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Environ Monit Assess (2024) 196:342
https://doi.org/10.1007/s10661-024-12462-z
RESEARCH
Satellite orground‑based measurements forair pollutants
(PM2.5, PM10, SO2, NO2, O3) data andtheir health hazards:
which ismost accurate andwhy?
ZainabMushtaq· ParginBangotra· AlokSagarGautam· ManishSharma·
Suman· SnehaGautam· KaranSingh· YogeshKumar· PoonamJain
Received: 3 July 2023 / Accepted: 17 February 2024
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024
Abstract Air pollution is growing at alarming rates
on regional and global levels, with significant conse-
quences for human health, ecosystems, and change in
climatic conditions. The present 12 weeks (4 Octo-
ber 2021, to 26 December 2021) study revealed the
different ambient air quality parameters, i.e., PM2.5,
PM10, SO2, NO2, and O3 over four different sam-
pling stations of Delhi-NCR region (Dwarka, Knowl-
edge park III, Sector 125, and Vivek Vihar), India,
by using satellite remote sensing data (MERRA-2,
OMI, and Aura Satellite) and different ground-based
instruments. The ground-based observation revealed
the mean concentration of PM2.5 in Dwarka, Knowl-
edge park III, Sector 125, and Vivek Vihar as 279
µg m−3, 274 µg m−3, 294 µg m−3, and 365 µg m−3,
respectively. The ground-based instrumental con-
centration of PM2.5 was greater than that of satellite
observations, while as for SO2 and NO2, the mean
concentration of satellite-based monitoring was
higher as compared to other contaminants. Negative
and positive correlations were observed among par-
ticulate matter, trace gases, and various meteorologi-
cal parameters. The wind direction proved to be one
of the prominent parameter to alter the variation of
these pollutants. The current study provides a percep-
tion into an observable behavior of particulate mat-
ter, trace gases, their variation with meteorological
parameters, their health hazards, and the gap between
the measurements of satellite remote sensing and
ground-based measurements.
Z.Mushtaq· Suman
Atmospheric Research Laboratory, Department
ofEnvironmental Sciences, SSBSR, Sharda University,
GreaterNoida, India
P.Bangotra(*)
Department ofPhysics, Netaji Subhas University
ofTechnology, Dwarka,NewDelhi110078, India
e-mail: pargin.bangotra@nsut.ac.in
A.S.Gautam(*)· K.Singh
Department ofPhysics, Hemvati Nandan Bahuguna
Garhwal University, Srinagar, Uttarakhand, India
e-mail: phyalok@gmail.com
M.Sharma
School ofScience andTechnology, Himgiri Zee
University, Dehradun, Uttarakhand, India
S.Gautam
Department ofCivil Engineering, Karunya
Institute ofTechnology andSciences, Tamil Nadu,
Coimbatore641114, India
S.Gautam
Water Institute, A Centre ofExcellence, Karunya
Institute ofTechnology andSciences, Tamil Nadu,
Coimbatore641114, India
Y.Kumar
Department ofPhysics, Hansraj College, University
ofDelhi, North Campus, Malka Ganj, NewDelhi110007,
India
P.Jain
Department ofPhysics, Sri Aurobindo College, University
ofDelhi, MalviyaNagar,NewDelhi110017, India
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Keywords Air quality· Particulate matter (PM2.5,
PM10)· Trace gases SO2, NO2, and O3· Remote
sensing· Health hazards
Introduction
The evaluation of air quality relies on several essen-
tial parameters that gauge the concentration of diverse
pollutants in the atmosphere. The Environmental Pro-
tection Agency (EPA) has set National Ambient Air
Quality Standards (NAAQS, 2011) for six common
criteria air pollutants, namely carbon monoxide (CO),
lead (Pb), ground-level ozone (O3), particulate mat-
ter (PM), nitrogen dioxide (NO2), and sulfur dioxide
(SO2). The two categories of particulate matter (PM)
are PM2.5 and PM10, which stand for particles having
sizes of 2.5 and 10 µm, respectively. Particles in the
air are referred to as aerosols, and the term particulate
matter (PM) is often used interchangeably with aero-
sol. In general, suspended particulate matter encom-
passes particles with a diameter of 10 µm or less that
are suspended in the atmosphere. Elevated levels of
PM2.5, PM10, SO2, NO2, and ground-level O3 arise
from various sources, including industrial activities
and combustion processes (Mushtaq etal., 2023).
Aerosols in the atmosphere are subtle elements
that scatter in solid and liquid forms and play a
critical role in regional and worldwide climate sys-
tems (Subramanian etal., 2023). Natural as well as
anthropogenic activities result in the production of
these aerosols. Due to their radiative influence in the
atmosphere and control over surface energy budget,
aerosols have been investigated extensively for their
effects on climate (Lubin etal., 2023). They signifi-
cantly impact air quality, negatively affecting the eco-
systems, earth’s radiation budget, and visibility (Kok
et al., 2023; Li etal., 2023; Hu et al., 2022; Singh
etal., 2022). Studies of a broad range of atmospheric
aerosols, physical features, and biochemical con-
figuration allow the prediction of potentially severe
and long-term ecological implications (Dulac et al.,
2022; Kruger etal., 2022; Sokhi etal., 2022). Aero-
sols have negative health impacts and have a crucial
influence on Earth’s climate through aerosol–radia-
tion and aerosol–cloud interfaces (Nair etal., 2023;
Lelieveld etal., 2015). The substantial environmen-
tal devastation caused by atmospheric aerosols gives
rise to a wide range of human interests and concerns
(Mushtaq etal., 2022).
Aerosols have relatively short lifetimes compared
to long-lasting climatic forcer such as CO2, result-
ing in high spatiotemporal unevenness (Unger etal.,
2008; Shindell etal., 2009). As a result, the climatic
and health consequences of aerosols are influenced
not only by inter-annual concentration fluctuations
but also by intra-annual variability. Satellite remote
sensing and ground-based instruments have recently
become the common tools for monitoring and distri-
butions of aerosol on local as well as on global scale
(Mushtaq et al., 2023; Che et al., 2015; Filonchyk
et al., 2019; Sarkar and Mishra 2018; Wang etal.,
2007). Satellite devices such as the Moderate Reso-
lution Imaging Spectroradiometer (MODIS) and the
Multi-Angle Imaging Spectroradiometer (MISR) are
used to measure the concentration of different sizes of
aerosols from remote locations. As each measurement
technique offers different insights into the scope and
underlying causes of air pollution, it is vital to use a
variety of measurement procedures, including both
satellite-based and ground-based techniques. While
ground-based measurements are essential for tracking
specific contaminants on a regional basis, however,
satellite data provides a global perspective. However,
due to the effects of geography and retrieval tech-
niques, data reception precision must be improved
on a regular basis (Kahn etal., 2009; Smirnov etal.,
2000; Levy etal., 2013; Zhang etal., 2017).
In many developing countries, economic progress
causes high air pollution activities, which has had
a severe negative consequence on the environment
and human health. More than 8.8 million deaths are
thought to occur each year as a result of prolonged
exposure to air pollutants such PM2.5, PM10, SO2,
NO2, and O3 (Burnett et al., 2018). Nitrogen diox-
ide (NO2) causes 4 million new instances of pedi-
atric asthma each year (Achakulwisut etal., 2019).
Although, what seems to be a global “pandemic” of
air pollution, manmade productions continue to rise
in many developing and some developed countries
(Crippa et al., 2018; Hoesly et al., 2018; Li et al.,
2017a).
