Air pollution in urban environments has been shown to have a negative impact
on air quality and human health, particularly in megacities. Over recent
decades, Delhi, India, has suffered high atmospheric pollution, with
significant particulate matter (PM) concentrations as a result of
anthropogenic activities. Organic aerosols (OAs) are composed of thousands of
different chemical species and are one of the main constituents of submicron
particles. However, quantitative knowledge of OA composition, their sources
and their processes in urban environments is still limited. This is important
particularly in India, as Delhi is a massive, inhomogeneous conurbation,
where we would expect the apportionment and concentrations to vary
depending on where in Delhi the measurements/source apportionment is
performed, indicating the need for multisite measurements. This study
presents the first multisite analysis carried out in India over different
seasons, with a focus on identifying OA sources. The measurements were taken
during 2018 at two sites in Delhi, India. One site was located at the India
Meteorological Department, New Delhi (ND). The other site was located at the
Indira Gandhi Delhi Technical University for Women, Old Delhi (OD).
Non-refractory submicron aerosol (NR-PM1) concentrations (ammonium,
nitrate, sulfate, chloride and organic aerosols) of four aerosol mass
spectrometers were analysed. Collocated measurements of volatile organic compounds, black carbon,
NOx and CO were performed. Positive matrix factorisation (PMF) analysis
was performed to separate the organic fraction, identifying a number of
conventional factors: hydrocarbon-like OAs (HOAs) related to traffic
emissions, biomass burning OAs (BBOAs), cooking OAs (COAs) and secondary OAs
(SOAs).
A composition-based estimate of PM1 is defined by combining black carbon (BC) and
NR-PM1 (C-PM1= BC + NR-PM1). No significant difference was
observed in C-PM1 concentrations between sites, OD (142 ± 117 µg m-3) compared to ND (123 ± 71 µg m3),
from post-monsoon measurements. A wider variability was observed between
seasons, where pre-monsoon and monsoon showed C-PM1 concentrations lower
than 60 µg m-3. A seasonal variation in C-PM1 composition
was observed; SO42- showed a high contribution over pre-monsoon
and monsoon seasons, while NO3- and Cl- had a higher
contribution in winter and post-monsoon. The main primary aerosol source was
from traffic, which is consistent with the PMF analysis and Aethalometer
model analysis. Thus, in order to reduce PM1 concentrations in Delhi
through local emission controls, traffic emission control offers the
greatest opportunity. PMF–aerosol mass spectrometer (AMS) mass spectra will help to improve future
aerosol source apportionment studies. The information generated in this
study increases our understanding of PM1 composition and OA sources in
Delhi, India. Furthermore, the scientific findings provide significant
information to strengthen legislation that aims to improve air quality in
India.
Introduction
Air pollution in urban environments has been shown to have a negative impact
on human health, particularly in megacities. The World Health Organization
(WHO) stated that in 2016 about 90 % of the global population living in
urban environments was exposed to particulate matter concentrations
exceeding the WHO air quality guidelines (https://www.who.int/gho/phe/air_pollution_pm25_concentrations/en/, last access: 20 June 2019). According to
the WHO global air quality database in 2018, Delhi was ranked in 11th
position of the cities with high particulate mass, measured as the mass of
particles whose size is equal to or lower than 2.5 µm (PM2.5),
having an annual average concentration of 143 µg m-3. Other
Indian cities are in the top 20 with Varanasi being in first place with a
PM2.5 concentration of 217 µg m-3 (https://www.who.int/airpollution/data/cities/en/, last access: 17 June 2019).
Previous studies have identified ambient fine particle concentrations, particularly the submicron fraction
lower than or equal to 1 µm, to
have detrimental effects on health (PM1) (Pope et al., 2002; Ramgolam
et al., 2009).
Organic aerosols (OAs) constitute a large proportion of PM1 mass, with
fractions between 40 %–60 % in urban environments (Zhang et al., 2007).
OAs are composed of thousands of different chemical species and are difficult
to study due to a variety of sources and processes. Black carbon (BC),
another important component of PM1, is a product of incomplete
combustion and is recognised as one of the main climate-forcing components
(Bond and Bergstrom, 2006; Lack et al., 2014). Moreover, the WHO, in a
health effects of BC report, states that while there is no clear evidence
of BC directly affecting human health, it may work as a medium to
transport a wide variety of chemical species of varying toxicities
(Janssen et al., 2012). BC sources and concentrations have been
studied in India (Thamban et al., 2017; Jain et al., 2018a). In 2014,
Gupta et al. (2017) studied the seasonal variation in BC
in Agra, identifying biomass burning to be the major BC source, especially
during winter. The BC mixing state was measured in a clean site (Nainital)
and a polluted site (Gurgaon), with average BC concentrations of 1.0 and
11.0 µg m-3, respectively (Raatikainen
et al., 2017). In 2016, a BC network in India was initiated with 16
Aethalometers (Laskar et al., 2016); the availability of these
instruments allowed the study of BC sources in different environments
(Rajesh and Ramachandran, 2017; Kolhe et al., 2018; Nazeer Hussain et al.,
2018).
Aerosol mass spectrometer (AMS) instruments (Aerodyne Research, Billerica,
MA) have been widely used in different locations around the world to
quantify online real-time non-refractory PM1 (NR-PM1) components: OAs, nitrate
(NO3-), sulfate (SO42-), ammonium (NH4+) and
chloride (Cl-). In India, the first NR-PM1 measurements were taken
on campus at the Indian Institute of Technology (IIT) in Kanpur in winter
2011, where OAs were found to contribute 70 % to NR-PM1 concentrations
(Chakraborty et al., 2015). Subsequently, a number of studies
have been conducted at the same site showing dominance of the OA fraction
with substantial contributions from secondary sources
(Kumar et al., 2016) and looking at organo-nitrates and OA
sources (Chakraborty et al., 2016, 2018) from December 2015
to January 2016, where NR-PM1 measurements helped to analyse the
temporal characteristics of brown carbon (Satish et
al., 2017). Aircraft measurements during the pre-monsoon and monsoon periods
in 2016, covering the NE Bay of Bengal and Indo-Gangetic Plain (IGP) regions, allowed the vertical
and horizontal aerosol chemical composition to be studied
(Brooks et al., 2019). Recent
long-term measurements in Mahabaleshwar
(Mukherjee et al., 2018), Bhubaneswar
(Kompalli et al., 2020) and New Delhi
(Gani et al., 2019) have allowed the study of
NR-PM1 seasonal variability.
Receptor modelling tools, such as chemical mass balance, principal component
analysis and positive matrix factorisation (PMF), have been used in aerosol source apportionment
studies in India (Tiwari et al.,
2013; Bhuyan et al., 2018; Jain et al., 2018b; Gadi et al., 2019; Shivani et
al., 2019; Jain et al., 2020). PMF has been shown to be a useful tool to
determine OA sources from aerosol mass spectrometer measurements across the
Northern Hemisphere (Ng et al., 2010). In India, source apportionment
studies using PMF have identified different OA sources; for instance,
seasonal analysis in 2016–2017 in Mahabaleshwar, a high-altitude site,
identified hydrocarbon-like OAs (HOAs), biomass burning OAs (BBOAs) and two
different types of oxygenated OA (OOA) as the OA sources (Mukherjee et al., 2018; Singla
et al., 2019). Similar OA factors were identified in a recent study
performed in New Delhi, with the main OA sources being HOAs, BBOAs, and OOAs
from a 1-year analysis (Bhandari et al., 2020).
