Isoprene and monoterpene emissions to the atmosphere are generally
dominated by biogenic sources. The oxidation of these compounds can lead to
the production of secondary organic aerosol; however the impact of this
chemistry in polluted urban settings has been poorly studied. Isoprene and
monoterpenes can form secondary organic aerosol (SOA) heterogeneously via anthropogenic–biogenic
interactions, resulting in the formation of organosulfate (OS) and
nitrooxy-organosulfate (NOS) species. Delhi, India, is one of the most polluted
cities in the world, but little is known about the emissions of biogenic
volatile organic compounds (VOCs) or the sources of SOA. As part of the DELHI-FLUX project, gas-phase
mixing ratios of isoprene and speciated monoterpenes were measured during
pre- and post-monsoon measurement campaigns in central Delhi. Nocturnal
mixing ratios of the VOCs were substantially higher during the post-monsoon
(isoprene: (0.65±0.43) ppbv; limonene: (0.59±0.11) ppbv;
α-pinene: (0.13±0.12) ppbv) than the pre-monsoon (isoprene:
(0.13±0.18) ppbv; limonene: 0.011±0.025 (ppbv); α-pinene: 0.033±0.009) period. At night, isoprene and monoterpene
concentrations correlated strongly with CO during the post-monsoon
period. Filter samples of particulate matter less than 2.5 µm in
diameter (PM2.5) were collected and the OS and NOS content analysed
using ultra-high-performance liquid chromatography tandem mass spectrometry
(UHPLC-MS2). Inorganic sulfate was shown to facilitate the formation of
isoprene OS species across both campaigns. Sulfate contained within OS and
NOS species was shown to contribute significantly to the sulfate signal
measured via AMS. Strong nocturnal enhancements of NOS species were observed
across both campaigns. The total concentration of OS and NOS species contributed
an average of (2.0±0.9) % and (1.8±1.4) % to the total
oxidized organic aerosol and up to a maximum of 4.2 % and 6.6 %
across the pre- and post-monsoon periods, respectively. Overall, this study
provides the first molecular-level measurements of SOA derived from isoprene
and monoterpene in Delhi and demonstrates that both biogenic and
anthropogenic sources of these compounds can be important in urban areas.
Introduction
India is undergoing significant urbanization and industrialization, with a
rapidly increasing population. India is home to 14 out of the top 20 most polluted cities in the world between 2017 and 2021 in terms of annual mean PM2.5 (particulate matter less than 2.5 µm in diameter) concentrations. In Delhi, the population-weighted mean
PM2.5 was estimated to be 209 µgm-3 (range: 120–339.5 µgm-3)
in 2017, over 40 times the WHO annual mean guidelines of 5 µgm-3
and greater than 5 times India's own standard of 40 µgm-3 (Balakrishnan
et al., 2019). Air pollution is estimated to cause over 1 million deaths per
year in India alone (Landrigan et al., 2018).
Numerous studies have investigated PM2.5 concentrations,
characteristics, and meteorological effects in Delhi
(Anand
et al., 2019; Bhandari et al., 2020; Chowdhury et al., 2004; Hama et al.,
2020; Kanawade et al., 2020; Miyazaki et al., 2009; Nagar et al., 2017). The
key sources of PM2.5 identified are secondary aerosol, fossil fuel
combustion, municipal waste, and biomass burning
(Chowdhury
et al., 2004; Sharma and Mandal, 2017; Stewart et al., 2021a, b).
Previous studies have also shown that alongside extremely high emissions of
pollutants, regional sources and meteorology in particular play an important
role in high-pollution events in Delhi
(Bhandari
et al., 2020; Sawlani et al., 2019; Schnell et al., 2018; Sinha et al.,
2014).
Secondary species have been shown to be significant contributors to PM1
and PM2.5 mass in Delhi, with organics contributing 40 %–70 % of
PM1 mass (Gani
et al., 2019; Shivani et al., 2019; Reyes-Villegas et al., 2021; Sharma and
Mandal, 2017). However, limited molecular-level analysis of organic aerosol
(OA) has been undertaken
(Chowdhury
et al., 2004; Elzein et al., 2020; Miyazaki et al., 2009; Singh et al.,
2021, 2012; Yadav et al., 2021). Kirillova et al. (2014) analysed the
sources of water-soluble organic carbon (WSOC) in Delhi, using radiocarbon
measurement constraints. The study identified that 79 % of WSOC was
classified as non-fossil carbon, attributed to biogenic and biomass burning
sources in urban Delhi (Kirillova
et al., 2014), similar to other studies from India
(Kirillova
et al., 2013; Sheesley et al., 2012). Studies across Asia, Europe, and North
America have also shown high contributions from non-fossil sources to
ambient PM concentrations in urban environments
(Du
et al., 2014; Kirillova et al., 2010; Szidat et al., 2004; Wozniak et al.,
2012). The sources of this modern carbon in urban areas are poorly
understood, although biomass burning is a key component
(Elser
et al., 2016; Hu et al., 2016; Lanz et al., 2010; Nagar et al., 2017).
Recently in Delhi, solid-fuel combustion sources such as cow dung cake or
municipal solid waste have been shown to release over 1000 different organic
components into the aerosol phase at emission
(Stewart
et al., 2021b). Alongside biomass burning, one potential source of this
non-fossil aerosol is biogenic secondary organic aerosol (BSOA), which is
formed via the oxidation of biogenic volatile organic compounds (BVOCs) and
subsequent gas–particle phase transfer
(Hallquist
et al., 2009; Hoffmann et al., 1997).
Isoprene is the most abundant BVOC, with annual global emissions estimates
of between 350–800 Tg yr-1 (Guenther
et al., 2012; Sindelarova et al., 2014). Globally, isoprene is predominately
emitted from biogenic sources, but anthropogenic sources become increasingly
important in urban areas especially, at night
(Borbon
et al., 2001; Hsieh et al., 2017; Khan et al., 2018a; Mishra and Sinha,
2020; Sahu et al., 2017; Sahu and Saxena, 2015). Monoterpenes are another
important BSOA precursor, with annual global emissions estimates of between
89 and 177 Tg yr-1
(Guenther
et al., 2012; Sindelarova et al., 2014). Monoterpenes, while mainly biogenic,
are also emitted from anthropogenic sources such as biomass burning, cooking,
and fragranced consumer products
(Cheng
et al., 2018; Gkatzelis et al., 2021; Panopoulou et al., 2020, 2021; Stewart
et al., 2021a, c; Zhang et al., 2020).
Numerous studies have identified and quantified molecular-level markers from
isoprene and monoterpenes, especially in the south-eastern US and China
(Brüggemann
et al., 2019; Bryant et al., 2020, 2021; Hettiyadura et al., 2019; Huang et
al., 2016; Rattanavaraha et al., 2016; Wang et al., 2016, 2018; Yee et
al., 2020). The complex sources of isoprene and monoterpenes in highly
polluted urban areas make source identification difficult. As such, the secondary organic aerosol (SOA)
markers in this study are referred to as originating from isoprene or
monoterpenes, but the emissions are likely from a mixture of biogenic and
anthropogenic sources as discussed previously (Cash
et al., 2021; Nelson et al., 2021).