In India, many cities are grappling with both
non-carcinogenic and carcinogenic risks posed
by various contaminants, which are present in dif-
ferent areas (Bangotra et al., 2023; Pandit et al.,
2020; Bangotra et al., 2019). Due to increased
Environ Monit Assess (2024) 196:342
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anthropogenic activities (rapid industrialization,
transportation activities, and infrastructure develop-
ment), India’s Indo-Gangetic Plain (IGP) is a hub of
air pollution activities (Mushtaq et al., 2023; Das
et al., 2020; Sharma et al., 2021). The five main
airborne particulates, PM2.5, PM10, SO2, NO2, and
O3, as well as meteorological parameters like tem-
perature (T), relative humidity (RH), wind speed
(WS), and wind direction (WD), were examined in
the current study. Using both ground-based and sat-
ellite-based monitoring devices, particulate matter
and trace gases were investigated across four differ-
ent locations in the Delhi-NCR region and further
scrutiny the best measurement method to perform
the health risk assessment from these carcinogens.
Study sites
The study was carried out in the Delhi-NCR (Fig.1)
of India. The Delhi-NCR is well-known for its high
pollution levels and is considered to be the most con-
taminated region in the world, largely due to its fast-
paced urbanization and industrial growth (Mushtaq
etal., 2023: Apte etal., 2011; Goel and Guttikunda,
2015; Kumari etal., 2017). The Delhi-NCR (33,578
km2) share its boundaries with three mega Indian
provinces (Haryana, Uttar Pradesh, and Rajasthan)
and a part of Indo-Gangetic region (alluvial plains).
The region has tropical climatic conditions with three
major seasons (winter, summer, and monsoon). The
samples were collected from 4 October 2021, to 26
Fig. 1 Selected location of the study
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December 2021, from four different cities Dwarka,
Knowledge park III, Sector 125, and Vivek Vihar.
Knowledge Park III, Greater Noida (KP-III)
Greater Noida Industrial Development Authority is
positioned in the Gautam Buddha Nagar district of
the Uttar Pradesh state (India). It is a portion of the
National Capital Region, which is located near Noida
in New Delhi. In the 1990s, it was a part of Noida;
currently, it is known as Greater Noida. It has an over-
all area of 238.64 km2, situated at 29.496152° N lati-
tude and 77.536011° E longitude. The study regions
are the smartest cities and are known as India’s one
of the commercial centers. The majority of the year
is often hot and humid, with exceptionally scorching
summer months and chilly and foggy winter months.
This region has a most significant roadway that links
to the Yamuna expressway and has connections to
many other prominent locations.
Sector 125, Noida
Sector 125 (28.5438° N, 77.3310° E) is adjoin-
ing to Noida, Greater Noida expressway. Many new
industrial and social amenities are being built in this
area. Greater Noida Expressway offers good access
to the rest of the main metropolis, because the area
is close to Noida. Small parks, banks, a police sta-
tion, a bird sanctuary, well-known Jamuna Beach,
and Playground are located close by in Sector 125’s
neighborhood.
Dwarka, Delhi
Dwarka is a sub-city in India that covers an area of
5648 acres between latitudes of 28°32′ and 28°38′
North and longitudes of 77°0′ and 78°8′ East (Fig.1).
It is one of the largest housing zones in Asia. Dwar-
ka’s land use allocation heeds a clear hierarchical
structure through the sub-city level to the sector level.
It is commonly known as the “Model Township.”
Vivek Vihar, Delhi
The subdivision of Shahdara district, Vivek Vihar
(28.6712° N, 77.3177° E), is a neighborhood to Delhi,
close to the border with Uttar Pradesh. It is one of
the ideal localities of Eastern Delhi, mostly made up
of residential developments with a focus on builder
floors and independent homes. The neighborhood is
horizontal in character and has low- to mid-rise build-
ings. Overall, this region has an excellent transporta-
tion system and is close to all social and commercial
amenities.
Material andmethods
At four locations in the Delhi-NCR, passive (dif-
fusive) or real-time monitoring samplers were
employed to quantify the different pollutants: particu-
late matter (PM2.5, PM10) and trace gases (SO2, NO2,
and O3). Sampling period (week-wise) began in Octo-
ber 2021 to December 2021. The four sampling loca-
tions were chosen in a systematic manner across the
NCR. Sites were chosen to be as far away from direct
local sources as practicable, including roadways and
other anthropogenic properties. The Central Pollution
Control Board (CPCB) of India provided measure-
ments for various meteorological parameters.
The PM2.5 concentration was determined by gravi-
metric analysis, while as SO2 and NO2 concentration
were determined using the modified West and Geake
method and Jacob and Hoehheiser (sodium arsenite
method). Surface O3, on the other hand, was studied
using the UV photometric method. Earlier and later
prior to the sampling, the PM2.5 filters were put in
desiccators for 24 h to remove absorbed water and
weighed in a precise atmospheric chamber after tak-
ing the filters out of the desiccators using an analyti-
cal scale. To determine the concentration of particu-
late matter in the air, the initial and final weights of
the filter paper were compared, with the difference
between the two being used to calculate the mass of
the aerosols in grams. Further, a gravimetric method
was used to estimate the concentration of aerosols.
Instrumentation
PM2.5 sampler (APM 550)
For sampling fine particles (PM2.5 percent), the sam-
pler is a manual technique designed for ambient air
quality monitoring based on USEPA (standardized
impactor). The APM 550 system takes the ambient
air through an omni-directional inlet and provides a
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proper aerodynamic cut-point for particulates of more
than 10 microns. Particles that are smaller than 10
microns in the airflow are directed towards a second-
ary impactor that has an aerodynamic cutoff point of
2.5 microns. After this, both the air sample and the
small particles that leave the PM2.5 impactors are
filtered through a Teflon filter membrane that is 47
mm in diameter, which captures and retains the small
particulate matter. A proper critical orifice keeps the
system’s sampling rate constant at 1.0 m3 h−1. The
APM 550 ensures that the temperature of the PM2.5
filter remains close to ambient temperature, and vola-
tile fractions of the PM2.5 are never lost by putting all
power-dissipating components in a separate cabinet.
An oil-free, continuous-rated pump provides the sys-
tem’s suction pressure. A dry gas meter is included in
the primary system to measure the entire air volume
sampled directly.
Respirable dust sampler (APM 460 DXNL)
The respirable dust sampler (RDS) is used to detect
PM10 concentration in atmospheric air on a regular
basis. It allows for precise measurement of respir-
able particulate matter, as well as the assessment of
total suspended particles by collecting dust contained
in the cyclone cup. The sampler has a special tool
called a cyclone that separates bigger particles from
the air before it goes through the filter. This upgraded
cyclone is even better than the old one, because it
can filter out particles that are 10 microns or bigger.
A thermal cut-out has been included to safeguard the
blower motor from overheating and burnout. At a flow
rate of 1.1 m3 min−1, ambient air is pulled through a
size-selective intake and through the glass fiber fil-
ter paper. The filter paper collects particles having
an aerodynamic diameter less than the inlet’s cut-
point. The variation in filter weights before and after
the measurement determines the mass of the particle
load. When chemical examination of particulate load
filter paper is required, the RDS is frequently used.
RDS includes 220 V, 2 A, a single-phase alternating
current power source and flow recording system, and
a timer with an automatic on-and-off function.
Gaseous pollutant sampler (APM 433)
The sampler is a type of monitoring device that
includes a suction pump, as well as a flowmeter,
time totalizer, and timer, for detecting inorganic
gaseous pollutants such as SO2, NO2, O3, NH3, and
other gaseous pollutants in ambient air pollutants. In
the process of monitoring gaseous pollutants, col-
lected air is bubbled through appropriate reagents that
absorb specific gaseous contaminants, and absorbing
media is assessed by means of standard wet chemi-
cal techniques. Three borosilicate glass impingers, 35
ml each, and 1 amber glass impinger for surface O3
monitoring, an additional glass impinger is available
as an option. The temperatures of the impingers can
be kept lower than the ambient temperature by using
thermo-electric cooling system. Special silica gel fil-
ter columns have been added to each impinger out-
flow channel, and the needle valve design has been
modified to trap high quantities of suspended par-
ticulate matter (SPM) that escape through the bubbler
and deposited in the flow rate control valve units in
the APM 433. As a result, the flow rate is unaffected
by the presence of suspended particle matter (SPM).