This work is part of the Atmospheric Pollution and Human Health in an Indian
Megacity (APHH-India) programme (https://www.urbanair-india.org/, last access: 5 March 2020). The main objective of the programme is
to support research on the sources and emissions of urban air pollution in
New Delhi, India; the processes underlying and impacting on these; and how
air pollution then impacts on health. In agreement with two of the working
packages of the programme (DelhiFlux and PROMOTE), the objectives in this
work are
to study the aerosol concentrations in a high time resolution in Old Delhi and New
Delhi through the year in two different locations
to identify the main OA sources and to determine the contribution of primary and
secondary OAs
to study the interaction of aerosol concentrations with local meteorology
to provide information to be used by the modelling community for
improvements in the emission inventories, air pollution forecast and source
apportionment.
MethodologySite description and meteorology
The measurements were taken during 2018 at two sites in Delhi, India. One
site was located at the Indian Meteorological Department (IMD), Mausam
Bhawan, Lodhi Road, New Delhi, lat 28.588, long 77.217, hereafter ND. The
other site was located at the Indira Gandhi Delhi Technical University for
Women (IGDTUW) at New Church Rd, Kashmere Gate, Old Delhi, lat 28.664, long 77.232, hereafter OD. Section S1 in the Supplement shows the
site locations.
The meteorology in India is highly influenced by the monsoon season, which
takes places between June and September and is characterised by an increase
in precipitation (Turner et al., 2019). Recent long-term measurements in
Mahabaleshwar (Mukherjee et al., 2018) and New Delhi (Gani et al., 2019)
have identified a clear pattern over the pre-monsoon, monsoon and post-monsoon seasons. Airborne measurements have been used to study the monsoon progression
across the Indo-Gangetic Plain region and its impacts on aerosol
characteristics (Brooks et al., 2019).
In the present study, temperature showed a wide range of variability; winter
was cool with an average temperature of 20 ∘C, whereas
pre-monsoon, monsoon and post-monsoon seasons were characterised by average
temperatures of 36, 31 and 25 ∘C, respectively (Fig. 1).
As expected, relative humidity (RH) showed the highest average over the monsoon season
(76 %) compared to the other seasons which had RH lower than 60 %. Similar
temperatures have been previously observed by
Brooks et al. (2019) with 31 ∘C pre-monsoon and 22 ∘C in the monsoon season as
well as by Gani et al. (2019) with
temperatures between 10–20 ∘C in winter, 25–40 ∘C pre-monsoon and 25–35 ∘C in the monsoon season.
Figure S3 in the Supplement shows box plots of temperature, wind speed and RH
for the different seasons.
Instrumentation
Four Aerodyne aerosol mass spectrometers were deployed across the different
seasons in 2018 (Table 1): one aerosol chemical speciation monitor (ACSM)
(Ng et al., 2011), one compact time-of-flight aerosol mass
spectrometer (cToF-AMS) (Drewnick et al., 2005) and two high-resolution
time-of-flight AMSs (HR-ToF-AMSs) (DeCarlo et al., 2006). The principle of
operation of aerosol mass spectrometer instruments has been widely explained
in previous publications; in general, these instruments quantify real-time
concentrations of NR-PM1 (OAs, NO3-, SO42-,
NH4+ and Cl-) by vaporising the aerosols at 600 ∘C with electron impact ionisation at 70 eV and final
detection in the mass spectrometer. The main difference between the
instruments used in this study is the mass spectrometer, with differences in
the sensitivity and the mass-to-charge (m/z) resolution. However, all are
reliable instruments and successful intercomparisons with parallel
measurements have been performed in previous studies (Ng et al.,
2011; Crenn et al., 2015). In this study, the cToF-AMS and the HR-AMS agree
to within 21 % from parallel ambient measurements in OD during the
pre-monsoon season (Fig. S7). Previous comparisons of AMS instruments have
shown agreement between instruments of between 19 % and 50 %, and 35 % is
widely recommended as the absolute accuracy of AMS instruments (DeCarlo
et al., 2008; Dunlea et al., 2009; Bahreini et al., 2009; Crenn et al.,
2015; Shinozuka et al., 2020). Section S2 shows the calibrations and quality
assurance analysis performed for the AMS measurements. The measurements in
PostM_OD_T and PostM_ND_T (see Table 1 for definitions) were taken from a height of 32 m from a tower (T). The
tower was used to perform eddy-covariance flux measurements, which will be
reported elsewhere. The rest were ground-based measurements (∼ 3 m high). All set-ups included a PM2.5 cyclone to cut particle size
and a drier to reduce humidity. The AMS measurements during the Diwali
festival (7 November) for the period PostM_ND_T_C were lost due to a power cut; thus this
special episode was removed from the NR-PM1 analysis.
Measurement dates and instrumentation. Winter (Win), pre-monsoon
(PreM), monsoon (Mon) and post-monsoon (PostM). New Delhi (ND) and Old Delhi
(OD) sites. ACSM (A), two HR-ToF-AMS instruments (H1 and H2),
cToF-AMS (C). Eddy-covariance flux measurements tower (T).
Box plots with (a) planetary boundary layer height (PBLH), (b) temperature, (c) relative humidity and (d) wind speed for the different
seasons. The marker represents the mean.
Other instruments used along with the mass spectrometers include two
Aethalometers (Magee Scientific, one model AE31 and one model AE33) and one
multi-angle absorption photometer (MAAP; Thermo Fisher Scientific)
(Petzold et al., 2002). Table 1 shows details about the instrument
locations and sampling periods, and Table S1 presents the instruments collocated with the mass spectrometers. Meteorology data, apart from
planetary boundary layer height (PBLH), were downloaded from
https://ncdc.noaa.gov/ (last access: 5 January 2019) at 1-hourly resolution.
PBLH data were sourced from ECMWF ERA5 0.25∘ results at a 1 h resolution.
PMF (Paatero et al., 2002) was used to identify potential
sources of OAs, based on the source finder tool, SoFi 4.8 (Canonaco et
al., 2013). Section S3 in the Supplement provides detailed information about the
criteria to select the OA factors. The PMF analysis of the OD_H2 measurements presented in this work was performed by
Cash et al. (2021) for the complete
dataset (26 May–20 November) using high-resolution organic mass spectra. The PMF
analysis of the PreM_ND_H1 dataset was
performed with unit mass resolution data. BC concentrations measured with the
Aethalometer AE-31 were corrected following the Weingartner method
(Weingartner et al., 2003) and using the SP2 (single particle soot photometer) as a reference BC
measurement. The Aethalometer model was applied, following the Sandradewi
method (Sandradewi et al., 2008), to Aethalometer
measurements, both AE-31 and AE-33, to identify the contribution of biomass
burning and fossil fuel to BC concentrations. Section S4 in the Supplement
shows the BC data processing.
Average aerosol concentrations (a) and relative contribution to
C-PM1(b).
ResultsAerosol concentrations and composition
Aerosol measurements were taken in OD and ND over different seasons in 2018.
The highest concentrations were observed in the post-monsoon season, where
OA concentrations of 400 µg m-3 were found (Fig. S8). Lower
aerosol concentrations were observed in the monsoon season, where OAs reached
between 80–200 µg m-3. Similarly to
Gani et al. (2019), we combine BC and
NR-PM1 concentrations to develop a composition-based estimate of
PM1 (C-PM1= BC + NR-PM1). The season with the highest
averaged C-PM1 concentrations was observed in PostM-OD, with average
concentrations (± SD) of 142 ± 117 µg m-3 in
PostM_OD_T_H2 (Fig. 2a). Lower
aerosol concentrations were observed in pre-monsoon and seasons with
C-PM1 average concentrations lower than 40 µg m-3 in
PreM_ND_H1 and Mon_OD_H2, with OAs being the component with the leading
contributor to PM1.