Recent studies have started to focus on anthropogenic–biogenic interactions,
whereby anthropogenic pollutants such as NOx and sulfate enhance the
formation of biogenically derived SOA species. Increased NO or NO2
concentrations can lead to higher organonitrate (ON) or
nitrooxy-organosulfate (NOS) concentrations through RO2+ NO or volatile organic compound (VOC)–NO3
pathways (Morales
et al., 2021; Takeuchi and Ng, 2019). Inorganic sulfate formed from the
oxidation of SO2 plays a pivotal role in OS and NOS formation
(Bryant
et al., 2020; Budisulistiorini et al., 2015; Glasius et al., 2018;
Hettiyadura et al., 2019; Hoyle et al., 2011; Xu et al., 2015). Sulfate
allows the acid-catalysed uptake of gas-phase oxidation products into the
particle phase. Both chamber and ambient studies have shown the direct link
between sulfate and OS concentrations
(Brüggemann
et al., 2020b; Bryant et al., 2020; Budisulistiorini et al., 2015; Gaston et
al., 2014; Lin et al., 2012; Riva et al., 2019; Surratt et al., 2008; Xu et
al., 2015). Yee et al. (2020) highlighted markers from both the high- and low-NO
isoprene oxidation pathways correlated linearly with sulfate over a large
range of sulfate concentrations (0.01–10 µgm-3) across central
Amazonia during the wet and dry seasons and in the SE US summer. They
conclude that the majority of isoprene oxidation products in pre-industrial
settings are still expected to be in the form of isoprene OS (OSi),
suggesting that they cannot be thought of as purely a biogenic–anthropogenic
product (Yee et al., 2020).
In this study, offline PM2.5 filter samples were collected across two
campaigns (pre- and post-monsoon) in central Delhi, alongside a comprehensive
suite of gas and aerosol atmospheric pollutant measurements. Filters were
analysed using ultra-high-performance liquid chromatography tandem mass
spectrometry and isoprene and monoterpene OS and NOS markers identified and
quantified. Isoprene and monoterpene emissions were observed to correlate
strongly to anthropogenic markers, suggesting mixed anthropogenic/biogenic
sources of these VOCs. OSi species showed strong seasonality and strong
correlations to particulate sulfate. NOS species showed strong nocturnal
enhancements, likely due to nitrate radical chemistry. This study is the
first molecular-level particle-phase analysis of OS and NOS markers from
isoprene and monoterpenes in Delhi and aims to improve our understanding of
the sources of isoprene and monoterpene SOA markers and their formation
pathways in extremely polluted urban environments.
ExperimentalFilter collection and site information
PM2.5 filter samples were collected as part of the Air Pollution and
Human Health (APHH)-India campaign, at the Indira Gandhi Delhi Technical
University for Women in New Delhi, India, (28∘39′55′′ N,
77∘13′56′′ E). The site is situated inside the Third Ring Road,
which caters to huge volumes of traffic, with a major road to the east,
between the site and the Yamuna River. Two train stations are located to the
south and south-west of the site, and there are several green spaces locally
in all
directions (Nelson
et al., 2021; Stewart et al., 2021c). Filters were collected during two field
campaigns in 2018. The first campaign was during the pre-monsoon period,
with 35 filters collected between 28 May and 5 June 2018. As part of the second
campaign during the post-monsoon period, 108 filters were collected between
9 October and 6 November 2018. Quartz filters (Whatman QMA, 10′′×8′′ or 20.32cm×25.4 cm) were
pre-baked at 550 ∘C for 5 h and wrapped in foil before use. Samples
were collected using a HiVol sampler (Ecotech 3000, Victoria Australia)
with a selective PM2.5 inlet at a flow rate of 1.33 m3 min-1.
Once collected, filters were stored in foil at -20∘C before, during,
and after transport for UK-based analysis.
Filter extraction
Using a standard square filter cutter, a section of filter was taken with an
area of 30.25 cm2, which was then cut into roughly 1 cm2 pieces and
placed in a 20 mL glass vial. Next, 8 mL of liquid chromatography–mass spectrometry (LC–MS) grade methanol (MeOH;
Optima, Fisher Chemical, USA) was added to the sample and sonicated for 45 min. Ice packs were used to keep the bath temperature below room
temperature, with the water swapped mid-way through. Using a 5 mL plastic
syringe, the MeOH extract was then pushed through a 0.22 µm filter
(Millipore) into another sample vial. An additional 2 mL (2×1 mL) of MeOH
was added to the filter sample and then extracted through the filter to
give a combined extract of ∼10 mL. This extract was then reduced
to dryness using a Genevac solvent evaporator under vacuum. The dry sample
was then reconstituted in 50:50MeOH:H2O (Optima, Fisher Chemical, USA)
for analysis
(Bryant
et al., 2020; Spolnik et al., 2018). Extraction efficiencies of
2-methyl-glyceric acid (2-MG-OS) and camphorsulfonic acid were determined
using authentic standards spiked onto a pre-baked clean filter, and
recoveries were calculated to be 71 % and 99 %, respectively.
Ultra-high-performance liquid chromatography tandem mass spectrometry
(UHPLC-MS<inline-formula><mml:math id="M57" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula>)
The extracted fractions of the filter samples were analysed using an
Ultimate 3000 UHPLC (Thermo Scientific, USA) coupled to a Q Exactive
Orbitrap MS (Thermo Fisher Scientific, USA) using data-dependent tandem mass
spectrometry (ddMS2) with a heated electrospray ionization (HESI) source.
The UHPLC method uses a reversed-phase, 5 µm, 4.6 mm × 100 mm, polar end-capped Accucore column (Thermo Scientific, UK) held at 40 ∘C. The mobile phase consisted of water (A, Optima-grade) and
methanol (B, Optima-grade), both with 0.1 % (v/v) of formic acid (98 %
purity, Acros Organics). Gradient elution was used, starting at 90 % (A)
with a 1 min post-injection hold, decreasing to 10 % (A) at 26 min, returning to the starting mobile-phase conditions at 28 min,
followed by a 2 min hold allowing the re-equilibration of the column. The
flow rate was set to 0.3 mL min-1. A sample injection volume of 4 µL was used. The capillary and auxiliary gas heater temperatures were
set to 320 ∘C, with a sheath gas flow rate of 45 (arbitrary unit, arb.) and an
auxiliary gas flow rate of 20 (arb.). Spectra were acquired in the negative
ionization mode with a scan range of mass-to-charge ratio (m/z) of 50 to 750, with a
mass resolution of 140 000. Tandem mass spectrometry was performed using
higher-energy collision dissociation with a stepped normalized collision
energy of 10, 45, and 60. The isolation window was set to m/z 2.0 with a loop
count of 10, selecting the 10 most abundant species for fragmentation in
each scan.
