominant signals from various other small molecules
with H:C ratios less than 1.3, which is consistent with the oxidation of
small aromatic compounds emitted during biomass burning such as phenolic
compounds. These compounds were observed to be major contributors of
gas-phase solid-fuel combustion factors in a recent VOC source apportionment
study conducted at the same site (Wang et al., 2020).
The diurnal trend of aged biomass burning is similar to the one of primary
factors, characterized by increased concentrations during evening hours and
a decline during daytime hours. The amplitude of the evening time peak
however differs between aged biomass burning and primary biomass burning
(Fig. S1). While the primary biomass burning increases by a factor of ∼50–60 between 17:00–22:00 LT, the increase in oxidized biomass burning is
within a factor of ∼5 during the same time. Between 22:00–07:00 LT, the
concentration of this factor steadily decays by a factor of 2.5. A distinct
peak is also observed between 07:00–09:00 LT, which coincides with an
increase in solar radiation, indicating that ageing of emissions takes place
during early morning hours. Note that it is likely that a majority of local
emissions are not oxidized during nighttime in Delhi due to very high
levels of NO (∼200–300 ppb), which may scavenge both O3 and NO3
radicals during nighttime and inhibit nocturnal degradation of VOCs (Haslett et al., 2022).
The diurnal pattern of the LO-OOA factor from AMS correlates well with the
aged biomass burning factor from the EESI-TOF and suggests that oxidation of
biomass burning emissions may be the dominant contributor to the LO-OOA
factor observed in the AMS.
Mixed urban SOA
The remaining factor is most likely a mixed SOA
factor, which has influences from both anthropogenic and biogenic sources.
The highest-intensity ions in this factor are C5H10O4,
C9H14O5, C6H10O5, C9H16O5,
C9H18O5, and C6H12O4. C5H10O4
is probably a product of isoprene oxidation, whereas the dominance of
C9 compounds suggests contributions from oxidation products of
C9 species. The C9 species have varied sources in urban areas which
include evaporative losses of fuels (e.g. gasoline), solvent use, and
unburnt exhaust emissions (Mehra et al., 2020; Zhang
et al., 2013), and hence this factor is likely influenced by different
sources linked to the Delhi urban area. This factor also has ∼2.5 % of mass
spectral signal contributed by levoglucosan (C6H10O5). Either this
could be due to non-perfect unmixing by PMF, or it could indicate the
contributions from biomass burning with other above-mentioned sources in
this factor. This factor is therefore named mixed urban SOA.
The diurnal pattern of this factor shows roughly stable concentrations from
00:00–06:00 LT, which is similar to the aromatic SOA factor. All other
factors show a decrease during these hours. It increases by a factor of ∼3
between 11:00–14:00 LT and then steadily decays before being enhanced
again by a factor of ∼15 between 18:00–21:00 LT. The daytime increase
suggests photochemical production of this factor. The diurnal trend of this
factor is stable during late night hours and does not show a marked early morning rush hour peak, indicating little or no influence of morning rush
hour emissions.
Estimation of mass contributions of EESI SOA factors
The EESI-TOF sensitivity towards individual compounds has been shown to vary
by up to 1–2 orders of magnitude. Although EESI-TOF factor sensitivities
likely vary by significantly less due to averaging effects, these variations
nonetheless make it challenging to ascertain relative contributions of
EESI-TOF factors on a mass concentration basis. To estimate the EESI-TOF
sensitivities (in cps µg-1 m3, where cps denotes counts per second) to different EESI
SOA factors and thus obtain a mass-based source apportionment of the
resolved SOA factors, a multiple linear regression (MLR) analysis was
performed to explain the AMS SOA (i.e. MO-OOA + LO-OOA) time series as a
function of the four EESI-TOF SOA factors. Eq. (11) was solved for
α1, α2, α3, and α4 where
the reciprocal of the coefficients αi represents the
sensitivity of the EESI-TOF to each factor in cps µg-1 m3.