Measurements during the post-monsoon season were taken in OD (PostM_OD_T_H2) and ND (PostM_ND_T_C) at the same time (6–20 November). This
comparison shows average C-PM1 concentrations in OD were slightly
higher (142 ± 117 µg m-3) compared to ND (123 ± 71 µg m-3). It is worth mentioning that, while removing the Diwali
festival as a special event, the measurements shown in this work still
include high concentrations from festivities that took place before and
after the Diwali festival on 7 November. The ND site is located in an
affluent residential green area, where the transit of heavy good vehicles is
controlled; thus, it could be expected that the use of fireworks is also
restricted here, whereas in OD, there is less control of the firework
activities. While no significant differences were observed when comparing
between sites, a clear difference between seasons was observed; the pre-monsoon
and monsoon seasons had similar C-PM1 concentrations lower than 60 µg m-3, while post-monsoon C-PM1 concentrations were higher than
120 µg m-3. Detailed statistical analysis for NR-PM1 and BC
is presented in Table S3.
OAs make the main relative contribution to PM1 with values of 0.45–0.62
followed by BC with values of 0.13–0.24 (Fig. 2b). This high relative
contribution of OAs has been observed in previous studies in India
(Bhandari et al., 2020; Singla et al., 2019). The high BC contributions of
around 18 % suggest an important contribution from primary sources.
Sulfate made a higher contribution in pre-monsoon and monsoon seasons, while
NO3- and Cl- had a higher contribution in winter and
post-monsoon, similarly to a previous study performed in Delhi
(Gani et al., 2019).
This wide range of concentrations is related to the meteorological
conditions (Fig. 1), mainly with boundary layer height as a result of
changes in temperature; winter and PostM-T had a temperature of
20–25 ∘C, resulting in wind speeds of 2.5–3.0 m s-1,
which may have resulted in a more stable mixing layer, accumulating aerosol
concentrations. In the early pre-monsoon season, an average temperature of
35 ∘C with wind speeds of 3.5 m s-1 facilitated
greater aerosol dispersion. From the diurnal cycles (Fig. 3), it can be seen
that NO3- and Cl- showed high concentrations during the
morning while BC and OAs showed high concentrations at night. SO42-
showed less diurnal variability, suggesting a regional origin with large
sources at midday when the photochemistry takes place compensating for the
boundary layer effect. BC is an aerosol constituent related to primary
emissions, fossil fuel (mainly traffic) and biomass burning
(Sandradewi et al., 2008); hence the fact that OA had
a diurnal pattern similar to BC during the post-monsoon season suggests OAs
were potentially dominated by primary sources, while in the pre-monsoon
season, OAs showed an increase in concentrations during the day, suggesting a
larger contribution from secondary OAs (Shrivastava et al., 2017). This
primary–secondary origin of OAs will be analysed in the next section.
Median diurnal concentrations of the aerosol chemical components.
Source apportionment of the organic aerosols
Organic aerosols are composed of thousands of chemical species, making source
attribution challenging. Methods using receptor modelling tools such as PMF
are often employed to deconvolve OAs into various OA factors that, depending
on their composition and temporal variations, can be attributed to potential
OA sources. In this work a number of factors were identified with PMF
analysis (Fig. 4); three common factors were found in all the seasons,
hydrocarbon-like OAs (HOAs), biomass burning OAs (BBOAs) and more oxidised
oxygenated OAs (MO-OOAs). Other identified sources were cooking OAs (COAs), less
oxidised oxygenated OAs (LO-OOAs) and oxygenated primary OAs (oPOAs). HOAs are
related to fossil fuel emissions, mainly attributed to traffic, whose mass
spectrum is characterised by peaks at a mass-to-charge ratio (m/z) of 55 and 57
and which are dominated by the CxHy+ family
(Zhang et al., 2005). BBOAs are characterised by m/z 60
(C2H4O+) which is related to anhydrosugar fragments, such as
levoglucosan (Alfarra et al., 2007). The mass spectrum of COAs presents
peaks at m/z 55 and 57, similarly to the HOA spectrum but with a lower
peak at m/z 57 (Allan et al., 2010; Mohr et al., 2012). The two oxygenated
OA (OOA) factors identified MO-OOAs and LO-OOAs. MO-OOAs have a dominant peak
at m/z 44 (CO2+), and LO-OOAs typically have a peak at m/z 44 and
a larger peak at m/z 43 (mostly C2H3+) compared to MO-OOA
(Zhang et al., 2011).
The HOA mass spectrum was consistent across the collection periods and
between the different instruments deployed, with peaks at m/z ratios of 41, 43, 55 and
57, and its diurnal cycle showed high concentrations in the morning and at
night resulting from the traffic rush hour. Moreover, HOAs showed their highest
concentrations during periods of low wind speeds (Fig. S19) and shallow
boundary layers, highlighting the importance of HOA as a primary local
source: even a small local source emitting into a shallow nocturnal
boundary-layer can result in significant accumulation of concentrations. The
MO-OOA mass spectrum is also consistent over all the seasons with peaks at
m/z 18 and m/z 44; its diurnal cycle shows the highest concentrations to
have been at around 10:00 to 15:00 (all times are in Indian standard time), suggesting a relation to the
stronger radiation occurring at that time, resulting in secondary aerosol
production from photochemistry. We can observe larger variability in the
BBOA mass spectra, with changes to the peak at m/z 60, which has a higher
relative intensity over the post-monsoon and winter seasons. oPOAs, which
show peaks at m/z at 43 and m/z 44, have a characteristic diurnal pattern
with high concentrations around 09:00. While the mass spectrum resembles
that of OOAs, the diurnal cycle behaved differently to those of LO-OOAs and
MO-OOAs. A more detailed analysis about the possible origin of oPOAs will be provided
in the next section. COAs were identified over the pre-monsoon and post-monsoon seasons but not in PostM_ND_T or during winter.
Moreover, previous studies in urban environments have identified a lunch
peak in the diurnal plots (Allan et al., 2010; Young et al., 2015).
However, this peak was not clearly observed in any of the seasons where COAs
were identified. Perhaps, the high OA concentrations resulting from the other
anthropogenic emissions, i.e. traffic and biomass burning along with the
boundary layer effect, would favour the mixing of OA sources and PMF would
struggle to completely separate the OA sources (Lanz et al., 2008).
Mass spectra and diurnal plots of OA factors from PMF analysis in
the different periods. Here the period all_OD_H2 is displayed to compare with the other periods. The period
all_OD_H2, which shows the high-resolution PMF
analysis performed with the complete dataset of the AMS instruments (H2) 26 May–23 November 2018, is analysed in detail by
Cash et al. (2021). Detailed analysis
of the all_OD_H2 dataset is presented in Table S4, where the PMF analysis identified seven factors, adding HOA = HOA + nHOA and BBOA = SFOA + SVBBOA to compare with our five-factor
solutions.
DiscussionThe role of meteorology in aerosol concentrations
The meteorology in India has been summarised in Sect. 2.1, stating the
impact of the monsoon season and the difference between pre-monsoon and
post-monsoon. This work allowed the study of NR-PM1 seasonal
variability, which showed strong variations through the seasons (Fig. 2),
where a seasonal cycle was observed with high aerosol concentrations
post-monsoon and in winter with low temperatures. From the previous diurnal
plots in winter_ND (Fig. 3), high Cl- concentrations
were observed in the morning. PMF analysis performed on this dataset also
identified a factor referred to as oPOAs with a morning peak (Fig. 4a).