A mass spectral library was built using the compound database function in
TraceFinder 4.1 General Quan software (Thermo Fisher Scientific, USA). To
build the library, compounds from previous studies
(Chan
et al., 2011; Nestorowicz et al., 2018; Ng et al., 2008; Riva et al., 2016b;
Schindelka et al., 2013; Surratt et al., 2008) were searched for in an
afternoon and a night-time filter sample extract analysis using the Xcalibur
software. Further details can be found in Bryant et al. (2021) and the Supplement.
Isoprene OS and NOS markers were quantified using authentic standards of
2-MG-OS and 2-methyl tetrol OS (2-MT-OS), with later-eluting monoterpene OS
and NOS quantified using camphorsulfonic acid. Standards were run across a
nine-point calibration curve (2 ppm–7.8 ppb, R2>0.99). More
details about the method can be found in Bryant et al. (2021). Overall
uncertainties associated with calibrations, proxy standards, and matrix
effects were estimated. The uncertainties associated with 2-MG-OS and
2-MT-OS were calculated to be 58.9 % and 37.6 %, respectively, mainly
due to the large uncertainties in the matrix correction factors. Isoprene
SOA markers quantified by the average of 2-MT-OS and 2-MG-OS calibrations
have an associated uncertainty of 69.9 %. For monoterpene SOA species
which were quantified by camphorsulfonic acid, the associated uncertainty is
estimated to be 24.8 %.
Supplementary measurements
A suite of complementary measurements were made alongside the filter
collection including
VOCs (Stewart et
al., 2021c), oxygenated VOCs, NOx, CO, O3, SO2, HONO,
photolysis rates, and measurements of PM1 non-refractory aerosol
chemical components with a high-resolution aerosol mass spectrometer
(HR-AMS). Detailed instrument descriptions can be found in Nelson et al. (2021). Briefly, VOCs and oxygenated VOCs were measured via two
gas chromatography (GC) instruments (DC-GS-FID and GC-GC-FID). NOx was
measured via a dual-channel chemiluminescence analyser fitted with a
blue-light converter for NO2 (Air Quality Designs Inc., Colorado)
alongside CO, which was measured with a resonance fluorescent instrument
(Model Al5002, Aerolaser GmbH, Germany). O3 was measured as outlined by
Squires et al. (2020) using an ozone analyser (49i, Thermo Scientific).
SO2 was measured using a 43i SO2 analyser (Thermo Scientific).
High-resolution aerosol mass spectrometry measurements were conducted as
outlined
in
Cash et al. (2021). Ion chromatography measurements were undertaken by the
experimental approach outlined by Xu et al. (2020) as part of an
intercomparison study. Briefly, filter cuttings were taken from the filter
and extracted ultrasonically for 30 min in 10 mL of ultrapure water and
then filtered before analysis
(Xu et al., 2020).
Meteorology data were downloaded from the NOAA Integrated Surface Database
via the worldmet R package for the Indira Gandhi International Airport
(code: 421810-99999) (Carslaw, 2021).
The planetary boundary layer height (PBLH) was obtained from the ERA5 (ECMWF
Reanalysis 5) data product at 0.25∘ resolution in 1 h time
steps at the position lat. 28.625∘, long. 77.25∘. The
data for both campaigns were then selected between the start time of the
first filter of that campaign and the end time of the last filter of the
same campaign.
ResultsMeteorology
The time series for temperature, RH, PBLH,
and ventilation coefficient (VC) across the pre- and post-monsoon campaigns
are shown in Fig. S1. For the pre-monsoon campaign, the average air
temperature was (35.8±4.5) ∘C compared to (24.7±4.6) ∘C in the post-monsoon campaign (Table S2). The pre-monsoon campaign
also showed higher average wind speeds, with an average of (3.8±1.4) m s-1, compared to (1.7±1.3) m s-1 in the post-monsoon
campaign. The average RHs of the pre- and post-monsoon were (39.4±13.6) % and (57.3±16.6) %, respectively, both showing similar
diurnals with a minimum around mid-morning and nocturnal maximum (Fig. S2). The PBLH shows a similar diurnal between the two campaigns, with the
nocturnal boundary layer breaking down around 06:00–07:00 IST with a midday
peak, before re-establishing the nocturnal boundary layer around 19:00. The
pre-monsoon PBLH has an average maximum of ∼2400 m compared
to post-monsoon ∼1700 m and a minimum of 270 m compared to 52 m (Fig. S2). The ventilation coefficient (VC = wind speed × PBLH) has
been used previously to identify periods of adverse meteorological
conditions and gives an idea of how stagnant atmospheric conditions are and
the general role of the atmosphere in the dilution of species
(Gani et al., 2019). As shown in
Fig. S1, the conditions during the post-monsoon campaign were much more
stagnant than the pre-monsoon campaign. The VC was on average 4.5 times
higher during the pre-monsoon campaign compared to the post-monsoon campaign
(Table S2), in line with previous studies
(Gani
et al., 2019; Saha et al., 2019). The more stagnant conditions during the
post-monsoon campaign likely trap nocturnal emissions and their reaction
products close to the surface, allowing for a significant build-up of
concentrations.
Gas-phase observations
Time series of the observed mixing ratios (ppbv) of NO, NO2, and O3
are shown in Fig. 1 for the pre- and post-monsoon campaigns. The campaign-averaged diurnal profiles are shown in Fig. S3, and the mean, median, and
maximum mixing ratios are given in Table S2. It should be noted that only
1 week of data was available for the pre-monsoon period. During the
post-monsoon campaign, extremely high mixing ratios of NO were observed with
a campaign maximum mixing ratio of ∼870 ppbv during the early
morning of 1 November. During the early part of the
pre-monsoon campaign, a large enhancement in NO was observed with mixing
ratios around 400 ppbv (Fig. S4), followed by significantly lower
concentrations throughout the rest of the campaign. The campaign-average NO
diurnal profile shows very high NO mixing ratios at night (pre-monsoon:
∼50 ppbv; post-monsoon: ∼300 ppbv), with low
afternoon mixing ratios <2 ppbv due to ozone titration. These high
NO concentrations at night likely reduce any night-time chemistry through
reactions with NO3 radicals and ozone. NO2 during the pre-monsoon
was observed to increase as the boundary layer reduced in the late
afternoon, with a mid-afternoon minimum. During the post-monsoon, a double
peak in concentrations was observed, in line with increasing ozone in the
morning and increasing NO in the afternoon. Ozone showed a strong diurnal
variation across both campaigns, with average afternoon mixing ratios
of ∼75 ppbv and pre- and post-monsoon maximums of 182 and
134 ppbv, respectively. Night-time O3 concentrations were significantly
higher during the pre-monsoon campaign, likely due to the significantly
lower NO concentrations.
Particle-phase observations
The sampling site was heavily polluted in terms of particulate matter. The
mean ±σ PM2.5 concentration (Table S2) during the
pre-monsoon campaign was (141±31) µgm-3 with a spike in
concentrations of 672 µgm-3 on 1 June 2018 at 21:00 IST (Fig. 1).