AMSSOA=α1×aromaticSOA+α2×biogenicSOA+α3×agedbiomassburning+α4×mixedurbanSOA+∈
To solve Eq. (11), a weighted least-squares approach was used where the
uncertainty-weighted residuals (denoted ∈) were minimized
for each point in time. The α coefficients for all EESI-TOF SOA
factors were constrained such that the obtained sensitivities of these
factors were between 0.1 and 1 times that of levoglucosan (∼55 cps µg-1 m3), consistent with previous observations of
bulk EESI-TOF sensitivities to SOA from different precursors (Lopez-Hilfiker et al., 2019). In addition to the MLR, the
EESI sensitivities towards individual oxidation products were estimated
using a gradient-boosting regression–prediction (GBRP) model (Wang et al., 2021) based on their elemental formulae (i.e. CxHyOz). The EESI-TOF sensitivity to different SOA factors
was derived by calculating the signal-weighted average based on the factor
profile of these individual ion sensitivities. The GBRP model results were
used in relative terms, where the response factors obtained for each
EESI-TOF factor using the GBRP model were normalized relative to that of
primary biomass burning. The EESI-TOF response factor for biomass burning
was calculated by taking the ratio of the summed EESI signal in primary biomass
burning to the summed AMS BBOA factors. This was then used to scale
the sensitivities of the SOA factors obtained using the GBRP model.
The MLR analysis was first applied to the entire time series, which resulted
in a correlation coefficient (r) value of 0.6 between modelled SOA
(α1× aromatic SOA +α2× biogenic SOA +α3× aged biomass burning +α4× mixed urban SOA) and measured SOA (sum of MO-OOA and LO-OOA from AMS). There were,
however, two issues with this analysis. One was that it showed systematic
positive and negative biases in certain parts of the time series, and the second
was that the fitted MLR coefficients for biogenic SOA, aged biomass burning,
and mixed urban SOA were near zero, which, based on previous studies,
implied a non-physical result (Lopez-Hilfiker et al., 2019).
The possible reason for these mentioned issues might be the presence of a
unique event from 18:00 LT on 3 January 2019 to 12:00 LT on 4 January
2019 when high signals of aromatic SOA with low signals of biomass burning
and other primary and secondary species were observed, driving the
coefficients of all other SOA factors except aromatic SOA to be near zero.
Based on the issues mentioned above, the time series was divided into two
parts: part 1 from 31 December 2018–3 January 2019 (till 18:00 LT)
and part 2 from 4 January 2019 (from 12:00 LT)–13 January 2019. The
data from 16:00 LT on 3 January 2019 to 12:00 LT on 4 January 2019
were omitted.
The sensitivities were then obtained by MLR analyses of different parts of
the dataset: (1) the sensitivity was estimated by performing MLR on the
entire EESI-TOF factor time series; (2) the sensitivity was estimated by
performing MLR on only part 1 of the time series; (3) the sensitivity was
estimated by performing MLR on only part 2 of the time series; (4) the
EESI-TOF sensitivity was assumed to be uniform for all factors, where the
bulk EESI sensitivity was calculated as the slope of the total EESI-TOF
signal vs. the total AMS organic mass. In addition, the EESI-TOF sensitivity
towards individual oxidation products was estimated using a
gradient-boosting regression–prediction (GBRP) model (Wang et
al., 2021) based on their elemental formulae (i.e.
CxHyOz+) as described above.
The sensitivities obtained using MLR analysis and predicted by the GBRP model
were used to calculate modelled SOA, and results were evaluated based on
three parameters (Fig. S4): (1) the Pearson correlation coefficient r between the
modelled and measured SOA; (2) the mean of the fractional residuals, i.e. measured SOA - modelled SOA / measured SOA; and (3) the mean of the scaled residuals,
i.e. measured SOA - modelled SOA / uncertainty in measured SOA.
