Delhi, India, routinely experiences some of the world's highest urban
particulate matter concentrations. We established the Delhi Aerosol Supersite
study to provide long-term
characterization of the ambient submicron aerosol composition in Delhi. Here
we report on 1.25 years of highly time-resolved speciated submicron
particulate matter (PM1) data, including black carbon (BC) and
nonrefractory PM1 (NR-PM1), which we combine to develop a
composition-based estimate of PM1
(“C-PM1” = BC + NR-PM1) concentrations.
We observed marked seasonal and diurnal variability in the concentration and
composition of PM1 owing to the interactions of sources and atmospheric
processes. Winter was the most polluted period of the year, with average
C-PM1 mass concentrations of ∼210µg m-3. The monsoon was hot and rainy, consequently
making it the least polluted (C-PM1∼50µg m-3) period. Organics constituted more than half
of the C-PM1 for all seasons and times of day. While ammonium, chloride,
and nitrate each were ∼10 % of the C-PM1 for the cooler
months, BC and sulfate contributed ∼5 % each. For the warmer
periods, the fractional contribution of BC and sulfate to C-PM1
increased, and the chloride contribution decreased to less than 2 %. The
seasonal and diurnal variation in absolute mass loadings were generally
consistent with changes in ventilation coefficients, with higher
concentrations for periods with unfavorable meteorology – low
planetary boundary layer height and low wind speeds. However, the variation
in C-PM1 composition was influenced by temporally varying sources,
photochemistry, and gas–particle partitioning. During cool periods when wind
was from the northwest, episodic hourly averaged chloride concentrations
reached 50–100 µg m-3, ranking
among the highest chloride concentrations reported anywhere in the world.
We estimated the contribution of primary emissions and secondary processes to
Delhi's submicron aerosol. Secondary species contributed
almost 50 %–70 % of Delhi's C-PM1 mass for the
winter and spring months and up to 60 %–80 % for the warmer summer
and monsoon months. For the cooler months that had the highest C-PM1
concentrations, the nighttime sources were skewed towards primary sources,
while the daytime C-PM1 was dominated by secondary species. Overall,
these findings point to the important effects of both primary emissions and
more regional atmospheric chemistry on influencing the extreme particle
concentrations that impact the Delhi megacity region. Future air quality
strategies considering Delhi's situation in both a regional
and local context will be more effective than policies targeting only local,
primary air pollutants.
Introduction
Outdoor air pollution has detrimental health effects and is
responsible for more than 4 million deaths every year globally
, resulting in substantial global and regional decrements in
life expectancy . India experiences high ambient air
pollution, with an annual population-weighted PM2.5 (particulate matter
with diameter less than 2.5 µm) mean of 74 µg m-3,
and experiences the highest number of deaths from ambient air pollution among
all countries in the world (∼1.1 million people yr-1, ∼1.5 years of life lost due to air pollution) .
Some of the most polluted cities in the world are in India. Delhi
(population = 28 million) is the world's most polluted megacity, with
recent annual-average PM2.5 concentrations of ∼140µg m-3.
Previous aerosol characterization campaigns in Delhi have noted the
importance of both primary and secondary sources to Delhi's
poor ambient air quality . These studies have
shown Delhi's PM to be rich in organics throughout the year
and to contain high concentrations of inorganic species such as chloride and
nitrate during the foggy wintertime. Furthermore, high concentrations of
black carbon (BC) and brown carbon attributable to primary emissions such as
biomass combustion and diesel exhaust have been observed across north India
. However, previous studies in Delhi
have mostly observed aerosol composition for short periods with limited
temporal information . The Delhi Aerosol Supersite (DAS)
study was designed to address current uncertainties in Delhi's aerosol composition by collecting continuous, highly time-resolved data on
a long-term basis. In addition to providing insights into the atmospheric
processes relevant for a polluted megacity, this study contributes to the
understanding of the atmospheric science for South Asia in general. The
lessons from Delhi have relevance for the entire Indo-Gangetic Plain
(population: ∼400 million; including parts of India, Pakistan,
Bangladesh, and Nepal) that experiences similar meteorology and high PM
levels, especially during wintertime .
Here we provide a detailed overview of the chemical composition of PM1
in Delhi by season and time of day based on a long-term deployment of a mass
spectrometer instrument. We also provide insights into the role of
meteorology in the concentration and composition of PM1. Finally, we
include a brief overview of the source apportionment results from the
positive matrix factorization (PMF) of aerosol mass spectra to understand the
contribution of primary emissions and secondary processes to
Delhi's PM concentrations, with details of the PMF provided
in a companion paper .
MethodsSampling site and pollutants measured
Delhi experiences a wide range of variation in temperature (T), relative
humidity (RH), wind speeds, and precipitation across the year and by time of
day (Fig. ). The winters (December to mid-February) are
cool (T∼10–20 ∘C, average diurnal range)
and humid (RH ∼45 %–90 %) with low wind speeds
(∼2–3 m s-1). Delhi
frequently experiences shallow inversion layers (depth <100 m) during the
winter, especially at night and in the morning hours. Summers (April to June)
are very hot (T∼25–40 ∘C) and dry (RH
∼30 %–55 %). Delhi and most of the Indo-Gangetic Plain
experiences episodic heavy rainfall during the monsoon (July to
mid-September), accompanied by slightly lower temperatures (T∼25–35 ∘C) than the summers. While the winds throughout
most of the year are predominately from the northwest, during the monsoon the
wind is from the south during the nighttime. Spring (mid-February
to March) and autumn (mid-September to November) are periods of transition
between these meteorological extremes. For all seasons, the ventilation
coefficient is highest during the daytime when the boundary layer height and
the wind speeds reach their diurnal maxima. Changes in ventilation play an
important role in the large seasonal and diurnal variation of PM
. Unfavorable meteorological conditions often amplify
primary emissions to produce spectacularly high PM2.5 concentrations
.