The diurnal (Fig. S5) shows concentrations generally flat throughout the
day. During the post-monsoon campaign, the average PM2.5 concentration
was higher at (182±94) µgm-3, with a spike in
concentrations of 695 µgm-3 at the end of the campaign (Fig. 1). The diurnal shows a mid-afternoon minimum with high morning and night
concentrations. HR-AMS was used to measure the PM1 sulfate and total
organics. Campaign-averaged total organic concentrations were approximately
double in the post-monsoon (48.7±35.4) µgm-3 compared to
the pre-monsoon (19.8±13.7) µgm-3. During the pre-monsoon
campaign, concentrations are generally flat throughout the day, with an
increase in the late afternoon, likely as the boundary layer decreases
(Fig. S5). During the post-monsoon, a much more prominent diurnal is
observed, with a midday minimum and high night-time concentrations. This
diurnal is likely driven by boundary layer conditions. Sulfate averaged (7.5±1.8) µgm-3 during the pre-monsoon campaign, with
slightly lower average concentrations observed in the post-monsoon: (5.6±2.7) µgm-3, as shown in Fig. S5. The sulfate diurnal
variations are similar to those of the organic aerosol.
Time series of pollutants across the pre- (a, c, e, g, i) and
post-monsoon (b, d, f, h, j) campaigns. During the pre-monsoon, NO
concentrations were filtered to below 60 ppbv, due to a large enhancement in
concentrations at the start of the campaign. The full time series is shown
in Fig. S4. NO, NO2, O3, and HR-AMS–SO42- were
averaged to 15 min. PM2.5 was measured hourly.
Isoprene and monoterpene measurements
Isoprene was measured hourly using gas chromatography with
flame ionization detection (GC-FID) across the two campaigns (Nelson et al.,
2021), with the time series shown in Fig. 2. The time series highlights
similar diurnal variability each day, driven by biogenic emissions. Figure 3
shows the average diurnal profiles of isoprene during pre-monsoon (a) and
post-monsoon (b). The mean isoprene mixing ratios were (1.22±1.28) and (0.93±0.65) ppbv, with maximum isoprene mixing ratios of
4.6 and 6.6 ppbv across the pre- and post-monsoon, respectively. This
is in the same range as measured in Beijing (winter mean: (1.21±1.03) ppbv; summer mean: (0.56±0.55) ppbv; Acton et al., 2020),
Guangzhou (year-round: 1.14 ppbv)
(Zou et al., 2019), and Taipei
(summer daytime: (1.26) ppbv; autumn daytime: (0.38) ppbv)
(Wang et al., 2013). The diurnal
variability observed in the pre-monsoon period corresponds to a typical
biogenic-emission-driven profile, with a rapid increase in isoprene around
05:00, reaching a peak around or after midday, before a nocturnal minimum.
Figure 3 indicates that average daytime peak isoprene mixing ratios during
the pre-monsoon campaign were roughly double that of the post-monsoon
campaign. In contrast, average nocturnal mixing ratios of isoprene were 5
times higher in the post-monsoon compared to the pre-monsoon ((0.65±0.43) versus (0.13±0.18) ppbv). In the post-monsoon campaign,
isoprene mixing ratios show a strong biogenic-emission-driven diurnal
profile at the start of the campaign. However, towards the end of the post-monsoon measurement period, the isoprene mixing ratios become less variable,
with a high mixing ratio maintained overnight (Fig. 2). This is
potentially due to more stagnant conditions, as observed by the VC in Fig. S1.
Time series across the pre- (a, c, e) and post-monsoon (b, d, f)
campaigns of isoprene (a, b), α-pinene (c, d), and limonene (d, e). The
vertical dotted lines represent midnight for each day.
A recent study in Delhi averaged across post-monsoon, summer, and winter
campaigns found that at vegetative sites biogenic isoprene contributed on
average 92 %–96 % to the total isoprene, while at traffic-dominated sites
only 30 %–39 % of isoprene was from biogenic sources
(Kashyap et
al., 2019). This is similar to the contributions of biogenic isoprene (40 %) to total isoprene mixing ratios at the traffic-dominated Marylebone
Road London
site (Khan et
al., 2018a). To gain some understanding of the sources of isoprene at our
site in Delhi, the observed concentrations of isoprene were correlated to
CO, which is an anthropogenic combustion tracer (Fig. 5), similar to
previous
studies (Khan
et al., 2018a; Wagner and Kuttler, 2014). The isoprene concentrations were
split between night and day (pre-monsoon night: 19:00–05:00 IST; pre-monsoon day: 05:00–19:00 IST; post-monsoon night: 17:00–06:00 IST; post-monsoon day: 06:00–17:00 IST), based
on the observed isoprene diurnals as shown in Fig. 3. Isoprene correlated
strongly with CO during the night across both campaigns (pre-monsoon:
R2=0.69; post-monsoon: R2=0.81), but no correlation was
observed during the day (R2<0.1). This suggests that daytime
isoprene is predominantly from biogenic sources, although a small amount
will be from anthropogenic sources, and that nocturnal isoprene is emitted
from anthropogenic sources, as seen in other locations
(Khan
et al., 2018b; Panopoulou et al., 2020; Wang et al., 2013). The night-time
isoprene mixing ratios (pre-monsoon: (0.13±0.18) ppbv; post-monsoon:
(0.65±0.43) ppbv) were substantially higher than measured previously
in Beijing and London (<50 pptv;
Bryant
et al., 2020; Khan et al., 2018b), but pre-monsoon concentrations were
similar to those observed at night in Taipei (0.19 ppbv) (Wang et al., 2013). The high
night-time concentrations during the post-monsoon period, towards the end of
October, are also likely influenced by the formation of a very low boundary
layer, trapping pollutants near the surface, affecting all species
similarly. An increase in biomass burning may also be a factor. Therefore,
during the post-monsoon campaign a significant number of isoprene oxidation
products will be of anthropogenic origin.
Diurnal variations across the pre- (a, c, e) and post-monsoon (b, d, f)
campaigns of isoprene (a, b), limonene (c, d), and α-pinene (e, f). The
shaded grey area represents the 95 % confidence interval. The shaded green area represents the times driven by biogenic emissions, as defined by
the isoprene diurnals.
Several monoterpenes were measured using a two-dimensional GC flame ionization detector (GCxGC-FID). The time series of two
monoterpenes, limonene and α-pinene, are shown in Fig. 2. The
α-pinene mixing ratio averaged (0.034±0.011) ppbv during the
pre-monsoon and (0.10±0.11) ppbv during the post-monsoon periods.
This is in comparison to limonene, which averaged (0.01±0.02) ppbv
and (0.42±0.51) ppbv across the pre- and post-monsoon campaigns,
respectively. A strong diurnal variation was observed for both monoterpenes
during the post-monsoon, peaking during the night (Fig. 3), with a midday
minimum. Nocturnal mixing ratios of the two monoterpenes were substantially
higher during the post-monsoon (limonene: (0.59±0.11) ppbv; α-pinene: (0.13±0.12) ppbv) than the pre-monsoon (limonene: (0.011±0.025) ppbv; α-pinene: (0.033±0.009) ppbv) period.