The fractional and scaled residuals were closest to zero and hence had the
least bias when modelled SOA was calculated by using α
coefficients obtained by MLR analysis on part 2 of the time series (Fig. S4). Hence the α coefficients obtained using MLR
analysis on part 2 of the time series were applied to the entire time-series
factors to calculate a mass-based estimation of the SOA factors. The
coefficients obtained were 0.15, 0.11, 0.10, and 0.12 for aromatic SOA,
biogenic SOA, aged biomass burning, and mixed urban SOA, respectively. These
coefficients correspond to sensitivities of 6.6, 9.1, 10.0, and 8.3 cps µg-1 m3, respectively. As a comparison, the
sensitivities predicted by Wang et al. (2021) were 6.1,
8.1, 8.6, and 11.1 cps µg-1 m3 for aromatic SOA, biogenic SOA, aged biomass burning, and mixed urban SOA,
respectively, and lay between ± 35 % of those obtained from MLR
analysis, providing evidence of robustness of this MLR analysis. Figure S5 shows the
time series of measured and modelled SOA obtained using the coefficients
derived from the five different strategies discussed above.
Source apportionment of total OA
Here, we combine MLR-corrected EESI-TOF concentrations for aromatic SOA,
biogenic SOA, aged biomass burning, and mixed urban SOA (the sum of which by
definition approximates the total AMS-derived SOA) with the AMS source
apportionment results for POA factors (i.e. HOA and BBOA) to provide an
overall description of the OA sources influencing Delhi. The 24 h average,
daytime average (10:00–16:00 LT), and nighttime average (22:00–04:00 LT)
factor contributions to total OA mass are shown in Fig. 4. While SOA
contributes only 40.0 % of total OA on a daily average basis, there is a
stark difference between day and night. SOA constitutes 76.8 % of total
OA during the daytime (10:00 to 16:00 LT). The aromatic SOA is the largest
contributor to daytime SOA and contributes 55.2 % of SOA during the daytime
(42.4 % of total OA), followed by biogenic SOA, which contributes 18.4 % to daytime SOA (14.2 % to total OA). The contributions of aged
biomass burning and mixed urban SOA to total SOA during daytime are 11.7 % and 8.5 %, respectively (15.2 % and 11.0 % to total OA). During the
nighttime (21:00 to 04:00 LT), SOA constitutes 31.0 % of total OA mass,
with biogenic SOA contributing 36.1 % of SOA (11.2 % of total OA)
followed by a 25.4 % contribution by aromatic SOA (7.9 % to total OA).
Aged biomass burning and mixed urban SOA contribute 15.4 % and 22.9 %
to total nighttime SOA, respectively (4.8 % and 7.1 % contribution to
total OA, respectively). During the nighttime, the high OA concentrations are
driven by high primary emissions into a shallow boundary layer; during the
daytime, the OA is dominated by secondary aerosol, including local
oxidation in the elevated boundary layer.
Overall OA source apportionment results, combining AMS
PMF with MLR-corrected EESI-TOF SOA source apportionment results. EESI-TOF
cooking-related OA is excluded. EESI-TOF primary biomass burning is assumed
to be equivalent to the sum of the AMS BBOA factors, and therefore only the
AMS factors are shown. EESI-TOF SOA concentrations are calculated using the
MLR-derived factor-dependent sensitivities. Panel (a) shows the overall
source apportionment, while panels (b) and (c) show the daytime
and nighttime results, respectively.
The differing relative contributions of primary vs. secondary OA as a
function of the time of day have implications for public health policy.