Figure 5 shows the diurnal trends of Cl- and oPOAs along with
meteorology parameters. Both Cl- and oPOAs peak at around 09:00, at the
time when RH shows its highest value, and their concentrations start
decreasing when the PBLH starts increasing at around 10:00. This decrease
in Cl- and oPOA concentrations is perhaps due to mixing with fresh air
masses from the break-up of the boundary layer; moreover, it is likely that
the Cl- reduction is due to some repartitioning into the gas phase as
temperature increases. NH4Cl has a higher vapour pressure than
NH4NO3 and will dissociate first. This is also consistent with the
decrease on Cl- concentrations before NO3- concentrations increase
in the morning. The same high Cl- concentrations were observed by
Chakraborty et al. (2018) in NR-PM1
measurements taken during September–October 2014 in Kanpur, India, where similar
meteorological conditions were present. This indicates the impact RH, PBLH
and temperature have on Cl- and oPOA concentrations. Figure S20 shows
Cl- and oPOA have different sources; it can be observed that Cl-
follows the trend of NH4+, which is expected as the AMS is
sensitive mainly to NH4Cl but not to other Cl- compounds, and oPOAs
follow the same pattern as SO42-. It is possible that the
properties of the condensed material in oPOAs may be similar to the assumed
NH4Cl, even though the sources are different; i.e. both have a similar
volatility. A more detailed analysis on the Cl- processes is performed
by Gunthe et al. (2021), where the ACSM_Winter data are part of the analysed datasets.
Diurnal median values of meteorological parameters, Cl- and
oPOA concentrations in Winter_ND.
Organic aerosol sources and their contribution to NR-PM1
Seasonal dependencies of the PMF factors can be investigated by looking at
their average contributions (Fig. 6). HOAs comprise the factor with the highest
concentration with 6.0–55.0 µg m-3 for pre-monsoon and
post-monsoon, respectively, in the PostM_OD_H2
dataset (Table S4), HOA concentrations of 30 µg m-3 were
identified. These HOA concentrations represent a relative contribution to OA
sources of 20 % to 50 %. Traditionally, HOAs have been related mainly to
traffic emissions of vehicles using petrol or diesel (Zhang et al.,
2005; Platt et al., 2017). However, in Delhi, compressed natural gas vehicles
and generators might also contribute to HOA concentrations (Prakash et
al., 2020). It is interesting to see that BBOAs have low concentrations (2.0–8.0 µg m-3) in the ND and pre-monsoon seasons with a low
contribution to OA sources (0.10–0.15). However,
Cash et al. (2021), in the
PostM_OD_T_H2 analysis,
identified a BBOA average concentration of 48.22 µg m-3,
representing a relative contribution of 25 %. COAs are an important
primary source in OD, with average concentrations of 2.0–12.0 µg m-3. It is worth mentioning that COA concentrations might be
overestimated due to a potentially higher relative ionisation efficiency,
which has been observed previously in a laboratory experiment
(Reyes-Villegas et al., 2018). Considering HOAs, BBOAs and COAs as
primary OAs (POAs) and the rest of the sources as OOAs and looking at the POA
relative contribution to OA concentrations (Fig. 6d), there is a more
constant concentration in ND with values between 54 %–56 %, while POA
relative contributions in OD range between 46 %–66 %. These POA relative
contributions close to 50 % over different times of the year have been
previously observed in a study performed in 2017 at IIT Delhi, New Delhi
(Bhandari et al., 2020), with similar average
concentrations for POAs and OOAs (52 and 56 µg m-3, respectively, for winter as
well as 30 and 31 mug m-3, respectively, for pre-monsoon). Cash et al. (2021)
identified POA relative contributions of 45 % and 52 % for pre-monsoon
and monsoon and 64 % for post-monsoon, showing the importance of POA
concentrations in OD during this latter season. OOA concentrations are produced by
both POAs and precursor gas. In other urban environments, it is possible to
see a more variable POA / OOA ratio contribution to OA concentrations
(Jimenez et al., 2009). For instance, in London, UK, in 2012 POA
contributions ranged from 80 % in winter to 45 % in summer
(Young et al., 2015), perhaps as a result of
the lower temperatures during winter compared to summer along with less
photochemistry in winter at higher altitudes. These findings demonstrate the
impact primary PM1 emissions in Delhi have on OA concentrations over
the year.
OA average concentrations (a) and relative contribution (b).
The whiskers represent the standard deviation of the POA / OOA contribution after
running PMF with different F peak values between -1 to 1 with steps of 0.2 to prove
the stability of the separation.
Fossil fuel and biomass burning sources of BC
The high contribution of POA has been explained in Sect. 4.2, and more
specifically, traffic was identified as an important OA source. This finding
is supported by the Aethalometer model analysis. This model based on the
Sandradewi method (Sandradewi et al., 2008) uses the
absorption coefficient (babs) and the absorption Ångström exponent to
apportion the contribution of fossil fuel (babs_950ff)
and biomass burning (babs_470bb) to Aethalometer
measurements (refer to Sect. S4 in the Supplement for details). Figure 7
shows the mean absorption coefficients for fossil fuel and biomass burning,
where high babs_950ff values compared to
babs_470bb are observed. Pre-monsoon and monsoon are the
seasons with low biomass burning influence, with average values between 8–20 Mm-1; during these seasons, fossil fuel showed babs values
between 35 and 50 Mm-1. Post-monsoon the influence of biomass
burning starts to increase with average values around 50 Mm-1. However,
at this time, traffic showed higher average values between 100 and 150 Mm-1. In winter, biomass burning has an important influence, with an
average of 80 Mm-1, while fossil fuel still has a major influence with
a value of 95 Mm-1. It is only during the Diwali festival, from
OD-PostMon (Fig. S16) where biomass burning makes a significant contribution to
BC (700 Mm-1). However, during this period, there is an important
contribution of BC from fossil fuel (500 Mm-1), showing the importance
of fossil fuel as a BC source.
Conclusions
Aerosol composition was studied to compare Old Delhi (OD) and New Delhi (ND)
over different seasons in 2018 with a focus on source apportionment. No
significant difference was observed in C-PM1 concentrations (defined as
total AMS mass plus black carbon, BC) from post-monsoon measurements between
sites: OD (142 ± 117 µg m-3) compared to ND (123 ± 71 µg m-3). A wider variability was observed between seasons, where the
pre-monsoon and monsoon seasons showed average C-PM1 concentrations
lower than 60 µg m-3. The fact that there is no remarkable
difference in C-PM1 concentrations in the two sites suggests that,
when aiming to control C-PM1 high concentrations, the actions should be
region orientated, for example considering the Delhi region, rather than considering
controlling air pollution in OD or ND only. OD presented high
SO42- concentrations (7.0 µg m-3 in OD and 3.4 µg m-3 in ND). In the post-monsoon and winter seasons, the C-PM1
mass was dominated by organic matter (OAs) rather than inorganic, and the
largest single contributor was hydrocarbon-like organic aerosols (HOAs).
NH4+, NO3- and Cl- peaked in the morning, while OAs
peaked at night. A seasonal variation in PM1 composition was observed;
SO42- showed a high contribution over pre-monsoon and monsoon
seasons, while (more volatile) NO3- and Cl- made a larger
contribution in the cooler seasons, winter and post-monsoon. The fact that
no remarkable difference between sites was found suggests the air pollution
in Delhi is a regional problem that is influenced by meteorological
conditions over the year, with the post-monsoon and winter seasons being the seasons with the
highest pollutant concentrations.
Average absorption coefficients for fossil fuel
(babs_950ff) and biomass burning (babs_470bb). The whiskers represent ± 1 standard deviation.
Analysis of the organic mass spectra by positive matrix factorisation (PMF)
identified a number of conventional factors: HOAs, biomass burning OAs (BBOAs),
cooking-like OAs (COAs), less oxidised oxygenated OAs (LO-OOAs) and more
oxidised oxygenated OAs (MO-OOAs). One additional factor (oPOAs) had a
particular diurnal trend, similar to that of Cl-, and a mass spectral signature
similar to that of OOAs. However, from examination of the polar plots (Fig. S20),
oPOAs appear to have similar source sectors to those of SO42-. This
suggests oPOA may be semi-volatile and driven by changes in T and RH, like
Cl-, whilst having different sources, undetermined at this time.