The diurnal variations across both campaigns are likely driven by both
emissions and boundary layer effects. The boundary layer effect
however is much stronger during the post-monsoon, with a shallower nocturnal
boundary layer; as such the post-monsoon period has a more pronounced
diurnal. Limonene was dominated by three short-lived spikes in concentrations
towards the start of the campaign (Fig. 2). α-pinene
concentrations generally increased during the morning, before decreasing
during the afternoon. Multiple monoterpenes were measured concurrently using
GCxGC-FID (Nelson
et al., 2021; Stewart et al., 2021c). For all MT species, the post-monsoon
period had higher mean mixing ratios, with large nocturnal enhancements in
mixing ratios. There are likely multiple factors leading the higher
concentrations during the post-monsoon, including accumulation due to
boundary layer effects, a lack of nocturnal radical chemistry, and an
increase in biomass burning (Jain et al., 2014). The average
isomeric speciation of the measured monoterpenes showed low variability
between daytime and night-time samples during each campaign, but significant
differences were observed between the campaigns (Fig. 4). Higher
contributions from limonene and β-ocimene were observed during the
post-monsoon compared to the pre-monsoon. The reason for the difference in
composition is likely due to differences in sources and/or sinks between the
two periods.
Average composition of monoterpenes across the pre-monsoon and
post-monsoon periods.
During the post-monsoon, α-pinene and limonene correlated strongly
with CO during the day (α-pinene: R2=0.82; limonene:
R2=0.90) and moderately at night (α-pinene: R2=0.49; limonene: R2=0.56), as shown in Fig. 5, suggesting
anthropogenic sources. Other potentially important anthropogenic monoterpene
sources include biomass burning, cooking, and the use of personal
care/volatile chemical products
(Coggon
et al., 2021; Gkatzelis et al., 2021; Hatch et al., 2019; Klein et al.,
2016). The shallow nocturnal boundary layers across both campaigns leads to
relatively high concentrations of total monoterpenes, with a maximum mixing
ratio of 6 ppbv observed during the post-monsoon
(Stewart et
al., 2021c). After sunrise, the expanding boundary layer dilutes the high
concentrations alongside increasing OH concentrations from photolytic
sources such as the photolysis of HONO and carbonyls, which likely causes a
rapid decrease in the monoterpene mixing ratios
(Lelieveld et al., 2016).
Correlations between isoprene, limonene, and α-pinene with
CO across the pre- (a, c, e) and post-monsoon (b, d, f) campaigns. The samples are
split between daytime (green) and night-time (black) as defined by the
isoprene diurnals in Fig. 3.
Time series across the pre- (a, c, e, g, i) and post-monsoon (b, d, f, h, j)
campaigns of the quantified SOA tracers: OSi (a, b), NOSi (c, d), OSMT(e, f), NOSMT(g, h), and the sum of all SOA tracers (i, j) with the
average campaign contributions. The vertical dotted lines represent midnight
for each day. Only species identified in more than 40 % of the samples
for each campaign were included.
Isoprene and monoterpene OS and NOS formation
At the measured concentrations, monoterpenes and isoprene are an important
source of ozone and OH reactivity at this site
(Nelson
et al., 2021). The resultant oxidized products will also be a key source of
SOA production. The UHPLC-MS2 analysis identified and quantified 75
potential markers across four classes of SOA: isoprene OS (OSi)- and NOS
(NOSi)-derived species and monoterpene OS (OSMT) and NOS (NOSMT)
species. Figure 6 shows the contribution to the total quantified SOA (qSOA),
which consists of the time-averaged sum of the four SOA classes (OSi, NOSi,
OSMT, NOSMT), across the pre- and post-monsoon campaigns. OSi
species were the dominant SOA class quantified in this study, contributing
75.6 % and 79.4 % of the qSOA across the pre- and post-monsoon
campaigns, respectively. NOSi species contributed significantly more to the
qSOA during the pre-monsoon (7.6 %) compared to the post-monsoon (2.1 %) period. Similar contributions from the monoterpene-derived SOA species
were observed across both campaigns.
Isoprene OS and NOS markers
OSi species are predominantly formed by photo-oxidation of isoprene by OH
radicals, with the subsequent products formed dependent on ambient NO
concentrations (Wennberg et al.,
2018). The pathways are split into high NO and low NO, although the NO
concentrations that constitute high and low are a sliding scale depending on
the amount of reactivity (defined as ([OH] ×kOH)
(Newland et al., 2021).
Under low-NO conditions, isoprene epoxydiol isomers (IEPOX)
(Paulot et al., 2009) are formed, which can then
undergo reactive uptake to the particle phase by acid-catalysed multiphase
chemistry involving inorganic sulfate to form 2-MT-OS
(Lin
et al., 2012; Riva et al., 2019; Surratt et al., 2010). Under high-NO
conditions, 2-methyl glyceric acid is the dominant gas-phase marker
produced, which can undergo reactive uptake to the particle phase to form
2-MG-OS
(Lin
et al., 2013b; Nguyen et al., 2015; Surratt et al., 2006, 2010).
A total of 21 potential OSi C2-5markers previously identified in
chamber studies
(Nguyen
et al., 2010; Riva et al., 2016c; Surratt et al., 2007, 2008) and other
ambient studies
(Bryant
et al., 2020; Budisulistiorini et al., 2015; Hettiyadura et al., 2019;
Kourtchev et al., 2016; Rattanavaraha et al., 2016; Wang et al., 2018,
2021b) were quantified in the collected ambient samples. It should be noted
that several of the smaller (C2-3) OSi tracers likely form from
glyoxal, methylglyoxal, and hydroxyacetone as well as isoprene, and as such
present a potential non-isoprene source of OSi
(Galloway
et al., 2009; Liao et al., 2015).
Molecular formulae, retention times (RTs), and time-weighted means (ng m-3) of organosulfate (OS) and nitrooxy organosulfate (NOS) species from
isoprene (i) and monoterpenes (MTs) observed across pre- and post-monsoon
campaigns in Delhi.
Figure 6 shows the time series of total OSi concentrations observed across
pre-monsoon (left, 5a) and post-monsoon (right, 5b) campaigns. Total OSi time-averaged concentrations (Table 1) were ca. 2.3 times higher during the
post-monsoon (∼556.6±422.5 ng m-3) campaign than
the pre-monsoon campaign (∼237.8±118.4 ng m-3).
These concentrations are similar to those observed in Beijing during summer
2017 (237.1 ng m-3;
Bryant et al.,
2020) but higher than those observed in Shanghai in 2018 (40.4 ng m-3) and 2019 (34.3 ng m-3)
(Wang et al., 2021b).