Diurnal profiles of meteorological parameters (temperature, relative
humidity, wind speed, wind direction, PBLH, and VC) by season. Average values
by season and hour of day are presented for all parameters except wind
direction. The median value is presented for wind direction. Ventilation
coefficient (VC) = PBLH × wind speed.
To investigate the composition of ambient air in New Delhi at high time
resolution, we installed a suite of online aerosol measurement
instrumentation at the Indian Institute of Technology Delhi (IITD) campus in
South Delhi. The instruments are situated in a temperature-controlled
laboratory on the top floor of a four-story building. The nearest source of
local emissions is an arterial road located 150 m away from the building. We
measured chemical composition of nonrefractory PM1
(NR-PM1) was measured using an Aerodyne
Aerosol Chemical Speciation Monitor (ACSM; Aerodyne Research, Billerica, MA).
BC was measured using a multi-channel aethalometer (Magee Scientific Model
AE33, Berkeley, CA) with a multi-spot sampling system designed to minimize
the filter loading artifact present on earlier aethalometer systems
. Particle size distributions (PSDs) were measured using
a scanning mobility particle sizer (SMPS; TSI, Shoreview, MN), consisting of
an electrostatic classifier (TSI model 3080), a differential mobility
analyzer (DMA; TSI model 3081), an X-ray aerosol neutralizer (TSI model
3088), and a water-based condensation particle counter (CPC; TSI model 3785).
Instrumentation
The instruments were placed on two separate sampling lines. The first
sampling line (SL1) had the ACSM and the SMPS in parallel. The second
sampling line (SL2) was for the aethalometer. Both sampling
lines had a PM2.5 cyclone at the inlet, followed by a water trap and a
Nafion membrane diffusion dryer (Magee Scientific sample stream dryer,
Berkeley, CA). The flow rate in SL1 was 3 L min-1, divided as follows: 1 L min-1
pulled by the SMPS, 0.1 L min-1 by the ACSM, and the remaining 1.9 L min-1 by an
in-line flow controller which was in parallel with the SMPS and downstream
from the ACSM. SL2 had a flow rate of 2 L min-1 pulled by the
aethalometer. For the SMPS, the CPC pulled at a 1 L min-1 flow rate, and the
electrostatic classifier was operated at a sheath flow rate of 4 L min-1 to
enable SMPS scanning over a broad range of particle sizes. We conducted
experiments at multiple sheath flow rates from 4 to 10 L min-1 and found the
results to be consistent.
The ACSM measures NR-PM1, i.e., those compounds that flash vaporize at
the heater temperature of ∼600∘C. The flash-vaporized
compounds are subsequently ionized in the ACSM via 70 eV electron impact
ionization and detected with a quadrupole mass spectrometer .
The scan speed was set at 200 ms amu-1 and pause
setting at 125 for a sampling time (64 s). Detailed operational
procedures for the ACSM are provided in
Appendix . Some submicron aerosol
constituents are refractory, including BC, metals, and crustal materials. For
our core analyses of PM1 mass, we use the sum of NR-PM1+ BC as a
composition-based proxy for total PM1, which we term “C-PM1”.
This C-PM1 metric excludes the contribution to submicron mass of
refractory metals and crustal materials, which we estimate results in a
5 %–10 % underestimate of total PM1 mass (see below).
Data processing
The SMPS scanned from 12 to 560 nm, with each subsequent scan 135 s apart. We used a mode fitting algorithm in the mass
domain to estimate the PSD between 560 and 1000 nm. We validated the
performance of our model by comparing the modeled and observed volume and
number concentrations for the observed particle size range. We found that the
model predicted the same volume as was observed (slope=1.00,
R2=1.00) but slightly overestimated particle number
concentrations (slope=1.06, R2=0.96), mostly for
smaller particles. In order to develop a supplemental PSD-based estimate of
submicron mass, we first estimated a complete (hybrid) PSD up to 1000 nm by
combining the observed PSD from 12 to 560 nm and the modeled PSD from 560 to
1000 nm. Estimates of aerosol densities from Asia range between
1.3 and 1.6 g cm-3. Using
a particle density of 1.6 g cm-3 and the hybrid
PSD, we developed a SMPS-based PM1 estimate (“SMPS-PM1”). On an
hourly basis, the linear fit between our core C-PM1 and supplemental
SMPS-PM1 estimates had a slope of 0.96 and an R2 of
0.85 (Fig. S1 in the Supplement). This linear fit suggested that our speciated PM1 data
(NR-PM1 species and BC) agreed reasonably well with the SMPS-PM1
estimates. We used the PSD to estimate the transmission efficiency (TE) of
the ACSM. The details of this correction along with other ACSM data
processing steps are provided in Appendix . We
estimate an overall uncertainty of up to 20 %–25 % in the ACSM data,
which is within expectations for measurements from this instrument
.
While we acquired data for each instrument at high time resolution (∼1 min for the aethalometer and the ACSM; ∼2 min for the SMPS), for
analytical simplicity we generally present the hourly averaged data for each
instrument in this study. We categorize the seasons as winters (December to
mid-February), summers (April to June), the monsoon (July to mid-September),
and spring (mid-February to March) . Autumn (mid-September to
November) is not included in our core analyses due to the unavailability of
ACSM data for that period. In our analysis, we define day as 07:00–19:00 and
night as 19:00–07:00.
We retrieved the hourly temperature and relative humidity (RH) data from the
Indira Gandhi International Airport (IGIA; 8 km from our site). To obtain
mesoscale (regional) meteorological data for wind speed, direction (10 m from
ground), and planetary boundary layer height (PBLH) in Delhi, we used a NASA
meteorological reanalysis dataset, MERRA2 . MERRA2 has a
spatial resolution of 0.5∘×0.625∘ (55 km × 60 km) and an hourly temporal resolution. We retrieved daily
precipitation data for Delhi from the European Centre for Medium-Range
Weather Forecasts' reanalysis dataset, ERA-Interim
.