Satellite data description
We have used all the datasets acquired from Geo-
spatial Interactive Online Visualization and Analysis
Infrastructure (GIOVANNI). The Goddard Earth Sci-
ences Data Information Service Center (GES DISC)
has created an internet-based program that offers an
effortless and easy-to-understand method for observ-
ing, examining, and obtaining massive quantities of
remote sensing data related to Earth science. Thus, it
is called the bridge between data and science. In this
study, version v4.36 of GIOVANNI (https:// giova nni.
gsfc. nasa. gov/ giova nni/) was used. The four atmos-
pheric pollutants that have been used are shown in
Table1, and also, a brief introduction is given below:
For PM2.5, this dataset has been downloaded from
the Modern-Era Retrospective Analysis for Research
and Application version 2 (MERRA-2) model with
the help of GIOVANNI. MERRA-2 is the latest ver-
sion of worldwide atmospheric re-evaluated for
the satellite era made by National Aeronautics and
Space Administration (NASA) Global Modelling
and Assimilation Office (GMAO) by means of the
Goddard Earth Observing System (GEOS) model
version 5.12.4. MERRA-2 provides the data prod-
uct M2TINXAER (or tavg1_2d_aer_Nx) that is an
hourly time-averaged two-dimensional data col-
lection. Included in this compilation are integrated
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evaluations of aerosol characteristics, such as the
density of columnar mass for various aerosol con-
stituents, mass concentration of surface-level aerosol
components, and complete obscuration (as well as
dispersion) measurements for aerosol optical thick-
ness (AOT) at 550 nm (Gautam etal., 2022).
For SO2, the data product of SO2 is OMSO2E.003
or SO2 total column daily L3 best pixel Global 0.25
deg Lat/Lon Grid. The instrument and platform of
this data product are surface O3 Monitoring Instru-
ment (OMI) and Aura Satellite. In this product, each
grid in the planetary boundary layer (PBL) contains
just one observation of the density of data columns
for SO2, which is obtained by utilizing an enhanced
principal component analysis (PCA) algorithm (Li
et al., 2017a) for NO2, the data product of NO2 is
OMNO2d or NO2 Cloud Screened Total and Tropo-
spheric Column L3 Global Gridded 0.25° × 0.25° v3.
This data product is where pixel data of good quality
are binned and “averaged” into 0.25° × 0.25° global
grids.
Health risk assessment fromtheexposure of PM2.5
and PM10
The dose exposure from any contaminant depends
upon the source medium (air, water, soil, and food)
and distinct parameters of the human body as breath-
ing frequency, breathing pattern, body weight, lung
tidal volume, skin adherence factors, and tissue
weighting factors (Bangotra et al., 2023, Bangotra,
et al., 2021; Bangotra et al., 2019; Pandit et al.,
2020; USEPA 2009a, 2009b). The pathway exposure
through PM is further divided into age-wise (adults,
child) and categorized into major sections: inhala-
tion, ingestion, and dermal effect. In the study, the
exposure assessment is based on respiration (inha-
lation) and can be estimated through average daily
dose, which is the term used to assess dose exposure
through PM as given in Eq.1 and Table2.
Assessment of non-carcinogenic and carcinogenic
risk
Hazard quotient (HQ) is the term used to assess the
non-carcinogenic risk from the distinct pollutant to
the population of any particular location and can be
estimated from the ratio ADD to Rfd as mentioned in
Eq.2 and Table2 (USEPA 2009a, 2009b).
Due to meagre information and non-availability of
Rfc for PM2.5 and PM10, a 5 µg m−3 Rfc of PM2.5
from anthropogenic sources (diesel emission) and
50 µg m−3 for PM10 was used to estimate the refer-
ence dose level (Table2).
Carcinogenic risk assessment is based on the
worldwide accepted parameter known as “Excess
Lifetime-Cancer risk (ELCR),” which is the term
used to assess the probability for a person to develop
the cancer risk from the exposure of PM through life-
time (Eq.3) as mentioned in Table2.
(1)
ADD (μgkg−1d−1)=
c
(
PM2.5∕PM10
)
× In × ED ×
EF
BW ×AT
(2)
HQ
=
ADD
Rfd
Table 1 Satellite-derived
atmospheric pollutants data
description
S. no. Parameter
(variable)
Data source Spatial resolution Temporal
resolution
1. PM2.5
(dust surface mass con-
centration—PM2.5, time
average)
MERRA model 0.5° × 0.625° Hourly
2. SO2
(SO2 column amount)
OMI 0.25° × 0.25° Daily
3. NO2
(NO2 tropospheric
column, 30% cloud
screened)
OMI 0.25° × 0.25° Daily
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Results
Throughout the monitoring period, we assessed vari-
ations among different particulates, trace gases, and
various meteorological factors using ground and sat-
ellite-based measurements.
Variation in the concentration of PM2.5 and PM10
For the ground-based monitoring, the average con-
centration of PM2.5 in Dwarka, Knowledge park III,
Sector 125, and Vivek Vihar was 279 ± 76 µg m−3,
274 ± 72 µg m−3, 294 ± 102 µg m−3, and 365 ± 93
µg m−3, respectively (Table3). For the satellite-based
measurements, the concentration of PM2.5 for the
Dwarka, Knowledge park III, Sector 125, and Vivek
Vihar was 116 ± 44 µg m−3, 108 ± 64 µg m−3, 108 ±
64 µg m−3, and 108 ± 64 µg m−3 (Fig.2). The ground-
based measurements capture local sources and hot-
spots more accurately, leading to higher recorded
concentration, whereas satellite-based measurements
provide a broader and possibly more generalized view
(3)
ELCR =
C
(
PM2.5∕PM10
)
×ET×ED ×EF×
IUR
AT
of air quality over a larger geographic area, result-
ing in lower recorded concentration. Various factors,
including measurement techniques, spatial resolution,
and representativeness, contribute to the observed dif-
ferences between the two monitoring methods. The
satellite measurements for all locations were same
(except Dwarka) due to the small distance between
these stations. For PM10, only ground-based measure-
ment is taken into consideration due to the non-avail-
ability of remote sensing data (MODIS sensor). The
mean concentration of PM10 through ground-based
monitoring for Dwarka, Knowledge park III, Sec-
tor 125, and Vivek Vihar was 476 ± 93 µg m−3,439
± 101 µg m−3, 417 ± 234 µg m−3, and 538 ±113 µg
m−3 (Table4). Maximum peaks of PM2.5 and PM10
together were perceived during 8 Nov–14 Nov (week
6), after India’s mega festival (Diwali). The burning
of firecrackers for the celebration of Diwali festival
as well as enormous vehicular movement throughout
the Diwali days may be responsible for raising the
concentration of pollutants (Ambade, 2018; Mushtaq
etal., 2023).