As previously discussed, OSi species have been shown to form via the
gas-phase photo-oxidation of isoprene, with the reactive uptake of the
oxidized species into the particulate phase via sulfate
(Lin et
al., 2013b; Surratt et al., 2010). Recently, a heterogeneous photo-oxidation
pathway from 2-MT-OS (C5H12O7S) to several OSi species was
proposed, including C5H10O7S, C5H8O7S,
C5H12O8S, C5H10O8S, and C4H8O7S
(Chen et al., 2020); 2-MT-OS showed
moderate correlations (pre-monsoon: R2=0.52–0.72; post-monsoon:
R2=0.14–0.35) with these OSi tracers that were lower than observed
in Beijing summer (R2=0.83–0.92)
(Bryant et al.,
2020). These correlations could suggest that this is a more common formation
route in pre-monsoon Delhi than in post-monsoon. However, the correlations
could also be driven by the common pathways between the OSi species, with
the reactive uptake of gas-phase intermediates via sulfate reactions. The
lower correlations during the post-monsoon could be due to increased
influences of anthropogenic sources coupled to the stagnant conditions.
Figure 7 shows the binned OSi concentrations for each filter collection time
across the pre- and post-monsoon campaigns to create a partial diurnal
profile. During the pre-monsoon, the daily variation in OSi concentrations
was much clearer, with daytime maxima and nocturnal minima, which are in
line with daily peak isoprene (Fig. 3) and OH radical concentrations. The
highest observed OSi concentrations during the pre-monsoon were
∼600 ng m-3, which occurred at the start of the
campaign. High isoprene concentrations may have been the cause, but
unfortunately isoprene measurements were not available during this period to
confirm. However, high OSi concentrations also occurred when particulate
inorganic sulfate concentrations were at their highest (Fig. S6), while
sulfate measured via the HR-AMS was also high during this period (Fig. 1).
During the post-monsoon, although a similar diurnal pattern was observed,
the variation was less pronounced, with higher OSi concentrations observed
at the start and end of the campaign (Fig. 6). Due to the secondary nature
of sulfate, the sulfate concentrations are less likely to be influenced by
the boundary layer effects, compared to directly emitted VOCs. The low OSi
concentrations during the middle of the campaign coincide with lower
isoprene and inorganic sulfate concentrations, but also low VC values,
suggesting more stagnant conditions.
The sum of OSi species across all filters sampled showed a variable
correlation with particulate sulfate across both campaigns. The pre-monsoon
correlation was similar to those observed in Beijing, Guangzhou, and the
SE US (R2:
0.55) (Bryant
et al., 2020, 2021; Budisulistiorini et al., 2015; Rattanavaraha et al.,
2016) while the post-monsoon was significantly weaker (R2: 0.28).
However, a clear relationship between OSi tracers and inorganic sulfate can
be seen in Fig. 8 across both campaigns, where the highest OSi
concentrations occurred under the highest particulate sulfate
concentrations. During the post-monsoon campaign, OSi concentrations
levelled off at high sulfate concentrations. In the pre-monsoon this
levelling-off is not observed, potentially due to the lower number of
samples. The high concentrations of organics measured by the HR-AMS (Table S2) during the post-monsoon ((48.7±35.4) µgm-3) compared to
the pre-monsoon ((19.8±13.7) µgm-3) suggests that the reactive
uptake of the gaseous OSi intermediates to the aerosol phase may be limited
due to extensive organic coatings on the sulfate aerosol. Multiple studies
have now shown that organic coatings on sulfate aerosol can limit the
reactive uptake of IEPOX, suggesting that the pre-monsoon is volume-limited, but
the post-monsoon is diffusion-limited
(Gaston
et al., 2014; Lin et al., 2014; Riva et al., 2016a).
Isoprene NOSs (NOSi's) have been shown to be produced by photo-oxidation in the
presence of NO and from NO3 oxidation chemistry
(Hamilton
et al., 2021; Ng et al., 2017; Surratt et al., 2008). A total of 10 different NOSi
tracers were screened for across the 2 campaigns, with 8 identified in
the pre-monsoon and 10 in the post-monsoon. These tracers included
mono-nitrated (C5H9O10NS, C5H11O9NS,
C5H11O8NS), di-nitrated (C5H10O11N2S),
and tri-nitrated (C5H9O13N3S) species. These tracers
have been identified previously in China
(Bryant
et al., 2020, 2021; Hamilton et al., 2021; Wang et al., 2018, 2021b).
Unlike the OSi tracers, total NOSi concentrations were on average higher
during the pre-monsoon (32.6±19.9) ng m-3 compared to the
post-monsoon (20.2±13.3) ng m-3. This is likely due to
extremely high night-time NO concentrations during the post-monsoon
quenching NO3 radicals, limiting the isoprene–NO3 pathway. The
NOSi time series and diurnal shown in Figs. 5 and 6, respectively, highlight
the strong nocturnal enhancements in concentrations during the pre-monsoon,
suggesting that the isoprene–NO3 formation pathway is dominant. Due to the
long sampling time, it is likely that these species are forming in the early
evening as NO3 oxidation becomes more competitive with OH, while
isoprene concentrations are still relatively high. During the post-monsoon,
NOSi concentrations were highest at night and the early morning. The high
morning concentrations could be due to non-local sources mixing down as the
shallow night-time boundary layer breaks down. Ideally, future work in Delhi
or India should focus on the measurements of radicals and OH reactivity
(kOH) in order to improve our understanding of the chemistry occurring
in extremely polluted environments. A large spike in NOSi
concentrations is observed at the start of the post-monsoon campaign, which
was not observed for the OSi tracers; this coincides with lower NO
concentrations than the rest of the post-monsoon campaign, reducing the
NO3 quenching by NO, allowing for more isoprene–NO3 oxidation.
The NOSi species did not correlate towards particulate sulfate (R2<0.2) across either campaign, suggesting that uptake onto sulfate
is not the limiting step in NOSi formation (unlike for the OSi species).
Partial diurnal variations from the binned concentrations of OSi,
NOSi, OSMT, and NOSMT concentrations at each filter collection time
across the pre- (a, c, e, g) and post-monsoon (b, d, f, h) campaigns. The lower and
upper parts of the box represent the 25th and 75th percentiles,
with the upper and lower lines extending no further than 1.5 times the
interquartile range of the highest and lowest values within the hinge,
respectively. Only species identified in more than 40 % of the samples
for each campaign were included.
Quantified SOA (OSi, NOSi, OSMT, NOSMT) vs. inorganic sulfate concentrations across the pre- (a, c, e, g) and post-monsoon (b, d, f, h) campaigns. The lower and upper parts of the box represent the
25th and 75th percentiles, with the upper and lower lines
extending no further than 1.5 times the interquartile range of the highest
and lowest values within the hinge, respectively. Only species identified in
more than 40 % of the samples for each campaign were included.
Monoterpene OS and NOS markers
Monoterpene-derived OS (OSMT) and NOS (NOSMT) markers have also
been identified from the oxidation by OH, NO3, and O3 in the
presence of SO2 or sulfate seed in simulation studies
(Brüggemann
et al., 2020a; Iinuma et al., 2007; Kleindienst et al., 2006; Surratt et
al., 2008; Zhao et al., 2018). Compared to isoprene, the ozonolysis of
monoterpenes is a key degradation pathway, with higher SOA yields from
ozonolysis observed when compared to isoprene
(Jonsson et al., 2005; Atkinson and Arey, 2003; Eddingsaas et al., 2012a,
b; Kristensen et al., 2013; Mutzel et al., 2016; Simon et al., 2020;
Zhao et al., 2015). A recent study in the SE US suggests that the
degradation of 80 % of monoterpenes at night is due to ozonolysis at that
location (Zhang et al.,
2018). Monoterpene-derived OS and NOS species have been extensively
observed, with ON contributing considerably to OA
(Lee
et al., 2016; Xu et al., 2015; Zhang et al., 2018). Recently NOS hydrolysis
has also been shown to be a potential formation route of OS particle-phase
species (Darer et al.,
2011; Passananti et al., 2016).