The hourly data for all species across the campaign are neither strictly
normally nor log-normally distributed (Fig. S2). However, since the data are
relatively closer to being log-normally distributed, we have included
geometric mean (GM) and geometric standard deviation (GSD) in addition to the
arithmetic mean (AM) wherever possible to provide a more complete
representation of the central tendency of the data. Furthermore, the annual
averages reported in this study are the averages of all the available hourly
data from 2017 for the NR-PM1 species and BC. It should be noted that we
do not have ACSM data (NR-PM1 species) for autumn and only a few days of
aethalometer data (BC) for the monsoon. On the basis of available SMPS-PM1
data (our site) and PM2.5 data (multiple regulatory monitors in Delhi),
we estimate that the true annual average differs from the data we collected
by within ±20 %. As a sensitivity analysis, we reconstruct annual and
campaign averages by giving equal weight to each 2-month period. For example,
to calculate the synthetic (reconstructed) annual average for 2017, we
averaged the averages of the six 2-month periods (January–February through
November–December). In Table S1 we have provided a comparison between the AM,
GM, and the synthetic averages of the PM1 components for the 2017 data
against the entire campaign data.
Results and discussionMass concentration
We observed marked seasonal and diurnal variation in the PM mass
concentration owing to the interactions of sources, atmospheric mixing, and
physicochemical processing. Figure shows the time
series of NR-PM1, individual submicron species, PM2.5 at a
background site, and selected meteorological parameters. The daily average
NR-PM1 concentrations at our site varied between 12.7 and 392 µg m-3, with an annual average of 87.3 µg m-3. Most C-PM1 mass was
nonrefractory – the average NR-PM1 fraction of C-PM1 was
highest in the winter (94 %) and lowest for warmer months (85 %)
(Fig. ). The average wintertime NR-PM1
concentration was ∼2 times higher than spring and
∼4 times higher than the warmer months. Using speciated mass
concentrations and the PSD, we observed that C-PM1 was highly correlated
with SMPS-PM1 (R2=0.83), and we achieved almost
complete mass closure (Fig. S1). That most of the PM1 was composed of
nonrefractory material and BC was consistent with past literature from Delhi
which observed that metals and other nonrefractory crustal materials, which
we did not measure in this study, constituted less than 5 % of PM1.
Time series of (a) PM1 species (Org, Chl,
NH4, NO3, SO4, and BC),
(b) NR-PM1 and PM2.5 (DPCC, R.K. Puram – 3 km from our
site), (c) relative humidity and temperature, (d) wind
speed and direction, and (e) PBLH and precipitation. A 24 h moving
average is applied on all time series presented.
Average absolute and fractional composition of PM1 (Org, Chl,
NH4, NO3, SO4, and BC) by season. Limited BC
data for the monsoon due to instrument downtime.
We estimated that the C-PM1 concentrations observed at our site were
generally ∼85 % of the PM2.5 concentrations (R2=0.54
and slope=0.85 for linear fit of hourly C-PM1 and
PM2.5 concentrations over entire campaign) measured at the nearest
monitoring station that is operated by the Delhi Pollution Control Committee
(DPCC), R.K. Puram (3 km away), where the annual average PM2.5
concentration for 2017 was 140 µg m-3. There were strong
seasonal and diurnal variations in mass loadings, with winter being the most
polluted with average concentration ∼4 times higher than the least
polluted summer and monsoon months. The daily average PM2.5
concentration exceeded the daily average Indian National Ambient Air Quality
Standards (NAAQS; 60 µg m-3) on more than 80 % of the
days and the World Health Organization (WHO) 24 h average air quality
guidelines (25 µg m-3) on all but two days. A distinct feature of
Delhi's wintertime air pollution is the nearly complete absence of periods of
clean air, in contrast to some other polluted megacities (e.g., Beijing),
which are characterized by episodic alternation between clean and polluted
conditions . For wintertime, the daily average PM2.5
concentrations exceeded 100 µg m-3 on 94 % of the days,
the Indian NAAQS on 99 % of the days, and the WHO guidelines on all days.
Daily-average PM2.5 concentrations at R.K. Puram exceeded
500 µg m-3 on four days in 2017.
PM1 composition: seasonal and diurnal variation
The concentrations and fractional contribution to PM1 of each species
varied by season and time of day. Over the campaign, organics comprised
54 % of the submicron mass, inorganics (chloride, ammonium, nitrate, and
sulfate) 36 %, and BC 10 %. There was a strong seasonality in C-PM1
loadings, with the wintertime average loadings exceeding the relatively less
polluted and warmer summer and monsoon months by 3–4 times. We
report the average seasonal concentrations of organics, sulfate, ammonium,
nitrate, chloride, and BC in Table and their
contribution to C-PM1 in Fig. . Within each
season there were distinct diurnal (time-of-day) trends for the average
concentrations by hour of day for NR-PM1 and PM1 components
(Fig. ). These diurnal swings of the average hourly
concentrations were the most prominent for the colder winter and spring
months. In winter, average hourly NR-PM1 concentrations ranged between
97.4 and 254 µg m-3 (minimum and maximum
concentrations for the average diurnal cycle). Spring conditions were
moderately less polluted, with hourly average concentrations ranging
diurnally from 37.0 to 167 µg m-3. The
NR-PM1 concentrations varied much less during summer (range of
concentrations for an average diurnal cycle: 38.7 to 72.4 µg m-3) and the monsoon (32.1 to 47.7 µg m-3). For most seasons, the hourly averaged
NR-PM1 concentrations peaked around 07:00–08:00 and then again
around 21:00–22:00, with the daily minimum typically occurring
around 15:00–16:00. However, for the monsoon months the NR-PM1
average hourly concentrations were similar throughout the day. The diurnal
variation in average hourly concentrations and fractional composition of
NR-PM1 species for each season is presented in
Fig. . The day and night averages by season for
each PM1 species along with the summary averages of meteorological
parameters are presented in Table . We did not
observe any marked day of the week difference in the levels or composition of
C-PM1 (Fig. S3).