Specifically, although POA dominates the overall OA concentration, the SOA
factors are most prevalent during times when people are most likely to be
outdoors and thus exposed to OA (i.e. daylight). It has been recently shown
that oxygenated OA contributes a substantially higher fraction of particle-bound reactive oxygen species (ROS) (Zhou et al., 2019) as
compared to primary OA. More specifically, anthropogenic SOA has been shown
to be more relevant in terms of oxidative potential (OP) than biogenic SOA
and POA (Daellenbach et al., 2020). In a recent
study at the same site in Delhi, the ratio of hourly averaged ambient DTT
(dithiothreitol) activity in PM2.5 to the NR-PM1 mass
concentration (i.e. the intrinsic oxidative potential (OPin)) was
found to be highest during the afternoon period (Puthussery et al., 2020). This coincides with the
increased contributions from photochemically formed secondary organic
aerosol (SOA) as observed in this study. Furthermore, the ratio of
anthropogenic to biogenic SOA in Delhi especially during the daytime is
high and the SOA fraction is dominated by aromatic SOA. This suggests that
the daytime increase in OPin observed by Puthussery et al. (2020) is most likely driven by
large contributions from aromatic SOA, which is similar to observations across Europe
by Daellenbach et al. (2020). The aromatic SOA is
most likely formed from the oxidation of light aromatics emitted by traffic.
Reducing traffic emissions, e.g. by cleaning exhaust emissions with
catalytic converters, can reduce emission factors of aromatic compounds and
may lead to a decrease in total SOA concentration and the oxidative potential of
OA.
Fraction of EESI-TOF factors attributable to local production or
emissions during daytime
As discussed previously, the temporal trends of the different factors are
likely driven by photochemical production (SOA), emissions (POA), boundary
layer dynamics, and gas–particle partitioning. Figure 5 shows the estimated
fraction of daytime concentrations attributable to photochemical production
for the EESI-TOF SOA factors or direct emissions for EESI-TOF POA factors.
AMS-derived PMF factors were not included in this analysis due to the lack
of reliable methods to compute saturation vapour concentrations of these
factors. The SOA factors (aromatic SOA, aged biomass burning, and mixed
urban SOA) have a high mean fraction of daytime photochemical production
values of 0.88, 0.82, and 0.83, respectively. This is significantly higher
than the daytime photochemical fraction of 0.55 for biogenic SOA (t test,
p<0.05). This daytime production suggests that local
photochemistry is an important driver for daytime air quality in Delhi and
thus relevant to human exposure and health outcomes. As shown in a VOC
source apportionment study at the same site (Wang et al.,
2020), the largest contributor of primary VOCs during the daytime at this site
is a traffic-related factor. This is consistent with high concentrations of
light aromatics, which are in turn consistent with the elevated
concentration and strong local production term of the EESI-TOF aromatic SOA
factor.
Box-and-whisker plots showing the fraction of daytime
local production (SOA) or emissions (POA) for EESI-TOF factors. The values
averaged over all days for all factors are depicted by numbers adjacent to
each box. For the ease of viewing, background shading denotes SOA (pink) and
POA (blue) factors.
The smaller daytime production fraction retrieved for biogenic SOA is
consistent with its description as a regionally influenced factor. This is
consistent with a projected source distribution that is diffused over a wide
area rather than limited to Delhi.
For the primary factors, a relatively small fraction, i.e. 0.53, of primary
biomass burning could be attributed to daytime emissions, whereas a
relatively high daytime emission fraction, i.e. 0.83, was observed for
cooking-related OA, consistent with expectations as primary biomass burning
most likely corresponds to nighttime heating activities, while the
cooking-related emissions emerge from active sources during specific
mealtimes.
Conclusions
Wintertime particulate air pollution in Delhi, India, is a critical public
health issue that affects millions of people. Previous studies have
identified key POA sources contributing to this pollution and suggested an
important role of total SOA. Here we investigate the sources contributing to
SOA via source apportionment of the first EESI-TOF deployment in India, in
conjunction with AMS source apportionment.