Twenty-three monoterpene-derived organosulfate (OSMT) species, which
have been seen previously in chamber (Surratt et al.,
2008) and ambient studies
(Brüggemann
et al., 2019; Wang et al., 2018, 2021b), were identified across the pre-
and post-monsoon campaigns. It should be noted that recently OSMT
artefacts have been shown to form when filters have been sampled without a
denuder (Brüggemann et al.,
2020a). However, the strong diurnal variations in the OSMT species and
lack of correlation with SO2 suggest this process is unlikely to have
contributed significantly to the OSMT measured in this study.
Post-monsoon concentrations were similar (3.96±1.6) ng m-3 to
the pre-monsoon (3.05±1.3) ng m-3, with C9H16O6S
the dominant species across both campaigns, contributing on average
∼29 % of the OSMT mass. C9H16O6S has
been observed in chamber studies
(Surratt et al., 2008) as well
as in ambient samples in Denmark, Shanghai, and Guangzhou
previously (Bryant
et al., 2021; Nguyen et al., 2014; Wang et al., 2017). It should be noted
that the majority of the OSMT species were not identified in every sample, and
as such only tracers which were identified in at least 40 % of the
samples were examined further.
Total OSMT showed a strong diurnal profile across both campaigns,
peaking at night, with an afternoon minimum (Figs. 5 and 6). During the
pre-monsoon campaign, the highest OSMT concentrations were observed
during a daytime sample, coinciding with peak sulfate and NO
concentrations. Both limonene and α-pinene also show peaks during
this filter sampling period of ∼0.05 ppbv. Spikes in limonene
and α-pinene concentrations were also observed on 31 May, but OSMT concentrations were much lower, likely due to the lower
sulfate concentrations. During the post-monsoon campaign, nocturnal
enhancements are observed (Fig. 7), suggesting MT - NO3 chemistry
is important. Like the NOSi markers, higher OSMT concentrations were
observed during the early morning sample, likely due to a lower PBLH
concentrating the markers coupled to MT–OH and MT–O3 occurring after sunrise
in the post-monsoon. The night-time formation of the OSMT species is in
line with previous studies (Bryant et al., 2021) and with the diurnal
variations in α-pinene and limonene, which peak at night. Previous
chamber studies investigating reactions of monoterpenes with NO3
radicals have also shown formation of OSMT with the same molecular
formulae as measured here
(Surratt et al., 2008).
OSMT concentrations observed in Delhi are much lower than those of the
OSi, similar to other studies
(Hettiyadura
et al., 2019; Wang et al., 2018, 2021b). Considering the high
concentrations of extremely reactive α-pinene and limonene observed
during the post-monsoon period, higher OSMT concentrations might be
expected. One possible reason for the low OSMT is the inability of
OSMT precursor species to undergo reactive uptake into the aerosol
phase under atmospherically relevant acidic conditions, with chamber studies
suggesting extremely acidic conditions are needed for uptake to occur
(Drozd et al., 2013). Delhi
is characterized by large concentrations of free ammonia and alkaline dust,
and previous studies have highlighted that it has less acidic aerosol (pH 5.7–6.7;
Kumar
et al., 2018) across the year than Beijing (pH 3.8–4.5;
Ding et al., 2019) and the SE US
(pH 1.6–1.9;
Rattanavaraha
et al., 2016).
Comparison of C10H17NO7S concentrations across
different locations. Locations and concentrations in bold were quantified
by authentic standards.
LocationC10H17NO7SReference(ng m-3)Delhi Pre-monsoon5.96This studyDelhi Post-monsoon13.36This studyGuangzhou summer7.15Bryant et al. (2021)Guangzhou winter11.11Bryant et al. (2021)Shanghai 15/166.21Wang et al. (2021b)Shanghai 16/175.55Wang et al. (2021b)Beijing12.00Wang et al. (2018)Atlanta9.00Hettiyadura et al. (2019)Hong Kong5.61Wang et al. (2021a)Guangzhou12.32Wang et al. (2021a)Shanghai16.51Wang et al. (2021a)Beijing13.15Wang et al. (2021a)
Unlike the OSMT species, the NOSMT species
(C10H17NO7S, C9H15NO8S,
C10H17NO9S, C9H15NO9S,
C10H17NO8S) showed strong seasonality, with pre- and
post-monsoon concentrations of (7.6±3.8) and (17.6±6.1) ng m-3, respectively. This is opposite to the quantified
NOSi species, which showed higher pre-monsoon concentrations. This is likely
due to much higher post-monsoon concentrations of monoterpenes. Of the
NOSMT species observed, C10H17NO7S was the most
abundant, contributing on average 79 % and 76 % of the NOSMT concentrations across the pre- and post-monsoon, respectively. Previous
studies have also highlighted that C10H17NO7S is the dominant
monoterpene-derived sulfate-containing tracer
(Wang et al., 2018). In
the post-monsoon nine C10H17NO7S isomers were observed, and
seven were observed in the pre-monsoon. The summed C10H17NO7S
concentrations during the pre- ((5.96±3.33) ng m-3) and
post-monsoon ((13.36±4.98) ng m-3) are of a similar magnitude to
those observed in other locations, as shown in Table 2. These concentrations
are also similar to those quantified by authentic standards across four
Chinese megacities (Wang et al., 2021a).
Like the OSMT species, some NOSMT species were not identified in
many of the filter samples, and as such tracers which were observed in more
than 40 % of the samples were summed for further analysis. The NOSMT
pre-monsoon time series (Fig. 6) shows a similar temporal profile to the
NOSi species, with lower concentrations during the enhancement in NO
concentrations (Fig. S4) at the start of the campaign. NOSMT showed
strong diurnal variations across both campaigns (Fig. 7), peaking at night,
with lower concentrations during the afternoon, as seen previously
(Bryant et
al., 2021; Wang et al., 2018). Therefore, the formation of NOSMT is
likely dominated by NO3 radical chemistry. Both NOSMT and
OSMT species showed limited correlation towards SO2 and
particulate sulfate (R2<0.1), indicating that although
sulfate is essential to their formation, sulfate availability does not
control NOSMT concentrations.