Seasonal summary of PM1 species – arithmetic mean (AM),
geometric mean (GM), and geometric standard deviation (GSD) for hourly
concentrations.
a Based on limited BC data for the monsoon due to
instrument downtime. b Composition-based estimate of PM1
(BC + NR-PM1). c SMPS-based estimate using hybrid PSD
and assuming a density of 1.6 g cm-3.
Average diurnal profiles of PM1 species by season. Limited BC
data for the monsoon due to instrument downtime. Composition-based estimate
of PM1 (C-PM1) = BC + NR-PM1.
Stacked average absolute and fractional diurnal profiles of
NR-PM1 species by season.
Day and night summary of PM1 species and meteorological
parameters. Arithmetic mean used for all species and parameters, except wind
direction for which we used median to estimate its central tendency.
Winter Spring Summer Monsoon DayNightDayNightDayNightDayNightOrg86138477629412026Chl18277.9111.71.30.30.5NH419219.3115.54.84.94.4Mass concentrationNO324248.79.33.64.03.43.8(µg m-3)SO416159.810109.5119.1BC9.4206.8155.2135.4a18aNR-PM11632268211750613944C-PM1b172246881325573––SMPS-PM1c1632348414557984766Temperature (∘C)1713262135313229Relative humidity (%)6078425934437181Meteorological parametersWind speed (m s-1)2.72.63.22.63.82.93.42.5Wind direction (∘ N)300300300300270270250190PBLH (m)92034018001000240016001600460
a Based on limited BC data for the monsoon due to
instrument downtime. b Composition-based estimate of PM1
(BC + NR-PM1). c SMPS-based estimate using hybrid PSD
and assuming a density of 1.6 g cm-3.
Organics were the single largest C-PM1 mass component for all seasons
and at all times of the average diurnal cycle. Organics consistently
contributed to more than ∼50 % of seasonal C-PM1 mass, with
some episodes when their contribution was as high as 80 %. The high organic
fraction of PM is consistent with studies from across the world
. The daily averages of organics at our site
varied between 6.4 and 293 µg m-3, with an
annual average of 51.5 µg m-3. The average
wintertime organic concentration was ∼2 times higher than
spring and ∼4–5 times higher than summer and the monsoon.
While the wintertime organic concentrations ranged between 53.3 and 166 µg m-3, with the highest concentration
during the night, the diurnal variations were less dynamic for the warmer
months, with the hourly average organic concentrations ranging between 20.8
and 49.8 µg m-3 for summertime. Comparing
daytime and nighttime f43 and f44 values for each season, the bulk
organic aerosol was generally more oxidized during the warmer periods (Fig. S4), presumably owing to the higher photochemical activity during that time
.
Ammonium was the prominent inorganic cation in C-PM1 and generally
balanced all the anionic inorganic species (chloride, nitrate, and sulfate).
Over the campaign, the molar ratio of the inorganic anions to cations
(ammonium) was 0.82 (R2=0.96). Ammonium mass
concentrations were consistently around ∼10 % of the observed
C-PM1. The daily average of ammonium at our site varied between 1.5 and
37.9 µg m-3, with an annual average of 9.0 µg m-3. The average ammonium concentration
for wintertime was ∼2 times higher than spring and
∼4 times higher than summer and the monsoon. Ammonium
concentration hourly averages ranged between 10.9 and 30.8 µg m-3 during the winter and between 4.2 and 8.3 µg m-3 during the summer.
We observed some of the highest chloride concentrations reported anywhere in
the world, with episodes when hourly averages exceeded 100 µg m-3 (more than 40 such hours across the
campaign). The 90th and 95th percentile
values of the hourly concentrations of chloride over the campaign were 26.7
and 43.8 µg m-3, respectively. The daily
average of chloride concentration at our site varied between 0.1 and 66.6 µg m-3, with an annual average of 6.1 µg m-3. Chloride concentrations showed the
strongest seasonal variability, with the average wintertime concentration
∼2 times higher than during spring and more than 20 times higher than during the warm (summer and monsoon) months.
During the cooler winter and spring months, chloride concentrations had the
largest diurnal variation among all PM species observed, with the average
diurnal minimum and maximum hourly concentration ranging between 4.6 and 47.3 µg m-3 in wintertime. The winter chloride
peak is notable for its timing in the early morning hours (∼ 07:00), which is considerably later than the diurnal peak in organics and BC,
which tended to occur shortly before midnight. While chloride contributed
more than 10 % of the submicron mass in the winters, it comprised less
than 2 % of the C-PM1 mass concentration during the summer and monsoon.
Furthermore, chloride constituted around 12 %–16 % of NR-PM1 for
temperatures below 15 ∘C but dropped to less than 4 % of the
NR-PM1 concentrations for temperatures above 25 ∘C (Fig. S5).
Given that ammonium was nearly always present in sufficient quantities to
neutralize the major inorganic anions measured by the ACSM, we infer that the
dominant fraction of chloride was usually present as ammonium chloride, for
which gas–particle partitioning is strongly temperature dependent. (However,
we cannot exclude the possibility that organic chlorides contributed a
subsidiary fraction of the chloride mass.) Even for high episodic chloride
concentrations, ammonium was present in sufficient levels to neutralize most
of the anionic species, with a deficiency of only ∼20 % when
considering only hours with chloride concentrations higher than the
90th percentile campaign value (26.7 µg m-3).
To understand whether the sharp drop in chloride concentrations for warmer
times of day could be explained by evaporation of ammonium chloride, we used
an inorganic aerosol thermodynamics model . The detailed
methodology and results of the model used are presented in
Appendix . Results from inorganic thermodynamic modeling
suggest that most of the ammonium chloride observed in the winter is expected
to evaporate at summer temperatures and relative humidity, consistent with
our observations. The volatile nature of ammonium chloride has also been
observed in other parts of the world and was
consistent with the sharp drop in the chloride fraction that we observed for
the warmer periods. We believe that gas–particle partitioning and episodic
sources (Sect. ) may drive much of the diurnal and
seasonal variation in particulate chloride. We would therefore expect a large
fraction of chloride to be in the gas phase, especially for warmer periods.