Variation in SO2 and NO2 concentration
The weekly ground-based instrumental mean con-
centration of SO2 for Dwarka, Knowledge park III,
Table 2 Different input parameters for dose exposure from PM2.5 and PM10
* For carcinogenic AT = 25,550 days (adults as per 70 years), 5475 days (child as per 15 years)
RfD =
Rfc×In
BW
Parameters Abbreviation Unit Values References
PM2.5/PM10 concentration C (PM2.5)/C (PM10)µg m−3 -- Obtained from the satellite and ground-based
measurement
Air inhalation rate InR m−3 day−1 7.63 (adults) USEPA 2009a , 2009b
20 (child)
Exposure duration ED Year 24 (adults) USEPA (2018, 2011, USEPA 2009a , 2009b)
6 (child)
Exposure frequency EF Day year−1 180 USEPA 2009a , 2009b
Exposure time ET Hour 8 USEPA 2009a , 2009b
Body weight BW kg 70 (adults) USEPA 2009a , 2009b
15 (child)
Average time* AT Year ED × 365 days USEPA 2009a , 2009b
Integrated unit risk IUR µg m−3 0.008 Green and Morris (2006)
Reference dose conversion Rfc µg m−3 5 (PM2.5)Oliveira etal. (2017); Li etal. (2017b)
50 (PM10)
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Table 3 PM2.5 (µg m−3)for both satellite and ground-based data
S. no. Months Time duration Dwarka Knowledge park III Sector 125 Vivek Vihar
Satellite Ground based Satellite Ground based Satellite Ground based Satellite Ground based
1. October 04 Oct–10 Oct
(week 1)
31 ± 5 155 ± 49 39 ± 11 86 ± 42 39 ± 11 200 ± 64 39 ± 11 198± 35
2. 11 Oct–17 Oct
(week 2)
33 ± 4 141 ± 51 32 ± 5 162 ± 79 32 ± 5 159 ± 52 32 ± 5 187 ± 141
3. 18 Oct–24 Oct
(week 3)
23 ± 7 49 ± 25 26 ± 7 20 ± 37 26 ± 7 121 ± 32 26 ± 7 46 ± 20
4. 25 Oct–31 Oct
(week 4)
52 ± 4 101 ± 34 45 ± 4 106 ± 53 45 ± 4 168 ± 35 45 ± 4 63 ± 12
5. November 01 Nov–07 Nov
(week 5)
116 ± 44 227 ± 116 108 ± 64 213 ± 178 108 ± 64 293 ± 117 108 ± 64 266 ± 123
6. 08 Nov–14 Nov
(week 6)
75 ± 13 279 ± 76 70 ± 13 274 ± 72 70 ± 13 294 ± 102 70 ± 13 365 ± 93
7. 15 Nov–21 Nov
(week 7)
46 ± 3 211 ± 42 42 ± 3 168 ± 68 42 ± 3 123 ± 101 42 ± 3 238 ± 61
8. 22 Nov–28 Nov
(week 8)
25 ± 2 213 ± 54 26 ± 2 196 ± 98 26 ± 2 161 ± 38 26 ± 2 279 ± 66
9. December 29 Nov–5 Dec
(week 9)
17 ± 1 198 ± 63 18 ± 1 156 ± 53 18 ± 1 124 ± 71 18 ± 1 235 ± 80
10. 06 Dec–12 Dec
(week 10)
8 ± 1 131 ± 21 7 ± 1 101 ± 36 7 ± 1 90 ± 22 7 ± 1 164 ± 52
11. 13 Dec–19 Dec
(week 11)
2 ± 0 171 ± 39 2 ± 0 124 ± 18 2 ± 0 122 ± 32 2 ± 0 206 ± 68
12. 20 Dec–26 Dec
(week 12)
4 ± 0 239 ± 67 5 ± 0 208 ± 115 5 ± 0 188 ± 60 5 ± 0 329 ± 130
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Sector 125, and Vivek Vihar was 9 ± 13 µg m−3, 36
± 8 µg m−3, 32 ± 31 µg m−3, and 14 ± 12 µg m−3,
respectively (Table5). The peak values of SO2 were
recorded between the 15th and 21st of November
(week 7) at KP-III (Fig.3). In the midst of the peak
pollution period (Diwali festival), spanning from
November 1 to November 7, there was a decrease in
the levels of SO2, attributed to the limited efficiency
of the photochemical transformation of SO2 (Chen
et al., 2022). The satellite-based mean concentra-
tion of SO2 for Dwarka, Knowledge park III, Sector
125, and Vivek Vihar was 40 ± 6 µg m−3,43 ± 3 µg
m−3,47 ± 6 µg m−3, and 47 ± 6 µg m−3, indicating
higher concentration as compared to ground-based
observations (Table5).
The concentration level of NO2 for ground-based
monitoring was also found to be less than the satel-
lite concentration. However, it was higher than SO2
concentration in all the sampling sites through-
out the entire period of monitoring (Table6). The
ground-based and satellite-based mean concentra-
tions of NO2 in Dwarka, Knowledge park III, Sec-
tor 125, and Vivek Vihar were 41 ± 17 µg m−3, 44
± 16 µg m−3, 21 ± 3 µg m−3, and 82 ± 157 µg m−3
and 75 ± 43 µg m−3, 57 ± 39 µg m−3, 82 ± 59 µg
m−3, and 82 ± 59 µg m−3, respectively (Table6 and
Fig.4). Elevated levels of SO2 and NO2 were iden-
tified as a result of a lower mixing height, which
confines pollutants near the Earth’s surface, com-
bined with a favorable wind direction originating
4Oct-10 Oct
11Oct- 17Oct
18Oct- 24Oct
25Oct- 31Oct
1Nov -7 Nov
8Nov -14Nov
15Nov -21Nov
22Nov -28Nov
29 Nov- 5Dec
6Dec-12Dec
13Dec-19Dec
20Dec-26Dec
4Oct-10 Oct
11Oct- 17Oct
18Oct- 24Oct
25Oct- 31Oc
t
1Nov -7 Nov
8Nov -14Nov
15Nov -21Nov
22Nov -28Nov
29 Nov- 5Dec
6Dec-12Dec
13Dec-19Dec
20Dec-26Dec
4Oct-10 Oct
11Oct- 17Oct
18Oct- 24Oct
25Oct- 31Oct
1Nov -7 Nov
8Nov -14Nov
15Nov -21No
v
22Nov -28No
v
29 Nov- 5Dec
6Dec-12Dec
13Dec-19Dec
20Dec-26Dec
4Oct-10 Oct
11Oct- 17Oct
18Oct- 24Oct
25Oct- 31Oct
1Nov -7 Nov
8Nov -14Nov
15Nov -21Nov
22Nov -28Nov
29 Nov- 5Dec
6Dec-12Dec
13Dec-19Dec
20Dec-26Dec
0
50
100
150
200
250
300
350 (a)
Date
Dwarka PM2.5
Concentration (µg m-3)
Satellite
Ground Based
0
50
100
150
200
250
300
350
400
Date
(b)KnowledgeParkIII(PM2.5)
Satellite
Ground based
Concentration(µg m-3)
0
100
200
300
400
(c)
Date
Concentration(µg m-3)
Sector 125 (PM2.5)
Satellite
Ground Based
0
50
100
150
200
250
300
350
400
450
500
(d)
Date
Concentration(µg m-3)
VivekViher (PM2.5)
Satellite
Ground based
Fig. 2 Variation in PM2.5 (µg m−3)between ground and satellite-based measurements in all the sampling stations
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from heavily polluted areas (Qin etal., 2023). Such
variation difference is explained by changes in syn-
optic wind direction, which outweigh meteorologi-
cal variables. The sampling period was in the win-
ter, when there was less movement in the air, and
the gaseous pollutants lasted for 2 to 3 weeks in the
atmosphere.
Variation in the concentration of O3
The maximum surface O3 concentration through the
ground-based monitoring was found in Sector 125
(117 ± 24 µg m−3) during week 5 (1 Nov–7 Nov),
followed by Knowledge park III (113 ± 14 µg m−3),
Dwarka (47 ± 10 µg m−3), and Vivek Vihar (38 ± 4
µg m−3) as given in Table7.