Traffic has been shown to be the main primary aerosol source for both OAs and BC, as
seen with the PMF analysis (HOA) and Aethalometer model analysis
(babs_950ff). The measurements indicate that in Delhi
further control of primary traffic exhaust emissions would make a
significant contribution to reducing PM1 concentrations. Moreover, by
controlling gas precursor emissions, OOA concentrations may also be reduced.
Primary organic aerosols (POAs) made a relatively constant contribution at the
different sites and seasons with a relative contribution of around 50 % to
OAs. This shows both the importance of primary sources and the fact that
daily average temperatures remain above 20 ∘C throughout
the year; 20 ∘C in winter and 43 ∘C pre-monsoon, compared, for example, with European urban environments where
average temperatures can drop to 5 ∘C or lower in winter
and summer temperatures are around 25–30 ∘C. This
temperature might increase the regional pollutant transport allowing an even
POA–SOA distribution over the Delhi region. When comparing these results
with Cash et al. (2021), it shows the
importance POA has in OD during the post-monsoon season, where a POA
relative contribution of 63 % is observed. Meteorology played an
important role in aerosol concentrations. High concentrations of Cl-
and oPOA were observed in winter_ND which was characterised
by high RH, with a sudden drop in concentrations when the boundary layer
broke up in the morning and temperatures rose. This Cl- peak has been
observed in a previous post-monsoon study in Kanpur, India (Chakraborty et
al., 2018; Gadi et al., 2019). More detailed research should be carried out in order
to identify the potential sources/processes that caused the peak in OA
concentrations around 09:00–10:00 in urban Indian environments, which has been
observed taking place during the post-monsoon and winter seasons.
The information generated in this study increases our understanding of
PM1 composition and OA sources in Delhi, India. The PMF-AMS mass
spectra can be used in future source apportionment studies in India and
perhaps other urban environments to improve the identification of sources
and provides a unique dataset for the assessment of atmospheric chemistry
and transport models. Furthermore, the scientific findings provide
significant information to target legislation that aim to improve air
quality in India.
Data availability
AMS data used in this paper have been archived at
https://catalogue.ceda.ac.uk/uuid/b7c9aeb6aee54698aef82f20365fc441 (Reyes Villegas et al., 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-11655-2021-supplement.
Author contributions
ERV, RJ and JDA measured PM1 using a cToF-AMS along with black carbon using
the Aethalometer AE31 and the SP2. ED, MF and AV measured PM1 using
a HR-ToF-AMS along with black carbon using the Aethalometer AE31. UP, SSG, ED,
and JDA measured PM1 using an ACSM along with black carbon using the
Aethalometer AE33. JMC, BL, CDM, NJM, S and EN measured PM1 using a HR-ToF-AMS.
MSA and LRC collected the 12 h samples in quartz filters for ion chromatography (IC) analysis.
DJR performed the IC analysis. TKM, BRG, JL, CNH, EN and JDA conceived the
Delhi-Flux project. All authors contributed to the discussion, writing and
editing of the article.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Financial support
This research has been supported by the UK NERC
through the DelhiFlux and PROMOTE projects under the Newton Bhabha Fund programme “Air Pollution and Human Health in a Developing Megacity (APHH-India)”, NERC reference numbers NE/P016502/1, NE/P01643X/1, NE/P016472/1 and
NE/P016480/1. The monsoon measurements were supported by the NERC National Capability award SUNRISE (NE/R000131/1).
James Cash is recipient of a NERC E3 DTP studentship (NE/L002558/1).
Review statement
This paper was edited by Nga Lee Ng and reviewed by two anonymous referees.
ReferencesAlfarra, M. R., Prevot, A. S. H., Szidat, S., Sandradewi, J., Weimer, S.,
Lanz, V. A., Schreiber, D., Mohr, M., and Baltensperger, U.: Identification
of the mass spectral signature of organic aerosols from wood burning
emissions, Environ. Sci. Technol., 41, 5770–5777, 10.1021/Es062289b, 2007.Allan, J. D., Williams, P. I., Morgan, W. T., Martin, C. L., Flynn, M. J., Lee, J., Nemitz, E., Phillips, G. J., Gallagher, M. W., and Coe, H.: Contributions from transport, solid fuel burning and cooking to primary organic aerosols in two UK cities, Atmos. Chem. Phys., 10, 647–668, 10.5194/acp-10-647-2010, 2010.Bahreini, R., Ervens, B., Middlebrook, A. M., Warneke, C., de Gouw, J. A.,
DeCarlo, P. F., Jimenez, J. L., Brock, C. A., Neuman, J. A., Ryerson, T. B.,
Stark, H., Atlas, E., Brioude, J., Fried, A., Holloway, J. S., Peischl, J.,
Richter, D., Walega, J., Weibring, P., Wollny, A. G., and Fehsenfeld, F. C.:
Organic aerosol formation in urban and industrial plumes near Houston and
Dallas, Texas, J. Geophys. Res.-Atmos., 114, D00F16,
10.1029/2008JD011493, 2009.Bhandari, S., Gani, S., Patel, K., Wang, D. S., Soni, P., Arub, Z., Habib, G., Apte, J. S., and Hildebrandt Ruiz, L.: Sources and atmospheric dynamics of organic aerosol in New Delhi, India: insights from receptor modeling, Atmos. Chem. Phys., 20, 735–752, 10.5194/acp-20-735-2020, 2020.Bhuyan, P., Deka, P., Prakash, A., Balachandran, S., and Hoque, R. R.:
Chemical characterization and source apportionment of aerosol over mid
Brahmaputra Valley, India, Environ. Pollut., 234, 997–1010,
10.1016/j.envpol.2017.12.009, 2018.Bond, T. C. and Bergstrom, R. W.: Light absorption by carbonaceous
particles: An investigative review, Aerosol Sci. Technol., 40,
27–67, 10.1080/02786820500421521, 2006.Brooks, J., Allan, J. D., Williams, P. I., Liu, D., Fox, C., Haywood, J.,
Langridge, J. M., Highwood, E. J., Kompalli, S. K., O'Sullivan, D., Babu, S.
S., Satheesh, S. K., Turner, A. G., and Coe, H.: Vertical and horizontal
distribution of submicron aerosol chemical composition and physical
characteristics across northern India during pre-monsoon and monsoon
seasons, Atmos. Chem. Phys., 19, 5615–5634, 10.5194/acp-19-5615-2019, 2019.Canonaco, F., Crippa, M., Slowik, J. G., Baltensperger, U., and Prevot, A.
S. H.: SoFi, an IGOR-based interface for the efficient use of the
generalized multilinear engine (ME-2) for the source apportionment: ME-2
application to aerosol mass spectrometer data, Atmos. Meas. Tech., 6,
3649–3661, 10.5194/amt-6-3649-2013, 2013.Cash, J. M., Langford, B., Di Marco, C., Mullinger, N. J., Allan, J., Reyes-Villegas, E., Joshi, R., Heal, M. R., Acton, W. J. F., Hewitt, C. N., Misztal, P. K., Drysdale, W., Mandal, T. K., Shivani, Gadi, R., Gurjar, B. R., and Nemitz, E.: Seasonal analysis of submicron aerosol in Old Delhi using high-resolution aerosol mass spectrometry: chemical characterisation, source apportionment and new marker identification, Atmos. Chem. Phys., 21, 10133–10158, 10.5194/acp-21-10133-2021, 2021.Chakraborty, A., Bhattu, D., Gupta, T., Tripathi, S. N., and Canagaratna, M.