We did not collect gas-phase HCl measurements here, but future studies could
validate this hypothesis through measurements of gas-phase chloride.
Nitrate comprised 6 %–12 % of Delhi's C-PM1, with
daily averages between 0.6 and 58.5 µg m-3, with an annual average of 8.8 µg m-3.
The average wintertime nitrate concentration was more than 2 times higher than spring and more than 6 times higher than summer and the monsoon.
The average diurnal cycle (lowest and highest hourly average concentration)
for wintertime concentrations ranged between 15.9 and 33.6 µg m-3, and the summer concentrations ranged between
2.2 and 6.3 µg m-3. The nitrate fraction
of NR-PM1 dropped from 12 % at temperatures below 25 ∘C to
5 %–9 % at temperatures above 25 ∘C, likely due to the
thermodynamics of ammonium nitrate. As with chloride, nitrate concentrations
were also generally highly correlated with ammonium concentrations
(R2=0.69 for hourly data over entire campaign),
suggesting that most of the nitrate observed was present as ammonium nitrate.
Considering the ubiquitous NOx sources in this megacity,
organic nitrates may also contribute to the total nitrate measured by the
ACSM.
The daily averages of sulfate at our sites varied between 3.1 and 34.5 µg m-3, with an annual average of 11.8 µg m-3. Sulfate had the least seasonal
variability among the NR-PM1 species, with wintertime average
concentration ∼1.5 times higher than each of the other
seasons (spring, summer, and monsoon). In addition to the low seasonal
variability, sulfate was also the chemical constituent with the least diurnal
variation and had relatively higher daytime concentrations for the warmer
summer months. The diurnal variation in sulfate concentration for the cooler
months was similar to that of other PM1 species, with the average early
morning concentrations for winter and spring almost 2 times higher than
the daytime concentrations. Sulfate was the only NR-PM1 species that had
a higher mass fraction during the warmer months, contributing
13 %–30 % to the C-PM1 mass in the warmer months,
8 %–20 % in spring, and 5 %–13 % in winter, with the mass
fraction being highest during the daytime for all seasons. The sulfate
fraction of NR-PM1 increased from less than 10 % for periods cooler than
25 ∘C to more than 25 % for periods above 35 ∘C. The
increase in sulfate mass fraction for warmer periods can be explained by the
lower diurnal and seasonal variability in its absolute concentration,
possibly due to a combination of increased daytime photochemical formation
rates for warmer months and sulfate being well mixed in the atmosphere
because of its transport from longer distances .
BC contributed to 6.4 % of the C-PM1 mass concentration in the winter
compared to 10 % in the spring and 14 % in the summer. We had limited
monsoon data for BC. The daily average of BC at our site varied between 2.2
and 35.2 µg m-3, with an annual average of
12.4 µg m-3. The average wintertime BC
concentration was ∼1.5 times higher than spring and summer.
The seasonal differences in the absolute BC concentrations were not as high
as any of the other PM1 species. One possible explanation for this
result relates to the presence of nearby BC sources within Delhi, including
the major ring road with truck traffic near our sampling site. These trucks
are often restricted to only passing through Delhi at night
. It is plausible that these nearby primary emissions
would be incompletely mixed into the boundary layer and are therefore
relatively less affected by atmospheric mixing
(Sect. and ).
Accordingly, BC had sharp diurnal variability, with peak nocturnal BC
concentrations typically ∼3–4 times higher than during
midday hours, with peak concentrations occurring at a similar time to the
temporal peak for organics (typically just before midnight).
Role of meteorology
The planetary boundary layer height (PBLH) had a strong seasonal variation
with summer heights 2–4 times larger than those during the
cooler months. The seasonal variability in the PBLH along with that in wind
speed resulted in the ventilation coefficient (VC = PBLH × wind
speed, sometimes referred to as normalized dilution rate) being 4–6 times slower for the wintertime compared to the summer. The VC
is often used as a parameter to characterize the role of atmospheric dilution
in pollutant concentrations, both in the Indian context
and globally . Seasonal
variability in the VC appears to reasonably agree with the higher NR-PM1
concentrations in less ventilated cold months and lower concentrations in the
warmer months when the VC was higher (Fig. ). The week with the
lowest VC was ∼6 times less ventilated than the most
ventilated week and had ∼6 times higher NR-PM1 mass
concentrations. For the non-monsoon periods, the VC was generally a good
indicator of NR-PM1 concentrations (R2=0.56 for
weekly averaged data). For the cooler winter and spring months, the
R2 for the linear fit of the weekly averaged VC and
NR-PM1 concentrations was 0.79. The monsoon concentrations were lower
than those that would be expected by the VC calculated for those periods.
This result could be explained by a combination of change in the prominent
nighttime wind direction from northwest to south during the monsoon and the
washout of PM by the monsoon rain. For the monsoon period, we observed that
the average NR-PM1 mass concentration was almost half on days when it
rained compared to the dry (no rain) days with no change in the composition
of NR-PM1. The strong modulating effect of meteorology on air pollution
is well appreciated for Delhi and other Indian cities
.
(a) Variations of NR-PM1 mass concentrations as a
function of ventilation coefficient. Each scatter point is a weekly average
and is color coded by month. Note that July to mid-September is the
monsoon season. (b) Average NR-PM1 composition for days with
(rainy) and without rain (dry). The vertical lines are the 25th (bottom)
and 75th (top) percentiles.
Even within each season, the VC showed large time-of-day variations, with
highest hourly average values 5–10 times larger than the
lowest. For each season, times of day with a lower VC had the highest
NR-PM1 concentrations, and the concentrations decreased as the VC value
increased (Fig. S7). The large diurnal range of the VC seemed to explain most of
the variability in NR-PM1 concentrations by time of day for most seasons
(Rwinter2=0.88;
Rspring2=0.93;
Rsummer2=0.81). For the monsoon, the diurnal
variability of most PM1 species was generally low, even though the VC varied
by time of day, possibly due to precipitation washout of PM and a change in
characteristic wind direction during the monsoon (as discussed above).