Correlation of mass concentration of pollutants with
meteorological parameters
Table 8 reveals the mean values of meteorological
parameters from week 1 to week 12. The meteorolog-
ical parameters from CPCB platform were not availa-
ble for all the locations, and the temperature variation
is only available for Dwarka and Knowledge park III;
Dwarka region is 65 km apart from Knowledge park
III. The T (°C) in Dwarka and Knowledge park III
varied from 14 to 30 °C and 29 to 33 °C, respectively
(Table 8). The RH in Dwarka, Knowledge Park III,
Sector 125, and Vivek Vihar varied from 56 to 76%,
54 to 76%, 67 to 80%, and 62 to 78%, respectively.
The wind speed in Dwarka, Knowledge Park III, and
Vivek Vihar varied from 0.14 to 8.14 mi s−1, 0.37 to
1.43 mi s−1, and 0.55 to 0.85 mi s−1, respectively. The
wind direction data for Dwarka, Knowledge park III,
and Vivek Vihar is illustrated in Fig. 5. The prime
wind direction for Dwarka was towards the West
between WSW and WNW, East between ENE and
ESE for the Knowledge Park III region, and towards
the West between WSW and WNW for Vivek Vihar
as depicted in Fig.5 and Table8.
The effect of correlation matrix with all pollutant
concentrations in all the sampling stations is shown
in Table8. Although the Delhi region has a semi-arid
climate with three clearly distinct seasons, winters are
typically chilly, and between December to February.
Delhi’s reported yearly average temperature was 31.5
°C (Rawat etal., 2017). In the present study, a strong
and significant correlation has been observed among
particulate matter and trace gases in the Dwarka
region of Delhi (Table7). For T (°C), a negative and
positive correlation was observed with PM2.5 (−0.42),
NO2 (−0.43), O3 (−0.41), and SO2 (0.32), respec-
tively. However, SO2 and O3 have not shown the cor-
relations with T (°C), WS, and RH, WS, respectively.
Wind direction (WD) shows its correlation with all
the pollutants as PM2.5 (0.63), PM10 (−0.58), SO2
(−0.29), NO2 (−0.72), and O3 (−0.32) in the Dwarka
region of Delhi. In Knowledge park III, weak corre-
lations were observed among particulate matter and
trace gases. Among meteorological parameters, only
T (°C) has shown the correlation with PM2.5 (0.39)
Table 4 PM10 (µg m−3) forground-based data
S. no. Months Time duration Dwarka Knowledge park III Sector 125 Vivek Vihar
October Ground Based Ground based Ground Based Ground based
1. 4 Oct–10 Oct (week 1) 282 ± 56 255 ± 59 215 ± 54 270 ± 63
2. 11 Oct–17 Oct (week 2) 247 ± 75 213 ± 48 294 ± 54 240 ± 73
3. 18 Oct–24 Oct (week 3) 187 ± 98 119 ± 81 317 ± 63 143 ± 73
4. 25 Oct–31 Oct (week 4) 266 ± 77 229 ± 63 353 ± 105 182 ± 72
5. November 1 Nov–7 Nov (week 5) 419 ± 108 326 ± 180 394 ± 155 391 ± 100
6. 8 Nov–14 Nov (week 6) 476 ± 93 439 ± 101 417 ± 234 538 ± 113
7. 15 Nov–21 Nov (week 7) 368 ± 50 281 ± 95 236 ± 172 358 ± 169
8. 22 Nov–28 Nov (week 8) 343 ± 57 212 ± 100 295 ± 63 480 ± 84
9. December 29 Nov–5 Dec (week 9) 325 ± 79 277 ± 89 258 ± 130 403 ± 100
10. 6 Dec–12 Dec (week 10) 232 ± 23 176 ± 47 186 ± 29 268 ± 47
11. 13 Dec–19 Dec (week 11) 291 ± 54 225 ± 22 211 ± 104 334 ± 62
12. 20 Dec–26 Dec (week 12) 378 ± 80 321 ± 163 363 ± 87 490 ± 217
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Table 5 SO2 (µg m−3) forboth satellite and ground-based data
S. no. Months Time duration Dwarka Knowledge park III Sector 125 Vivek Vihar
Satellite Ground based Satellite Ground based Satellite Ground based Satellite Ground based
1. October 04 Oct–10 Oct
(week 1)
18 ± 3 3 ± 1 26 ± 3 21 ± 5 27 ± 3 29 ± 12 27 ± 3 8 ± 2
2. 11 Oct–17Oct
(week 2)
22 ± 3 2 ± 1 26 ± 2 24 ± 7 24 ± 2 15 ± 4 24 ± 2 7 ± 3
3. 18 Oct–24 Oct
(week 3)
11 ± 2 3 ± 1 19 ± 3 14 ± 3 19 ± 3 18 ± 8 19 ± 3 2 ± 1
4. 25 Oct–31 Oct
(week 4)
18 ± 3 4 ± 2 31 ± 3 21 ± 8 31 ± 3 13 ± 4 31 ± 3 4 ± 1
5. November 01 Nov–07 Nov
(week 5)
26 ± 8 9 ±13 31 ± 4 19 ± 5 31 ± 4 13 ± 1 31 ± 4 5 ± 1
6. 08 Nov–14 Nov
(week 6)
23 ± 3 5 ± 1 36 ± 2 33 ± 9 36 ± 2 15 ± 5 36 ± 2 6 ± 4
7. 15 Nov–21 Nov
(week 7)
25 ± 2 6 ± 2 39 ± 2 36 ± 8 39 ± 2 8 ± 7 39 ± 2 7 ±1
8. 22 Nov–28 Nov
(week 8)
36 ± 7 4 ± 1 43 ± 6 15 ± 6 43 ± 6 10 ± 3 43 ± 6 11 ± 6
9. December 29 Nov–05 Dec
(week 9)
29 ± 7 3 ± 1 43 ± 6 24 ± 5 47 ± 6 11 ± 6 47 ± 6 8 ± 2
10. 06 Dec–12 Dec
(week 10)
26 ± 8 3 ± 0 36 ± 5 19 ± 3 36 ± 5 14 ± 4 36 ± 5 5 ± 2
11. 13 Dec–19 Dec
(week 11)
27 ± 6 3 ± 1 32 ± 3 20 ± 14 32 ± 3 21 ± 8 32 ± 3 4 ± 2
12. 20 Dec–26 Dec
(week 12)
40 ± 6 3 ± 2 43 ± 3 11 ± 8 45 ± 3 32 ± 31 45 ± 3 14 ± 12
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and PM10 (0.35). RH and WS recorded weak correla-
tions with SO2, NO2, and O3 in the Knowledge park
region of Greater Noida (Table9).
In Vivek Vihar region, a strong and signifi-
cant correlation has been observed among PM2.5,
PM10, SO2, and NO2. However, O3 is weekly cor-
related with PM2.5 and PM10. Meteorological
parameters such as RH, WS, and WD were weekly
correlated with PM2.5, PM10, SO2, and O3 as given
in Table 9. The weak and negative correlations
were observed among WD and PM2.5 (−0.29),
PM10 (−0.23), SO2 (−0.49), NO2 (−0.19), and O3
(−0.93) and positive among RH and PM2.5 (0.22),
PM10 (0.28), and SO2 (0.34), respectively. NO2
has not shown any correlation with meteorologi-
cal parameters in the Vivek Vihar region of Noida
(Table8). In Sector 125, SO2 and NO2 exhibited
correlations with PM2.5 (0.13), PM10 (0.11), NO2
(−0.39), and O3 (−0.42) and PM2.5 (0.77), PM10
(0.55), SO2 (−0.39), and O3 (0.22), respectively.
RH showed a positive/negative correlation with
PM2.5 (0.39), PM10 (−0.11), SO2 (0.61), NO2
(−0.35), and O3 (−0.27), respectively.