R.: Real-time measurements of ambient aerosols in a polluted Indian city:
Sources, characteristics, and processing of organic aerosols during foggy
and nonfoggy periods, J. Geophys. Res.-Atmos., 120,
9006–9019, 10.1002/2015jd023419, 2015.Chakraborty, A., Gupta, T., and Tripathi, S. N.: Chemical composition and
characteristics of ambient aerosols and rainwater residues during Indian
summer monsoon: Insight from aerosol mass spectrometry, Atmos. Environ., 136,
144–155, 10.1016/j.atmosenv.2016.04.024, 2016.Chakraborty, A., Mandariya, A. K., Chakraborti, R., Gupta, T., and Tripathi,
S. N.: Realtime chemical characterization of post monsoon organic aerosols
in a polluted urban city: Sources, composition, and comparison with other
seasons, Environ. Pollut., 232, 310–321, 10.1016/j.envpol.2017.09.079, 2018.Crenn, V., Sciare, J., Croteau, P. L., Verlhac, S., Fröhlich, R., Belis,
C. A., Aas, W., Äijälä, M., Alastuey, A., Artiñano, B.,
Baisnée, D., Bonnaire, N., Bressi, M., Canagaratna, M., Canonaco, F.,
Carbone, C., Cavalli, F., Coz, E., Cubison, M. J., Esser-Gietl, J. K.,
Green, D. C., Gros, V., Heikkinen, L., Herrmann, H., Lunder, C.,
Minguillón, M. C., Močnik, G., O'Dowd, C. D., Ovadnevaite, J.,
Petit, J. E., Petralia, E., Poulain, L., Priestman, M., Riffault, V.,
Ripoll, A., Sarda-Estève, R., Slowik, J. G., Setyan, A., Wiedensohler,
A., Baltensperger, U., Prévôt, A. S. H., Jayne, J. T., and Favez,
O.: ACTRIS ACSM intercomparison – Part 1: Reproducibility of concentration
and fragment results from 13 individual Quadrupole Aerosol Chemical
Speciation Monitors (Q-ACSM) and consistency with co-located instruments,
Atmos. Meas. Tech., 8, 5063–5087, 10.5194/amt-8-5063-2015, 2015.DeCarlo, P. F., Kimmel, J. R., Trimborn, A., Northway, M. J., Jayne, J. T.,
Aiken, A. C., Gonin, M., Fuhrer, K., Horvath, T., Docherty, K. S., Worsnop,
D. R., and Jimenez, J. L.: Field-deployable, high-resolution, time-of-flight
aerosol mass spectrometer, Anal. Chem., 78, 8281–8289, 10.1021/Ac061249n,
2006.DeCarlo, P. F., Dunlea, E. J., Kimmel, J. R., Aiken, A. C., Sueper, D., Crounse, J., Wennberg, P. O., Emmons, L., Shinozuka, Y., Clarke, A., Zhou, J., Tomlinson, J., Collins, D. R., Knapp, D., Weinheimer, A. J., Montzka, D. D., Campos, T., and Jimenez, J. L.: Fast airborne aerosol size and chemistry measurements above Mexico City and Central Mexico during the MILAGRO campaign, Atmos. Chem. Phys., 8, 4027–4048, 10.5194/acp-8-4027-2008, 2008.Drewnick, F., Hings, S. S., DeCarlo, P., Jayne, J. T., Gonin, M., Fuhrer,
K., Weimer, S., Jimenez, J. L., Demerjian, K. L., Borrmann, S., and Worsnop,
D. R.: A new time-of-flight aerosol mass spectrometer (TOF-AMS) – Instrument
description and first field deployment, Aerosol Sci. Technol., 39,
637–658, 10.1080/02786820500182040, 2005.Dunlea, E. J., DeCarlo, P. F., Aiken, A. C., Kimmel, J. R., Peltier, R. E.,
Weber, R. J., Tomlinson, J., Collins, D. R., Shinozuka, Y., McNaughton, C.
S., Howell, S. G., Clarke, A. D., Emmons, L. K., Apel, E. C., Pfister, G.
G., van Donkelaar, A., Martin, R. V., Millet, D. B., Heald, C. L., and
Jimenez, J. L.: Evolution of Asian aerosols during transpacific transport in
INTEX-B, Atmos. Chem. Phys., 9, 7257–7287, 10.5194/acp-9-7257-2009, 2009.Gadi, R., Shivani, Sharma, S. K., and Mandal, T. K.: Source apportionment
and health risk assessment of organic constituents in fine ambient aerosols
(PM2.5): A complete year study over National Capital Region of India,
Chemosphere, 221, 583–596, 10.1016/j.chemosphere.2019.01.067, 2019.Gani, S., Bhandari, S., Seraj, S., Wang, D. S., Patel, K., Soni, P., Arub,
Z., Habib, G., Hildebrandt Ruiz, L., and Apte, J. S.: Submicron aerosol
composition in the world's most polluted megacity: the Delhi Aerosol
Supersite study, Atmos. Chem. Phys., 19, 6843–6859, 10.5194/acp-19-6843-2019, 2019.Gunthe, S. S., Liu, P., Panda, U., Raj, S. S., Sharma, A., Darbyshire, E.,
Reyes-Villegas, E., Allan, J., Chen, Y., Wang, X., Song, S., Pöhlker, M.
L., Shi, L., Wang, Y., Kommula, S. M., Liu, T., Ravikrishna, R., McFiggans,
G., Mickley, L. J., Martin, S. T., Pöschl, U., Andreae, M. O., and Coe,
H.: Enhanced aerosol particle growth sustained by high continental chlorine
emission in India, Nat. Geosci., 14, 77–84, 10.1038/s41561-020-00677-x, 2021.Gupta, P., Singh, S. P., Jangid, A., and Kumar, R.: Characterization of
black carbon in the ambient air of Agra, India: Seasonal variation and
meteorological influence, Adv. Atmos. Sci., 34, 1082–1094, 10.1007/s00376-017-6234-z, 2017.Jain, C. D., Gadhavi, H. S., Wankhede, T., Kallelapu, K., Sudhesh, S., Das,
L. N., Pai, R. U., and Jayaraman, A.: Spectral properties of black carbon
produced during biomass burning, Aerosol. Air. Qual. Res., 18, 671–679, 10.4209/aaqr.2017.03.0102, 2018a.Jain, S., Sharma, S. K., Mandal, T. K., and Saxena, M.: Source apportionment
of PM10 in Delhi, India using PCA/APCS, UNMIX and PMF, Particuology, 37,
107–118, 10.1016/j.partic.2017.05.009, 2018b.Jain, S., Sharma, S. K., Vijayan, N., and Mandal, T. K.: Seasonal
characteristics of aerosols (PM2.5 and PM1.0) and their source apportionment
using PMF: A four year study over Delhi, India, Environ. Pollut.,
262, 114337, 10.1016/j.envpol.2020.114337,
2020.
Janssen, N. A., Gerlofs-Nijland, M. E., Lanki, T., Salonen, R. O., Cassee, F., Hoek, G., Fischer, P., Brunekreef, B., and Krzyzanowski, M.: Health effects of black carbon, WHO Regional Office for Europe Copenhagen, Denmark, 55, 2012.