In general, the sharp variation in the VC by season and time of day appear to
explain much of the variability in NR-PM1 concentrations. Furthermore,
volatile species (e.g., ammonium chloride and ammonium nitrate) evaporate to
the gas phase during warmer periods, further lowering the mass concentrations
compared to the cooler periods. While there are seasonal differences in
emissions from sources such as crop burning and local biomass burning for
heat , our analysis suggests that in addition to
episodic sources, meteorology being unfavorable is an important factor for
some of the high PM concentrations observed.
Episodic high concentrationsChloride episodes and wind direction
Delhi experiences a prominently northwestern wind
(Fig. ). However, we observed that for brief periods
during winter and spring when the wind was from the south, the peak
chloride concentrations dropped from as high as 50–100 µg m-3 on one day to less than 10 µg m-3 on the next
(Fig. ). Furthermore, the highest decile of
chloride concentrations in the campaign were mostly observed when the wind
was from the northwest (Fig. ). During winter mornings, when
chloride concentrations were generally highest, the chloride fraction of
C-PM1 was almost 2 times higher for periods with a northwestern
wind compared to periods with wind from any other direction (Fig. S8). These
findings suggest a large source of chloride in the northwest of Delhi. The
high levels of chloride observed in Delhi are neither observed in other South
Asian countries , nor in other parts of
India , suggesting that these
extreme levels of chloride probably come from more than just the usual type
of biomass and waste burning , which is ubiquitous across
South Asia . While filter-based studies can cause
underreporting of volatile species such as ammonium chloride, the levels of
chloride we observe in Delhi are much higher than those reported from studies
in South Asia (outside Delhi) that use online aerosol instrumentation
. Furthermore, the PMF factor for biomass
burning organic aerosol of does not correlate with
chloride. There are many industrial sites in the northwest of Delhi,
including metal processing plants that use HCl for steel pickling
. The fugitive HCl fumes from these industries along
with the high ammonia in Delhi could be a pathway for
these high observed particulate chloride concentrations .
Other possible sources of HCl are from the combustion of polyvinyl chloride,
coal, and biomass burning .
Our findings are based on measurement of particulate chloride and inorganic
thermodynamic modeling and can be tested by future studies that measure both
gas and particulate chloride.
Time series of PM1 species (Org, Chl, NH4,
NO3, SO4, and BC) – stacked absolute concentrations and
fraction of PM1 – along with wind speed and wind direction for a period
with high chloride concentrations.
Relative frequency for high episodic concentrations of PM1
species (concentrations greater than the 90th percentile concentration of
that species for the entire campaign) as a function of the wind direction.
High organic episode
While organics contributed to almost half of the C-PM1 for all seasons
and times of day, there were episodes for which the contribution of organics
increased to as high as 80 % of the C-PM1. One such episode was around
Lohri (13 January 2018), a festival celebrated in many parts of north
India (including Delhi and regions upwind of Delhi), with bonfires burnt at
night. In 2018, Lohri was on a weekend (Saturday), and we observed a sharp
increase in nighttime C-PM1 concentrations, almost
2–3 times higher than the weekday nights preceding Lohri
(Fig. ). The contribution of both organics and BC
increased for this period, with organics concentrations as high as 300 µg m-3 during these bonfire
nights, contributing to ∼60 %–70 % of the C-PM1.
Time series of PM1 species (Org, Chl, NH4,
NO3, SO4, and BC) – stacked absolute concentrations and
fraction of PM1 – for a period with high organic PM concentration
coinciding with the Lohri bonfire festival of 15 January 2018.
Autumn PM2.5 episodes
The PM2.5 concentrations in Delhi ramp up during the autumn, with some
of the highest episodic concentrations observed during this period and often
attributed to agricultural burning . In 2017 the most polluted episodes were in the autumn, with the
highest PM2.5 hourly concentrations exceeding 500 µg m-3
for 75–228 h across various locations in Delhi (DPCC monitoring stations
and US Embassy). These autumn episodes constituted 80 %–100 %
(across sites) of the hours for which PM2.5 exceeded
500 µg m-3 in 2017 across Delhi. The highest PM
concentrations within autumn were observed during the periods with a
relatively lower VC and when the wind was from the north or the northwest
(Fig. ). The concentrations were relatively lower for
periods with higher VC values and when the wind was from the south. While
some of these observations seem to support the role of agricultural burning
in these episodic PM concentrations, we plan to strengthen this hypothesis in
a future study using composition data that we have collected during the
autumn of 2018.
Primary vs. secondary organic aerosol
Positive matrix factorization (PMF) conducted on the ACSM mass spectra
provided further information on the sources and atmospheric processes that
affect NR-PM1 concentrations in Delhi . The organic
aerosol (OA) was separated into two factors: primary OA (POA) and oxygenated
OA (OOA), with periods when the POA factor further separated into
hydrocarbon-like OA (HOA) and biomass-burning OA (BBOA). POA exhibited strong
diurnal variability, reflecting the impact of primary combustion emissions
modulated by diurnal cycles in the PBLH. The POA fraction of organics was
generally highest during the nighttime (∼50 % for winter and ∼40 % for summer) and lowest during the daytime (∼20 % for both
winter and summer). As observed in other megacities, OOA was the largest
constituent of the organic aerosol throughout the year ,
demonstrating the profound influence of secondary formation on particle
concentrations in Delhi. OOA contributed to 50 %–80 % of the
organics almost year-round (Fig. S9). We estimated primary particulate matter
(PPM=POA+Chl+BC) and secondary
particulate matter (SPM=OOA+NH4+NO3+SO4) following . Since chloride was considered
primary, and ammonium was generally highly correlated with chloride, we
apportioned a chloride-equimolar
amount of ammonium as primary and the remaining as secondary organic aerosol.