The AMS source apportionment yielded POA factors related to traffic, primary
biomass burning (two factors), and SOA (two factors), which in total comprised
60 % and 40 % of the OA mass, respectively. The source apportionment of the
EESI-TOF dataset yielded six factors. Two primary factors were identified as
primary biomass burning and cooking-related OA, while the remaining four
factors were attributed to secondary sources: aromatic SOA, produced from
the oxidation of light aromatics emitted by traffic; biogenic SOA,
influenced by isoprene and monoterpene oxidation products and of regional
influence; aged biomass burning; and mixed urban SOA, containing oxidation
products consistent with a mix of sources and processes typical of the Delhi
area. Multiple linear regression (MLR) analysis allowed us to calculate
response factors for the EESI-TOF SOA factors and enabled apportioning the
contribution of each EESI-TOF SOA factor to total SOA mass. During the daytime,
SOA dominated, comprising 76.8 % of the total OA mass with 42.4 %
contribution from aromatic SOA. The nighttime concentrations were dominated
by POA, making up 69.0 % of total OA mass. Large variations in the
relative contribution of SOA vs. POA to total OA were observed between the day
and night, with anthropogenic SOA sources being major contributors to
daytime SOA, explaining the previously observed daytime increase in OP of
PM at the same site (Puthussery et al., 2020).
A simple partition-and-dilution modelling analysis was used to estimate the
fraction of daytime concentrations that could be attributed to
photochemical production for the SOA factors and emissions for the POA
factors. Aromatic SOA was found to have the highest photochemical production
among all SOA factors, consistent with the high abundance of aromatic VOCs at
the site as was previously seen (Wang et al., 2020). Biogenic
SOA had significantly lower daytime photochemical production than other SOA
factors, indicating its regional nature and that its temporal behaviour is
controlled by dilution and partitioning and to a lesser extent by
photochemical production. This study reveals that the HOA and BBOA are the
main POA sources in Delhi and that aromatic SOA, biogenic SOA, aged biomass
burning, and mixed urban SOA constitute total SOA. The daytime OA mass is
dominated by SOA, which is mainly composed of aromatic SOA, whereas the
nighttime OA is dominated by POA sources of which biomass burning is the
dominant one.
Data availability
The data presented in figures are available in the Zenodo online repository
10.5281/zenodo.6584570 (Kumar, 2022).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-7739-2022-supplement.
Author contributions
ASHP, JGS, and SNT designed the study and acquired the necessary funding. VK
led the field campaign, carried out formal data analysis, and wrote the manuscript.
JGS and VK interpreted the data together. JGS, SG, SLH, YT, CPL, SM, RS, PV,
JVP, and DG provided necessary support during the campaign. AS, JSD, and
NR provided AMS data. DSW provided GBRP model results. GS, VP, AB, RC, PR, DB, and LQ participated in the campaign from the PSI side. DMB, VV, CM, SNT, ASHP,
and UB participated in the interpretation of data. All authors read and
edited the manuscript.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Sachchida N. Tripathi gratefully acknowledges the financial
support provided by the Department of Biotechnology (DBT), government of India, and the Central Pollution Control Board (CPCB), government of India. Sophie L. Haslett and Claudia Mohr gratefully
acknowledge the financial support provided by the Knut and Alice Wallenberg
Foundation.
Financial support
This research has been supported by the SDC
Clean Air Project in India (grant no. 7F-10093.01.04); the
Swiss National Science Foundation projects BSSGI0_155846 (IPR-80 SHOP), 200021_169787 (SAOPSOAG), and IZLCZ2_169986; the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions (grant no. 701647); and the Knut and Alice Wallenberg Foundation (WAF project CLOUDFORM (grant no. 2017.0165)). Sachchida N. Tripathi was also supported by the Department of Biotechnology (DBT), government of India (grant no. 644), BT/IN/UK/APHH/41/KB/2016-17 dated 19 July 2017, and the Central Pollution Control Board (CPCB), government of India, under grant no. AQM/Source apportionment_EPC Project/2017.
Review statement
This paper was edited by Tuukka Petäjä and reviewed by two anonymous referees.
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