Jimenez, J., Canagaratna, M., Donahue, N., Prevot, A., Zhang, Q., Kroll, J.,
DeCarlo, P., Allan, J., Coe, H., and Ng, N.: Evolution of organic aerosols
in the atmosphere, Science, 326, 1525–1529, 2009.Kolhe, A. R., Aher, G. R., Ralegankar, S. D., and Safai, P. D.:
Investigation of aerosol black carbon over semi-urban and urban locations in
south-western India, Atmos. Pollut. Res., 9, 1111–1130, 10.1016/j.apr.2018.04.010, 2018.Kompalli, S. K., Suresh Babu, S. N., Satheesh, S. K., Krishna Moorthy, K.,
Das, T., Boopathy, R., Liu, D., Darbyshire, E., Allan, J. D., Brooks, J.,
Flynn, M. J., and Coe, H.: Seasonal contrast in size distributions and
mixing state of black carbon and its association with PM1.0 chemical
composition from the eastern coast of India, Atmos. Chem. Phys., 20,
3965–3985, 10.5194/acp-20-3965-2020, 2020.Kumar, B., Chakraborty, A., Tripathi, S. N., and Bhattu, D.: Highly time
resolved chemical characterization of submicron organic aerosols at a
polluted urban location, Environ. Sci. Proc. Imp., 18,
1285–1296, 10.1039/c6em00392c, 2016.Lack, D. A., Moosmüller, H., McMeeking, G. R., Chakrabarty, R. K., and
Baumgardner, D.: Characterizing elemental, equivalent black, and refractory
black carbon aerosol particles: a review of techniques, their limitations
and uncertainties, Anal. Bioanal. Chem., 406, 99–122, 10.1007/s00216-013-7402-3, 2014.Lanz, V. A., Alfarra, M. R., Baltensperger, U., Buchmann, B., Hueglin, C.,
Szidat, S., Wehrli, M. N., Wacker, L., Weimer, S., Caseiro, A., Puxbaum, H.,
and Prevot, A. S. H.: Source attribution of submicron organic aerosols
during wintertime inversions by advanced factor analysis of aerosol mass
spectra, Environ. Sci. Technol., 42, 214–220, 10.1021/Es0707207, 2008.Laskar, S. I., Jaswal, K., Bhatnagar, M. K., and Rathore, L. S.: India
Meteorological Department, Proceedings of the Indian National Science Academy, India, 82, 1021–1037, 10.16943/ptinsa/2016/48501, 2016.Mohr, C., DeCarlo, P. F., Heringa, M. F., Chirico, R., Slowik, J. G.,
Richter, R., Reche, C., Alastuey, A., Querol, X., Seco, R., Penuelas, J.,
Jimenez, J. L., Crippa, M., Zimmermann, R., Baltensperger, U., and Prevot,
A. S. H.: Identification and quantification of organic aerosol from cooking
and other sources in Barcelona using aerosol mass spectrometer data, Atmos.
Chem. Phys., 12, 1649–1665, 10.5194/acp-12-1649-2012, 2012.Mukherjee, S., Singla, V., Pandithurai, G., Safai, P. D., Meena, G. S.,
Dani, K. K., and Anil Kumar, V.: Seasonal variability in chemical
composition and source apportionment of sub-micron aerosol over a high
altitude site in Western Ghats, India, Atmos. Environ., 180, 79–92, 10.1016/j.atmosenv.2018.02.048, 2018.Nazeer Hussain, S., Chakradhar Rao, T., Balakrishnaiah, G., Rama Gopal, K.,
Raja Obul Reddy, K., Siva Kumar Reddy, N., Lokeswara Reddy, T., Pavan
Kumari, S., Ramanjaneya Reddy, P., and Ramakrishna Reddy, R.: Investigation
of black carbon aerosols and their characteristics over tropical urban and
semi-arid rural environments in peninsular India, J. Atmos.
Sol.-Terr. Phys., 167, 48–57, 10.1016/j.jastp.2017.10.010, 2018.Ng, N. L., Canagaratna, M. R., Zhang, Q., Jimenez, J. L., Tian, J., Ulbrich,
I. M., Kroll, J. H., Docherty, K. S., Chhabra, P. S., Bahreini, R., Murphy,
S. M., Seinfeld, J. H., Hildebrandt, L., Donahue, N. M., DeCarlo, P. F.,
Lanz, V. A., Prevot, A. S. H., Dinar, E., Rudich, Y., and Worsnop, D. R.:
Organic aerosol components observed in Northern Hemispheric datasets from
Aerosol Mass Spectrometry, Atmos. Chem. Phys., 10, 4625–4641, 10.5194/acp-10-4625-2010, 2010.
Ng, N. L., Herndon, S. C., Trimborn, A., Canagaratna, M. R., Croteau, P.,
Onasch, T. B., Sueper, D., Worsnop, D. R., Zhang, Q., and Sun, Y.: An
Aerosol Chemical Speciation Monitor (ACSM) for routine monitoring of the
composition and mass concentrations of ambient aerosol, Aerosol Sci.
Technol., 45, 780–794, 2011.Paatero, P., Hopke, P. K., Song, X. H., and Ramadan, Z.: Understanding and
controlling rotations in factor analytic models, Chemometr. Intell. Lab., 60,
253–264, 10.1016/S0169-7439(01)00200-3, 2002.
Petzold, A., Kramer, H., and Schonlinner, M.: Continuous measurement of
atmospheric black carbon using a multi-angle absorption photometer, Environ.
Sci. Pollut. R., 9, 78–82, 2002.Platt, S. M., El Haddad, I., Pieber, S. M., Zardini, A. A., Suarez-Bertoa,
R., Clairotte, M., Daellenbach, K. R., Huang, R. J., Slowik, J. G.,
Hellebust, S., Temime-Roussel, B., Marchand, N., de Gouw, J., Jimenez, J.
L., Hayes, P. L., Robinson, A. L., Baltensperger, U., Astorga, C., and
Prévôt, A. S. H.: Gasoline cars produce more carbonaceous
particulate matter than modern filter-equipped diesel cars, Sci. Rep.-UK, 7,
4926, 10.1038/s41598-017-03714-9, 2017.Pope, I. C., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., and Thurston, G. D.: Lung cancer,
cardiopulmonary mortality, and long-term exposure to fine particulate air
pollution, J. Am. Med. Assoc., 287, 1132–1141, 10.1001/jama.287.9.1132, 2002.Prakash, J., Vats, P., Sharma, A. K., Ganguly, D., and Habib, G.: New
Emission Inventory of Carbonaceous Aerosols from the On-road Transport
Sector in India and its Implications for Direct Radiative Forcing over the
Region, Aerosol Air Qual. Res., 20, 741–761, 10.4209/aaqr.2019.08.0393, 2020.Raatikainen, T., Brus, D., Hooda, R. K., Hyvarinen, A. P., Asmi, E., Sharma,
V. P., Arola, A., and Lihavainen, H.: Size-selected black carbon mass
distributions and mixing state in polluted and clean environments of
northern India, Atmos. Chem. Phys., 17, 371–383, 10.5194/acp-17-371-2017, 2017.Rajesh, T. A. and Ramachandran, S.: Characteristics and source
apportionment of black carbon aerosols over an urban site, Environ. Sci.
Pollut. R., 24, 8411–8424, 10.1007/s11356-017-8453-3, 2017.