In Fig. we separate C-PM1 into PPM and SPM by season
and time of day. We observed that almost 50 %–70 % of Delhi's
C-PM1 was secondary in nature for the winter and spring months and up to
60 %–80 % for the warmer summer and monsoon months. Our results show
that secondary aerosol accounts for the dominant fraction of Delhi's ambient
NR-PM1 under most conditions. While our analyses do not provide direct
evidence for the origin of the secondary fraction of PM1, consideration
of typical advection timescales from the upwind boundaries of Delhi (∼2–3 h at typical wind speeds) suggests that a substantial fraction of
Delhi's secondary aerosol may be transported from upwind regions, which also
experience high PM mass loadings . These findings
suggest that improving Delhi's air quality will require a concerted effort at
both at the local and the regional level. Future work could usefully
apportion the composition of PM1 at receptor sites upwind of Delhi.
BC was found to be well correlated (R2=0.65) with HOA
(for periods when HOA was a separate factor) , suggesting
that traffic, diesel generators, and other liquid fossil fuel combustion
contribute substantially to the BC inventory for Delhi. Furthermore, unlike
chloride, the highest BC concentrations were uncorrelated with any particular
wind direction (Fig. ) and also showed less seasonal
variation than other PM1 species (Fig. ),
potentially indicating a nearby year-round source that was less affected by
atmospheric mixing. We suspect that trucks (and other diesel vehicles) were a
major source of the high BC concentrations that we observed, similar to what
has been observed in other urban environments in India .
While BC absolute concentrations did not vary as much as other PM1
species, the fractional contribution to BC was as high as 20 % during periods
when the C-PM1 was lower (Fig. ). These findings
indicate the large local nature of BC emissions and the potential to reduce
BC concentrations by targeting high emitters such as heavy-duty trucks and
diesel generation systems . Previous studies have shown
that a small fraction (10 %–20 %) of high-emitting heavy-duty trucks
contribute to almost half of the total BC emissions from heavy-duty trucks
.
Time series of PM1 species (Org, Chl, NH4,
NO3, SO4, and BC) – stacked absolute concentrations and
fraction of PM1 – for a relatively less polluted (warm) period.
Average diurnal variation of mass concentrations and mass fractions
of primary and secondary C-PM1 by season.
Conclusions
We used continuous, highly time-resolved, and long-term data to provide a
detailed seasonal and diurnal characterization of Delhi's
PM1. We included data for organics, chloride, ammonium, nitrate,
sulfate, and BC from January 2017 to April 2018. The submicron mass for each species
varied dynamically by season and by time of day. Meteorology was found to be
an important factor in the modulation of PM levels, specifically by change in
the VC that varied dynamically as the PBLH varied by season and time of day.
The PM levels were generally the highest during the cooler months and
times of day, periods when the VC values were the lowest. Furthermore,
concentrations of volatile species (e.g., ammonium chloride) were further
enhanced during the cooler periods, when they had a higher tendency to be in
the particle phase. While organics from biomass burning were enhanced during
the cooler months, organics in general consistently (across seasons and
times of day) contributed to ∼60 % of Delhi's
PM1. We observed some of the highest chloride concentrations measured
anywhere in the world, with average concentrations higher than 50 µg m-3 for periods during winter mornings when winds
were from the northwest, resulting in part from what we suspect to be an
industrial source.
We estimate that substantially more than half of Delhi's
PM1 is of secondary origin. In combination with other evidence,
including the high levels of remotely sensed PM2.5 observed across the
upwind states of Haryana and Punjab , this
finding points to the likely conclusion that the high pollution observed in
Delhi is not merely a local problem but one with a widespread regional
source as well. Accordingly, reducing the PM levels in Delhi will require
both a local and a regional effort, with benefits that will be felt across the
Indo-Gangetic Plain. At the same time, primary PM1 levels in Delhi are
extremely high in absolute mass terms and are likely driven principally by
local emissions within the Delhi National Capital Region.
Delhi's air pollution has many critical sources; some are
local, and some are regional .
Coordinated regional and local control of nearly all contributors will be
required to bring about the order-of-magnitude concentration reductions that
will ultimately make the air safe to breathe .
Long-term monitoring campaigns such as the DAS can contribute previously
unavailable information on the evolving role of sources and other processes
that govern air pollution in Indian cities. In particular, continuous,
highly time-resolved data provide a basis for evaluating the intended and
unintended impact of policies and natural events on Delhi's
air quality in near-real time. However, air pollution is spatially variable,
and a single site generally does not provide sufficient information for the
complete assessment of air quality in a large urban area like Delhi. Future
work could usefully expand on this study through coordinated measurements of
aerosol chemical composition at other locations. One key research need is to
conduct similar measurements at sites upwind and downwind of Delhi to help quantify the role of local and regional sources in driving
Delhi's air pollution more precisely. Long-term studies of the changing nature of air
pollution in South Asian cities can help inform much-needed efforts to
protect a large part of the world's population from the adverse
effects of poor air quality.
Data availability
Hourly concentrations for PM1 species used in this study
are available via the Texas Data Repository, 10.18738/T8/9L33CI.
ACSM: calibration and operational procedures
Lens alignment and flow calibration were conducted at the start of the
campaign. Ionizer tuning, quadrupole resolution adjustment, adjustment of
multiplier voltage, and m/z calibration were conducted bimonthly. The pinhole
was cleaned at least biweekly. Calibrations for the response factor (RF) of
nitrate and the relative ionization efficiencies (RIEs) of ammonium and
sulfate were conducted several times throughout the campaign (Table S2). For
the RF and RIE calibrations, 300 nm particles, generated from 5 mM solutions
of ammonium nitrate and ammonium sulfate, were injected simultaneously into
the ACSM and CPC. The size-selected particles were sampled in jump mode (for
all calibrations) as well as single scan mode (September 2017 and January 2018), which
is now the recommended procedure for this calibration. The
RF/N2 air beam ratio was consistent in all jump mode
calibrations, suggesting a consistent sensitivity of the instrument. Thus,
the RF and RIE values from the two single scan mode calibrations were used
for all data (one value up to September 2017 and another value for the data
post September 2017).