4Oct-10Oct
11Oct- 17Oct
18Oct- 24Oct
25Oct- 31Oct
1Nov -7 Nov
8Nov -14Nov
15Nov-21Nov
22Nov-28Nov
29 Nov- 5Dec
6Dec-12Dec
13Dec-19Dec
20Dec-26Dec
0
5
10
15
20
25
30
35
40
45
50
(a)
Date
Concentration(µg m-3)
Dwarkha(SO2)
Satellite
Ground Based
4Oct-10Oct
11Oct- 17Oct
18Oct- 24Oct
25Oct-31Oct
1Nov -7 Nov
8Nov -14Nov
15Nov-21Nov
22Nov-28Nov
29 Nov- 5Dec
6Dec-12Dec
13Dec-19Dec
20Dec-26Dec
0
10
20
30
40
50
(b)
Date
Concentration(µg m-3)
Knowledge-park (SO2)
Satellite
Ground Based
4Oct-10Oct
11Oct- 17Oct
18Oct- 24Oct
25Oct- 31Oct
1Nov -7 Nov
8Nov -14Nov
15Nov-21Nov
22Nov-28Nov
29 Nov- 5Dec
6Dec-12Dec
13Dec-19Dec
20Dec-26Dec
0
10
20
30
40
50
60
70
(c)
Date
Concentration(µg m-3)
Sector 125(SO2)
Satellite
Ground Based
4Oct-10Oct
11Oct- 17Oct
18Oct- 24Oct
25Oct- 31Oct
1Nov -7 Nov
8Nov -14Nov
15No
v-21Nov
22No
v-28Nov
29 Nov- 5Dec
6Dec-12Dec
13Dec-19Dec
20Dec-26Dec
0
10
20
30
40
50
60
(d)
Date
Concentration(µg m-3)
Vivekvihar (SO2)Satellite
Ground Based
Fig. 3 Variation in SO2 (µg m−3)between ground and satellite-based data measurements in all the sampling stations
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Table 6 NO2 (µg m−3) forboth satellite and ground-based data
S. no. Months Time duration Dwarka Knowledge park III Sector 125 Vivek Vihar
Satellite Ground based Satellite Ground based Satellite Ground based Satellite Ground based
1. October 04 Oct–10 Oct
(week 1)
20 ± 5 26 ± 4 21 ± 3 19 ± 5 28 ± 7 3 ± 0 19 ± 7 29 ± 7
2. 11 Oct–17Oct
(week 2)
18 ± 3 29 ± 13 27 ± 12 23 ± 4 24 ± 6 2 ± 0 24 ± 6 28 ± 5
3. 18 Oct–24 Oct
(week 3)
29 ± 10 15 ± 5 20 ± 7 14 ± 4 34 ± 6 3 ± 1 34 ± 6 22 ± 4
4. 25 Oct–31 Oct
(week 4)
19 ± 4 21 ± 5 22 ± 5 31 ±10 24 ± 8 3 ± 1 24 ± 8 26 ± 4
5. November 01 Nov–07 Nov
(week 5)
24 ± 7 30 ± 4 18 ± 4 26 ± 6 29 ± 12 21 ± 3 29 ± 12 30 ± 2
6. 08 Nov–14 Nov
(week 6)
29 ± 12 39 ± 5 30 ± 21 44 ±16 28 ± 13 20 ± 3 28 ± 13 40 ± 7
7. 15 Nov–21 Nov
(week 7)
33 ± 23 39 ± 8 26 ± 11 18 ± 4 36 ± 28 11 ± 8 36 ± 28 82 ± 15
8. 22 Nov–28 Nov
(week 8)
43 ± 20 41 ±17 18 ± 3 16 ± 10 32 ± 17 8 ± 3 32 ± 17 42 ± 13
9. December 29 Nov–05 Dec
(week 9)
43 ± 20 34 ± 7 30 ± 23 19 ± 9 30 ± 15 3 ± 2 30 ± 15 33 ± 12
10. 06 Dec–12 Dec
(week 10)
42 ± 18 23 ± 2 30 ± 23 15 ± 4 43 ± 20 3 ± 1 29 ±15 30 ± 7
11. 13 Dec–19 Dec
(week 11)
40 ± 28 29 ± 10 29 ± 4 6 ± 8 50 ± 40 3 ± 5 50 ± 40 50 ± 50
12. 20 Dec–26 Dec
(week 12)
75 ± 43 39 ± 6 57 ± 39 11 ± 6 82 ± 59 1 ± 1 82 ± 59 44 ± 12
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Health risk assessment
A separate (children and adults) average daily dose
(ADD), hazard quotients (HQ), and excess life
cancer risk (ELCR) based on satellite and ground-
based measurements for both PM2.5 and PM10 are
mentioned in Tables10, 11, and 12.
Non-carcinogenic risk assessment
For satellite-based measurements of PM2.5 (adults),
ADD (µg kg−1 day−1) varied from 1.1 to 6.24 µg
kg−1 day−1, 0.1 to 5.8 µg kg−1 day−1, 0.1 to 5.8 µg
kg−1 day−1, and 0.1 to 5.8 µg kg−1 day−1 in Dwarka,
Knowledge park, Sector 125, and Vivek Vihar
regions, respectively (Table10). The satellite-based
measurements of PM2.5 (children), ADD (µg kg−1
day−1) varied from 0.3 to 19.1 µg kg−1 day−1, 0.3 to
17.8 µg kg−1 day−1, 0.3 to 17.8 µg kg−1 day−1, and
0.3 to 17.8 µg kg−1 day−1 in Dwarka, Knowledge
park, Sector 125, and Vivek Vihar, respectively
(Table11).
However, the ADD based on ground-based meas-
urements of PM2.5 (adults) varied from 2.6 to 15 µg
kg−1 day−1, 1.0 to 14.7 µg kg−1 day−1, 4.8 to 15.8
µg kg−1 day−1, and 2.45 to 19.6 µg kg−1 day−1 in
Dwarka, Knowledge park, Sector 125, and Vivek
Vihar, respectively (Table 10). The ADD based on
4Oct-10Oct
11Oct- 17Oct
18Oct- 24Oct
25Oct-31Oct
1Nov -7 Nov
8Nov -14Nov
15Nov-21Nov
22Nov-28Nov
29 Nov- 5Dec
6Dec-12Dec
13Dec-19Dec
20Dec-26Dec
-10
0
10
20
30
40
50
60
70
80
90
100
110
120
130
(a)
Date
Dwarka (NO2)
Satellite
Ground Based
Concentration(µg m-3)
4Oct-10Oct
11Oct- 17Oct
18Oct- 24Oct
25Oct- 31Oct
1Nov -7 Nov
8Nov -14Nov
15Nov-21Nov
22Nov-28Nov
29 Nov- 5Dec
6Dec-12Dec
13Dec-19Dec
20Dec-26Dec
-10
0
10
20
30
40
50
60
70
80
90
100 (b)
Date
Concentration(µg m-3)
Knowledgepark(NO2)
Satellite
Ground Based
4Oct-10Oct
11Oct- 17Oct
18Oct- 24Oct
25Oct-31Oct
1Nov -7 Nov
8Nov -14Nov
15Nov -21Nov
22Nov -28Nov
29 Nov- 5Dec
6Dec-12Dec
13Dec-19Dec
20Dec-26Dec
-10
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150(c)
Date
Concentration(µg m-3)
Sector 125(NO2)
satellite
Ground Based
4Oct-10Oct
11Oct-17Oct
18Oct-24Oct
25Oct-31Oct
1Nov -7 Nov
8Nov -14Nov
15No
v-21Nov
22No
v-28Nov
29 Nov- 5Dec
6Dec-12Dec
13Dec-19Dec
20Dec-26Dec
0
50
100
150
200
250 (d)
Date
Concentration(µg m-3)
VivakViher (NO2)
Satellite
Ground Based
Fig. 4 Variation in NO2 (µg m−3) between ground and satellite-based measurements in all the sampling station
Environ Monit Assess (2024) 196:342
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ground-based measurements of PM2.5 (children) var-
ied from 8.1 to 45.9 µg kg−1 day−1, 3.3 to 45 µg kg−1
day−1, 14.8 to 48.3 µg kg−1 day−1, and 7.6 to 60 µg
kg−1 day−1 in Dwarka, Knowledge park, Sector 125,
and Vivek Vihar regions, respectively (Table11).