Contributions of total isoprene and monoterpene OS and NOS (qSOA) to
particulate mass
Particulate concentrations in Delhi are among the highest across the world
(World's most polluted cities (historical data 2017–2021): https://www.iqair.com/in-en/world-most-polluted-cities, last access: 22 December 2022), with concentrations over 600 µgm-3 being observed
during this study; qSOA, defined here as the sum of all OSi, NOSi,
OSMT, and NOSMT tracers quantified (including those not identified
in more than 40 % of the samples), was calculated to determine the total
contribution these species make to particulate mass in Delhi. Total oxidized
organic aerosol (OOA), a proxy for SOA in PM1, was derived from the
HR-AMS measurements during the pre- and post-monsoon campaigns, with
averages of (19.8±13.7) and (48.7±35.4) µgm-3, respectively; qSOA contributed on average (2.0±0.9) % and (1.8±1.4) % to the total OOA. Isoprene- and monoterpene-derived species contributed on average 83.2 % and 16.8 % of qSOA
across the pre-monsoon, respectively, compared to 81.5 % and 18.5 %
during the post-monsoon, respectively. During certain periods qSOA
contributed a maximum of 4.2 % and 6.6 % to OOA during the pre- and
post-monsoon, respectively. This is under the assumption that when the OS
and NOS species fragment in the AMS ion source they lose their sulfate and
nitrate groups. This is similar to the contributions made by OSi markers in
Beijing to total OOA (2.2 %)
(Bryant et al.,
2020). Previous studies in the SE US have reported much higher contributions
of isoprene species to total OA. As quantified by an aerosol chemical
speciation monitor, summed isoprene SOA tracers on average accounted for 9.4 % of
measured OA at Look Rock, downwind of Maryville and Knoxville, but up to a
maximum of 28.1 %
(Budisulistiorini
et al., 2015). This is lower than that measured at a rural site at
Yorkville, Georgia, with just low-NO isoprene SOA tracers accounting for
between 12 %–19 % of total OA
(Lin et al.,
2013a).
Sulfate was also measured in the PM1 size range by HR-AMS, with pre-
and post-monsoon mean concentrations of (7.5±1.8) and (5.5±2.7) µgm-3. The sulfate-containing OS and NOS
species quantified in this study may fragment in the AMS to produce a
sulfate signal which is not related to inorganic sulfate. To estimate the
contribution that sulfate contained within qSOA species could make to total
AMS sulfate, the quantified mass of sulfate contained within each marker was
calculated based on the fraction of sulfate to each marker of molecular mass.
For example, 2-MT-OS has an accurate mass of m/z 216.21, meaning the percentage
of 2-MT-OS mass associated with sulfate is ∼44 %. During
the pre-monsoon campaign the qSOA sulfate accounted for on average 2.2 %
of the total PM1 sulfate, but up to 4.8 % on certain days; qSOA
contributed considerably more to the sulfate in the post-monsoon campaign,
with an average of (6.1±4.5) % and a maximum of 18.7 %. This
finding indicates the need to consider the sources of particulate sulfate
measured by the AMS when calculating aerosol pH. The sulfate contribution
from the fragmentation of common small OS compounds (hydroxymethanesulfonate,
methanesulfonic acid) can be distinguished in the AMS using the relative
ratio of sulfur-containing fragments (Chen
et al., 2019). However, more work is needed to determine
how larger OS and NOS fragment in the AMS such as those quantified in this
study. Overall, this highlights that isoprene and MT oxidation can make
significant contributions to organic and sulfate-containing aerosol, even in
extremely polluted environments such as Delhi. It should be noted that this
is just a subset of potentially many more SOAs from isoprene and monoterpene
markers and only focusses on sulfate-containing species.
Conclusion
Isoprene- and monoterpene-derived organosulfate (OS) and nitrooxy
organosulfate (NOS) species were quantified during pre- and post-monsoon
measurement periods in the Indian megacity of Delhi. An extensive dataset of
supplementary measurements was obtained alongside filter samples, including
isoprene and speciated monoterpenes. Isoprene and monoterpene emissions were
found to be highly influenced by anthropogenic sources, with strong
correlations to anthropogenic tracers at night across both campaigns. High
nocturnal concentrations of pollutants were observed due to a low boundary
layer height and stagnant conditions, especially during the post-monsoon
period.
Isoprene OS markers (OSi) were observed in higher concentrations during the
post-monsoon ((557±423) ng m-3) compared to the pre-monsoon
campaign ((238±118) ng m-3). OSi showed a moderate correlation
with inorganic sulfate across both campaigns. However, concentrations
levelled off at high sulfate concentrations during the post-monsoon, which is
consistent with organic coatings limiting uptake of isoprene epoxides.
Isoprene NOS species (NOSi) showed nocturnal enhancements across both
campaigns, while the highest average concentrations were observed in the
morning samples of the post-monsoon campaign. The high morning
concentrations are likely due to the oxidation of VOCs by OH radicals from
photolytic processes throughout the morning. Monoterpene-derived OS
(OSMT) and NOS (NOSMT) markers were observed to have nocturnal
enhancements in concentrations, in line with their precursors. NOSMT
markers were observed in similar concentrations to those of other
megacities. Total quantified SOA contributed on average (2.0±0.9) % and (1.8±1.4) % to the total OOA. Considering high OOA
concentrations were observed across the two campaigns, the total markers
contributed up to a maximum of 4.2 % and 6.6 % across the pre- and
post-monsoon, respectively. Overall, this work highlights that even small
numbers of isoprene- and monoterpene-derived SOA markers can make significant
contributions to OA mass, even in highly polluted megacities.
Data availability
Data used in this study can be accessed from the CEDA archive: https://catalogue.ceda.ac.uk/uuid/ba27c1c6a03b450e9269f668566658ec (last access: May 2022; Nemitz
et al., 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-23-61-2023-supplement.
Author contributions
DJB prepared the manuscript with contributions from all authors. DJB, BSN,
SJS, SHB, WSD, ARV, JMC, WJFA, BL, EN, and JRH provided measurements and data
processing of pollutants used in this study. MJN and ARR contributed to
scientific discussion. S, RG, BRG, TM, and EN assisted with logistics. CNH,
JDL, ARR, and JFH provided overall guidance to the experimental set-up and
design.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors acknowledge Tuhin Mandal at CSIR National Physical Laboratory
for his support in facilitating the measurement sites used in this project
and Gareth Stewart for the VOC measurements. This work was supported by the
Newton Bhabha fund administered by the UK Natural Environment Research
Council through the DelhiFlux and ASAP projects of the Atmospheric Pollution
and Human Health in an Indian Megacity (APHH-India) programme. The authors
gratefully acknowledge the financial support provided by the UK Natural
Environment Research Council and the Earth System Science Organization,
Ministry of Earth Sciences, Government of India, under the Indo-UK Joint
Collaboration (DelhiFlux). Daniel J. Bryant and Beth S. Nelson acknowledge
the NERC SPHERES doctoral training programme for studentships. James M. Cash
is supported by a NERC E3 DTP studentship.
Financial support
This research has been supported by the Natural Environment Research Council
(grant nos. NE/P016502/1 and NE/P01643X/1) and the Government of India, Ministry
of Earth Sciences (DelhiFlux (grant no. MoES/16/19/2017/APHH)).
Review statement
This paper was edited by Ivan Kourtchev and reviewed by two anonymous referees.
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