ACSM: data processing
Time-dependent air beam corrections were applied to the raw data based on
N2 signal changes relative to the reference N2 signal (when the
calibration was performed). Relative ion transmission (RIT) correction was
applied using the default RIT curve (not the measured RIT curve) because of
the occurrence of a low naphthalene signal due to high concentration of m/z
fragments in sampling that build up and desorb during the filter sampling
period (Philip Croteau, Aerodyne Research, personal communication, 2017).
Detection limits were applied to species concentrations , and
data below the detection limit were replaced with 0.5 times the detection
limit. Collection efficiency (CE) was applied to
account for inefficient aerosol collection due to effects such as particle
bounce at the vaporizer. A composition-dependent CE was calculated based on
the method described in . An inline Nafion dryer
lowered RH levels to well within 50 % (<80 %), and the ammonium
nitrate fraction was less than 40 % throughout the campaign. Accordingly,
we only applied the acidity-dependent CE. This method assumes that the
particles are internally mixed, and hence a single correction factor was
applied for all species.
To account for particle loss during transmission through the aerodynamic
lens, a transmission efficiency (TE) correction factor was computed using
hourly averaged SMPS data. The following method was used to compute the TE
correction factor.
Hourly particle density was computed using hourly averaged ACSM composition
. Ammonium was attributed to each of the other inorganic
species, assuming that ammonium would first neutralize sulfate, followed by
nitrate and then chloride . The tracer-based method was used to
compute average organics composition and density
. Mobility diameter was converted to
vacuum aerodynamic diameter (Dva) using the method described in
, by assuming the Jayne shape factor to be 1 and using the calculated density. The
averaged experimental TE curve of an aerodynamic focusing lens system
was applied to the particle size distribution, and the TE
correction factor was calculated as the ratio of total particle volume to the
volume after applying the TE curve. Finally, average TE factors were computed
for every hour of the day for every season (Fig. S10), and the ACSM
concentrations were multiplied by this correction factor.
Inorganic modeling
The Extended Aerosol Thermodynamics Model (E-AIM) is used for interpreting
the effect of gas–particle partitioning on the seasonality of
concentrations . The focus of this modeling is on inorganic
species concentrations. While E-AIM can account for organic–inorganic
interactions, since the identity of organic-phase compounds is unknown, these
interactions are ignored. Further, model IV of E-AIM is employed as it
permits the variation of temperature and RH in the presence of the chloride
anion. However, there are at least two limitations to the approach:
The model always requires that the charge balance be maintained, although charged aerosols have been previously reported in literature. Further, the model does not
provide a route to account for periods with excess cations; no additional anions are available in the model. Na+, the only additional cation available
and used as a surrogate for metal cations not measured in this study, is used to balance the charge for periods with excess anions.
Periods with RH less than 60 % cannot be run in the presence of chloride. To deal with this, RH for all such periods is set to 60 %.
Due to data limitations and the above conditions, only periods between 00:00
and 03:00 and between 11:00 and 24:00 are analyzed. Hourly averaged diurnal
NR-PM1 species concentrations and gas-phase NH3 concentrations
(obtained when available from the nearest central regulatory monitoring
stations) for the winter of 2017 are input into the model. This technique of
running the model has been recently validated considering newly discovered
issues in such thermodynamic models . The model is
run in two modes – a “constrained” and an “unconstrained” mode. In the
first run, diurnal data for the winter of 2017 are input, together with
actual temperature and constrained RH; this mode forces the model to prevent
gas–aerosol partitioning of the input data and instead generate equilibrium
concentrations of gas-phase species HCl and HNO3. Together with the
measured NR-PM1 speciated concentrations and NH3, these
concentrations are used to obtain total concentration estimates for
NH3 (NH3+NH4+), NO3
(HNO3+NO3-), Cl (HCl+Cl-), and H+
(HCl+HNO3). Other species are non-volatile, and their
particle-phase concentrations are their total concentrations. The obtained
actual concentrations corrected for VC effects are run with the temperature
and RH of summer 2017. Thus, to estimate maximum PM formation potential
relative to the sources in winter 2017, diurnal “source” concentration averages for winter 2017 are applied to summer. We
run the model in an unconstrained mode – the goal being to allow
repartitioning for achieving equilibrium.
For the winter of 2017, chloride and nitrate were almost completely in aerosol
phase except between 12:00 and 17:00 (for analyzed periods >55 % of chloride and >85 % of nitrate in particle phase).
Applying winter 2017 source strength to summer, we observe a significant
shift – maximum nitrate in particle phase never exceeds 40 % (10 µg m-3), and chloride never exceeds 10 %
(3.5 µg m-3). Thus, temperature and RH can
explain the dramatic drop in concentrations of particle-phase chloride and
nitrate.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-6843-2019-supplement.
Author contributions
JSA, LHR, GH, SG, and SB designed the study. SG, SB, PS,
ZA, and SS carried out the data collection. SG, SB, KP, and SS carried out
the data processing and analysis. SG, SB, KP, DSW, LHR, and JSA assisted with
the interpretation of results. All co-authors contributed to the writing and
reviewing of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Joshua S. Apte was supported by the Climate Works Foundation. We are thankful to the
Indian Institute of Technology Delhi (IITD) for institutional support. We are
grateful to all student and staff members of the Aerosol Research
Characterization laboratory (especially Nisar Ali Baig and Mohammad Yawar)
and the Environmental Engineering laboratory (especially Sanjay Gupta) at
IITD for their constant support. We are thankful to Philip Croteau (Aerodyne
Research) and Maynard Havlicek (TSI) for always providing timely technical
support for the instrumentation.
Review statement
This paper was edited by Alex Lee and reviewed by three
anonymous referees.
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