For PM10, only ground-based measurements are
available for children and adults. For children, the
ADD varied from 123 to 313µg kg−1 day−1, 78.2 to
288.7µg kg−1 day−1, 122.3 to 274.2 µg kg−1 day−1,
and 94 to 353.8µg kg−1 day−1 in Dwarka, Knowledge
park, Sector 125, and Vivek Vihar regions, respec-
tively (Table 12). For adults, the ADD varied from
10.1 to 25.6 µg kg−1 day−1, 6.4 to 23.6 µg kg−1 day−1,
10 to 22.4 µg kg−1 day−1, and 7.7 to 28.9 µg kg−1
day−1 in Dwarka, Knowledge park, Sector 125, and
Vivek Vihar regions, respectively.
Hazardous quotient for PM2.5 and PM10
For PM2.5 PM10, the hazardous quotient for adults
and children is same in the locations. The mean HQ
values for satellite and ground-based measurement
were 3.6, 3.5, 3.5, and 3.5 and 17.4, 14.9, 16.8, and
21.2 in Dwarka, Knowledge park, Sector 125, and
Vivek Vihar regions, respectively (Tables11 and 12).
However, the mean HQ values of PM10 (for adults
and children) for ground-based measurement were
3.1, 2.5, 2.9, and 3.4 in Dwarka, Knowledge park,
Sector 125, and Vivek Vihar regions, respectively
(Table12). The mean HQ values (PM2.5) of ground-
based measurements were higher as compared to sat-
ellite-based measurement. The ground-based meas-
urement values of HQ values for PM10 were lower as
compared to HQ values of PM2.5.
Excess lifetime cancer risk (ELCR)
As per the United State Environmental Protection
Agency (USEPA) and World Health Organization, the
recommended the value of ELCR is 1 × 10−6 and 1 ×
10−6 to 1 × 10−5, respectively (Tables11 and 12). The
ELCR of ground-based measurements was observed
to be higher as compared to satellite-based measure-
ments. The ELCR for PM10 was not estimated due to
the unavailability of cancer slop and integrated unit risk
Table 7 Ground-based
monitoring data of O3 (µg
m−3)
S. no. Months Time duration Dwarka Knowledge park III Sector 125 Vivek Vihar
1. October 04 Oct–10 Oct
(week 1)
28 ± 10 66 ± 10 46 ± 16 34 ± 7
2. 11 Oct–17Oct
(week 2)
15 ± 6 61 ± 39 76 ± 17 31 ± 22
3. 18 Oct–24 Oct
(week 3)
11 ± 6 75 ± 26 73 ± 20 26 ± 9
4. 25 Oct–31 Oct
(week 4)
35 ± 13 77 ± 36 116 ± 13 38 ± 4
5. November 01 Nov–07 Nov
(week 5)
47 ± 10 98 ± 11 117 ± 24 36 ± 4
6. 08 Nov–14 Nov
(week 6)
40 ± 15 94 ± 33 52 ± 44 36 ± 13
7. 15 Nov–21 Nov
(week 7)
52 ± 3 113 ± 14 32 ± 35 25 ± 12
8. 22 Nov–28 Nov
(week 8)
46 ± 7 95 ± 22 87 ± 39 36 ± 10
9. December 29 Nov–05 Dec
(week 9)
25 ± 13 50 ± 35 56 ± 33 13 ± 9
10. 06 Dec–12 Dec
(week 10)
33 ± 4 68 ± 9 60 ± 7 21 ± 4
11. 13 Dec–19 Dec
(week 11)
36 ± 6 65 ± 11 81 ± 23 22 ± 11
12. 20 Dec–26 Dec
(week 12)
27 ± 7 48 ± 35 19 ± 18 26 ± 12
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factors. For PM2.5, the ELCR for children was slightly
higher as compared to adults (Tables10 and 11).
Discussion
The precise aim of the study is to determine whether
satellite-based or ground-based measurements should
be used to evaluate air pollutants. The measurement
methods directly influence the health risk assess-
ment and health hazards. The mean concentration of
ground-based monitoring of PM2.5 was higher than
the recommended limits of NAAQS for industrial
(annual (120 µg m−3)/24 h (150 µg m−3)) and resi-
dential rural (annual (60 µg m−3)/24 h (100 µg m−3))
areas (NAAQS 2021). The satellite-based PM2.5 con-
centration monitoring was below the recommended
NAAQS limits for industrial areas but exceeded
them for residential rural areas (NAAQS 2021). The
ground-based concentration of PM10 was higher than
the recommended limits (NAAQS 2021) for residen-
tial rural areas (annual (140 µg m−3)/24 h (200 µg
m−3)) and lower than industrial areas (annual (140
µg m−3)/24 h (200 µg m−3)) except Vivek Vihar. The
PM2.5 elemental concentration was almost two times
higher than the PM10 elemental concentration that
confirmed the local elevating anthropogenic activi-
ties in the studied region. The anthropogenic activi-
ties (vehicular emission, construction, and firecracker
activities) and long range transportation of various
pollutants continuously elevate the concentration of
PM2.5 and PM10 (Mushtaq etal., 2023).
For PM2.5, the ground-based measurements of
Dwarka, Knowledge Park, Sector 125, and Vivek
Vihar were 2.45, 2.53, 2.72, and 3.37 folds higher as
compared to satellite-based measurements. Due to
their close proximity to pollution sources, high spa-
tial resolution, and accessibility to real-time data,
ground-based measures are typically thought to be
more accurate at the local level (Yan et al., 2023).
Hence, they are extremely pertinent for determin-
ing acute health hazards, particularly in metropolitan
regions where pollution exposure is most intense. The
observed ground-based PM2.5 and PM10 elemental
concentrations were slightly higher than neighbor-
ing Indo-Gangetic region’s cities as Agra and Delhi
(Das et al., 2020; Pipal et al., 2014), and almost
tenfolds (for PM2.5) and five to sixfolds (for PM10)
higher than the elemental concentration recorded in
Table 8 Metrological parameters of all sites
S. no. Months Duration Dwarka, Delhi Knowledge park III, Greater
Noida
Sector 125, Noida Vivek Vihar, Delhi
Temp. RH WS WD Temp. RH WS WD Temp. RH WS WD Temp. RH WS WD
1. October 4 Oct–10 Oct (week 1) 30 66 0.45 249 32 58 0.37 NA NA 73 NA NA NA 71 0.55 222
2. 11 Oct–17 Oct (week 2) 28 56 0.45 225 30 54 0.72 103 NA 67 NA NA NA 62 0.86 211
3. 18 Oct–24 Oct (week 3) 25 68 0.45 224 29 62 1.43 79 NA 76 NA NA NA 75 0.71 194
4. 25 Oct–31 Oct (week 4) 22 63 0.81 242 31 66 1.29 93 NA 69 NA NA NA 66 0.55 240
5. November 1 Nov–7 Nov (week 5) 22 63 0.14 193 33 76 2.29 76 NA 72 NA NA NA 73 0.85 178
6. 8 Nov–14 Nov (week 6) 20 67 0.91 211 31 59 0.71 60 NA 69 NA NA NA 69 0.85 241
7. 15 Nov–21 Nov (week 7) 19 63 0.82 196 32 57 0.86 75 NA 71 NA NA NA 69 0.71 207
8. 22 Nov–28 Nov (week 8) 19 65 0.14 178 32 56 0.90 101 NA 72 NA NA NA 70 0.85 144
9. December 29 Nov–5 Dec (week 9) 18 76 0.26 214 32 64 0.86 72 NA 80 NA NA 75 0.59 228
10. 6 Dec–12 Dec (week 10) 17 70 0.36 237 33 59 1.03 70 NA NA NA NA NA 70 0.58 229
11. 13 Dec–19 Dec (week 11) 14 68 0.45 222 32 59 1.57 91 NA NA NA NA NA 67 0.55 190
12. 20 Dec–26 Dec (week 12) 15 73 0.32 149 33 64 0.86 111 NA NA NA NA NA 78 0.71 159
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other countries as China (PM2.5, 16.41–38.8 µg m−3;
PM10, 40.7–98.7 µg m−3), USA (PM2.5, 2.79–21.64
µg m−3; PM10, 7.94–61.06 µg m−3), and Iran (PM2.5,
22.6–39.5 µg m−3; PM10, 62.5–104.3 µg m−3), respec-
tively (Filonchyk et al., 2016; Yang et al., 2018;
Yunesiana etal., 2019).