Ramgolam, K., Favez, O., Cachier, H., Gaudichet, A., Marano, F., Martinon,
L., and Baeza-Squiban, A.: Size-partitioning of an urban aerosol to identify
particle determinants involved in the proinflammatory response induced in
airway epithelial cells, Part Fibre Toxicol., 6, 6–9, 2009.Reyes-Villegas, E., Bannan, T., Le Breton, M., Mehra, A., Priestley, M.,
Percival, C., Coe, H., and Allan, J. D.: Online Chemical Characterization of
Food-Cooking Organic Aerosols: Implications for Source Apportionment,
Environ. Sci. Technol., 52, 5308–5318, 10.1021/acs.est.7b06278, 2018.Reyes Villegas, E., Allan, J., McFiggans, G., and Coe, H.: APHH: Compact Time of Flight Aerosol Mass Spectrometer measurements made at the Indira Gandhi Delhi Technical University for Women (IGDTUW) site and India Meteorological Department (IMD) during the post monsoon periods for the DelhiFlux field campaign 2018, available at: https://catalogue.ceda.ac.uk/uuid/b7c9aeb6aee54698aef82f20365fc441TS8,
last access: 15 March 2021.Sandradewi, J., Prévôt, A. S. H., Szidat, S., Perron, N., Alfarra,
M. R., Lanz, V. A., Weingartner, E., and Baltensperger, U. R. S.: Using
aerosol light abosrption measurements for the quantitative determination of
wood burning and traffic emission contribution to particulate matter,
Environ. Sci. Technol., 42, 3316–3323, 10.1021/es702253m,
2008.Satish, R., Shamjad, P., Thamban, N., Tripathi, S., and Rastogi, N.:
Temporal Characteristics of Brown Carbon over the Central Indo-Gangetic
Plain, Environ. Sci. Technol., 51, 6765–6772, 10.1021/acs.est.7b00734, 2017.Shinozuka, Y., Saide, P. E., Ferrada, G. A., Burton, S. P., Ferrare, R.,
Doherty, S. J., Gordon, H., Longo, K., Mallet, M., Feng, Y., Wang, Q.,
Cheng, Y., Dobracki, A., Freitag, S., Howell, S. G., LeBlanc, S., Flynn, C.,
Segal-Rosenhaimer, M., Pistone, K., Podolske, J. R., Stith, E. J., Bennett,
J. R., Carmichael, G. R., da Silva, A., Govindaraju, R., Leung, R., Zhang,
Y., Pfister, L., Ryoo, J. M., Redemann, J., Wood, R., and Zuidema, P.:
Modeling the smoky troposphere of the southeast Atlantic: a comparison to
ORACLES airborne observations from September of 2016, Atmos. Chem. Phys.,
20, 11491–11526, 10.5194/acp-20-11491-2020, 2020.Shivani, Gadi, R., Sharma, S. K., and Mandal, T. K.: Seasonal variation,
source apportionment and source attributed health risk of fine carbonaceous
aerosols over National Capital Region, India, Chemosphere, 237, 124500,
10.1016/j.chemosphere.2019.124500, 2019.Shrivastava, M., Cappa, C. D., Fan, J., Goldstein, A. H., Guenther, A. B.,
Jimenez, J. L., Kuang, C., Laskin, A., Martin, S. T., Ng, N. L., Petaja, T.,
Pierce, J. R., Rasch, P. J., Roldin, P., Seinfeld, J. H., Shilling, J.,
Smith, J. N., Thornton, J. A., Volkamer, R., Wang, J., Worsnop, D. R.,
Zaveri, R. A., Zelenyuk, A., and Zhang, Q.: Recent advances in understanding
secondary organic aerosol: Implications for global climate forcing, Rev.
Geophys., 55, 509–559, 10.1002/2016rg000540, 2017.Singla, V., Mukherjee, S., Pandithurai, G., Dani, K. K., and Safai, P. D.:
Evidence of Organonitrate Formation at a High Altitude Site, Mahabaleshwar,
during the Pre-monsoon Season, Aerosol Air Qual. Res., 19, 1241–1251, 10.4209/aaqr.2018.03.0110, 2019.Thamban, N. M., Tripathi, S. N., Moosakutty, S. P., Kuntamukkala, P., and
Kanawade, V. P.: Internally mixed black carbon in the Indo-Gangetic Plain
and its effect on absorption enhancement, Atmos. Res., 197, 211–223,
10.1016/j.atmosres.2017.07.007, 2017.
Tiwari, S., Pervez, S., Cinzia, P., Bisht, D. S., Kumar, A., and Chate, D.:
Chemical characterization of atmospheric particulate matter in Delhi, India,
part II: Source apportionment studies using PMF 3.0, Sustain. Environ.
Res., 23, 295–306, 2013.Turner, A. G., Bhat, G. S., Martin, G. M., Parker, D. J., Taylor, C. M.,
Mitra, A. K., Tripathi, S. N., Milton, S., Rajagopal, E. N., Evans, J. G.,
Morrison, R., Pattnaik, S., Sekhar, M., Bhattacharya, B. K., Madan, R.,
Govindankutty, M., Fletcher, J. K., Willetts, P. D., Menon, A., Marsham, J.
H., team, a. t. I., Hunt, K. M. R., Chakraborty, T., George, G., Krishnan,
M., Sarangi, C., Belušić, D., Garcia-Carreras, L., Brooks, M.,
Webster, S., Brooke, J. K., Fox, C., Harlow, R. C., Langridge, J. M.,
Jayakumar, A., Böing, S. J., Halliday, O., Bowles, J., Kent, J.,
O'Sullivan, D., Wilson, A., Woods, C., Rogers, S., Smout-Day, R., Tiddeman,
D., Desai, D., Nigam, R., Paleri, S., Sattar, A., Smith, M., Anderson, D.,
Bauguitte, S., Carling, R., Chan, C., Devereau, S., Gratton, G., MacLeod,
D., Nott, G., Pickering, M., Price, H., Rastall, S., Reed, C., Trembath, J.,
Woolley, A., Volonté, A., and New, B.: Interaction of convective
organization with monsoon precipitation, atmosphere, surface and sea: The
2016 INCOMPASS field campaign in India, Q. J. Roy.
Meteorol. Soc., 146, 2828–2852, 10.1002/qj.3633, 2019.Weingartner, E., Saathoff, H., Schnaiter, M., Streit, N., Bitnar, B., and
Baltensperger, U.: Absorption of light by soot particles: determination of
the absorption coefficient by means of aethalometers, J. Aerosol. Sci., 34,
1445–1463, 10.1016/S0021-8502(03)00359-8, 2003.Young, D. E., Allan, J. D., Williams, P. I., Green, D. C., Flynn, M. J.,
Harrison, R. M., Yin, J., Gallagher, M. W., and Coe, H.: Investigating the
annual behaviour of submicron secondary inorganic and organic aerosols in
London, Atmos. Chem. Phys., 15, 6351–6366, 10.5194/acp-15-6351-2015, 2015.Zhang, Q., Alfarra, M. R., Worsnop, D. R., Allan, J. D., Coe, H.,
Canagaratna, M. R., and Jimenez, J. L.: Deconvolution and quantification of
hydrocarbon-like and oxygenated organic aerosols based on aerosol mass
spectrometry, Environ. Sci. Technol., 39, 4938–4952, 10.1021/es048568l, 2005.Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Allan, J. D., Coe, H.,
Ulbrich, I., Alfarra, M. R., Takami, A., Middlebrook, A. M., Sun, Y. L.,
Dzepina, K., Dunlea, E., Docherty, K., DeCarlo, P. F., Salcedo, D., Onasch,
T., Jayne, J. T., Miyoshi, T., Shimono, A., Hatakeyama, S., Takegawa, N.,
Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian,
K., Williams, P., Bower, K., Bahreini, R., Cottrell, L., Griffin, R. J.,
Rautiainen, J., Sun, J. Y., Zhang, Y. M., and Worsnop, D. R.: Ubiquity and
dominance of oxygenated species in organic aerosols in
anthropogenically-influenced Northern Hemisphere midlatitudes, Geophys. Res.
Lett., 34, L13801,
10.1029/2007gl029979, 2007.
Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Ulbrich, I. M., Ng, N. L.,
Worsnop, D. R., and Sun, Y.: Understanding atmospheric organic aerosols via
factor analysis of aerosol mass spectrometry: a review, Anal.
Bioanal. Chem., 401, 3045–3067, 2011.