There is significant variation between the con-
centration of SO2 in the study area as determined
by ground-based and satellite-based methods, with
satellite-based data consistently indicating higher
levels. The highest SO2 concentration (36 ± 8 µg
m−3) was measured using ground-based instruments
between November 15 and November 21 (week 7)
at KP-III (Fig.3). Reduced air circulation caused by
the high SO2 content made it possible for gaseous
pollutants to persist in the atmosphere for longer
duration (Mbengue et al., 2023). Due to growing
urbanization, industrial emissions, and coal-based
power plants, India has seen rising SO2 levels, par-
ticularly in industrialized areas (Sahu etal., 2023;
Guttikunda and Jawahar, 2014). SO2 levels in urban
areas like Delhi are higher than their recommended
limits, with a large contribution coming from indus-
trial sources and vehicular emissions (Sahu et al.,
2023). Except Dwarka, the rest of the ground-based
monitoring stations showed a higher concentration
of SO2 as compared to China (neighbor of India)
and US studies (EPA 2021; Javed etal., 2022).
The ground-based NO2 concentration varied rang-
ing from 21 ± 3 to 82 ± 157 µg m−3. Vivek Vihar
recorded the highest NO2 concentration (82 ± 157
µg m−3). Satellite-based observations showed vari-
ances as well, with levels varying from 57 to 82 µg
m−3 in various places. In the studied regions, ground-
based NO2 measurements were lower as compared
to satellite-based measurements, and the results are
in accordance with the previous study conducted by
Bassani et al. (2023). As compared to India, China
and the UK have also witnessed elevated NO2 levels,
with a concentration range of 11.54–42.59 µg m−3
N
NNE
NE
ENE
E
ESE
SE
SSE
S
SSW
SW
WSW
W
WNW
NW
NNW
0
2
4
6
8
10
12
14
16
18
20
22
Calms: 0
DirectionWin d
>= 1.6
1.4-1.6
1.2-1.4
1-1.2
0.8-1
0.6-0.8
0.4-0.6
Calms:
0
Dwarka (week 1- week 4)(A)N
NNE
NE
ENE
E
ESE
SE
SSE
S
SSW
SW
WSW
W
WNW
NW
NNW
0
2
4
6
8
10
12
14
16
18
20
22
24
Calms: 0
DirectionWin d
>= 5
4-5
3-4
2-3
1-2
0-1
Calms:
0
Knowledgepark(Week1- Week 4) (A )N
NNE
NE
ENE
E
ESE
SE
SSE
S
SSW
SW
WSW
W
WNW
NW
NNW
0
2
4
6
8
10
12
14
16
18
20
Calms: 0
DirectionWind
>= 0.6
0.58 -0.6
0.56 -0.58
0.54 -0.56
0.52 -0.54
0.5-0.52
0.48 -0.5
0.46 -0.48
0.44 -0.46
Calms:
0
VivakVihar (Week1-Week4)(A )
N
NNE
NE
ENE
E
ESE
SE
SSE
S
SSW
SW
WSW
W
WNW
NW
NNW
0
2
4
6
8
10
12
14
16
18
20
Calms: 0
DirectionWind
>= 1.4
1.3-1.4
1.2-1.3
1.1-1.2
1-1.1
0.9-1
0.8-0.9
0.7-0.8
0.6-0.7
0.5-0.6
Calms:
0
Dwarkha(week 5- week 8)(B)N
NNE
NE
ENE
E
ESE
SE
SSE
S
SSW
SW
WSW
W
WNW
NW
NNW
0
2
4
6
8
10
12
14
16
18
20
22
24
26
Calms: 0
DirectionWind
>= 4.5
4-4.5
3.5-4
3-3.5
2.5-3
2-2.5
1.5-2
1-1.5
0.5-1
0-0.5
Calms:
0
Knowledge Park (Week5-Week8)(B)N
NNE
NE
ENE
E
ESE
SE
SSE
S
SSW
SW
WSW
W
WNW
NW
NNW
0
2
4
6
8
10
12
14
16
18
Calms: 0
DirectionWind
>= 0.62
0.6-0.62
0.58 -0.6
0.56 -0.58
0.54 -0.56
0.52 -0.54
0.5-0.52
0.48 -0.5
0.46 -0.48
Calms:
0
VivakVihar (Week5-Week8)(B)
N
NNE
NE
ENE
E
ESE
SE
SSE
S
SSW
SW
WSW
W
WNW
NW
NNW
0
2
4
6
8
10
12
14
16
18
20
22
24
Calms: 0
DirectionWin d
>= 1.3
1.2-1.3
1.1-1.2
1-1.1
0.9-1
0.8-0.9
0.7-0.8
0.6-0.7
0.5-0.6
0.4-0.5
Calms:
0
Dwarka (Week9-Week12) (C)
N
NNE
NE
ENE
E
ESE
SE
SSE
S
SSW
SW
WSW
W
WNW
NW
NNW
0
2
4
6
8
10
12
14
16
18
20
22
24
26
Calms: 0
DirectionWin d
>= 5
4-5
3-4
2-3
1-2
0-1
Calms:
0
Kowledge park (Week9-Week12) (C)N
NNE
NE
ENE
E
ESE
SE
SSE
S
SSW
SW
WSW
W
WNW
NW
NNW
0
2
4
6
8
10
12
14
16
18
20
Calms: 0
DirectionWin d
>= 0.62
0.6-0.62
0.58 -0.6
0.56 -0.5 8
0.54 -0.56
0.52 -0.54
0.5-0.52
0.48 -0.5
0.46 -0.48
0.44 -0.46
Calms:
0
Vivak Vihar(Week 9- Week 12)(C)
Fig. 5 Wind rose diagrams for Dwarka, Knowledge park III, and Vivek Vihar
Environ Monit Assess (2024) 196:342
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and 2.26–84.05 µg m−3 (Filonchyk etal., 2016; Nie-
psch etal., 2022).
Unlike PM2.5 and PM10, the ground-based SO2
and NO2 concentrations were different for all sat-
ellite-based monitoring stations. The ground-based
monitoring concentration of SO2 and NO2 was lower
than the satellite-based monitoring concentration and
higher than the recommended limits NAAQS (2021)
for industrial (annual (80 µg m−3)/24 h (120 µg m−3))
and residential rural areas (annual (60 µg m−3)/24
h (80 µg m−3)). The O3 concentration followed the
same pattern of PM2.5 and PM10, and its satellite con-
centration is almost same in all the monitoring sta-
tions. The ground-based monitoring concentration
of O3 was lower than the recommended limit (60 µg
m−3) of World Health Organization (WHO, 2021).
Table 9 Represents the
Pearson’s correlation of all
sampling sites
** Correlation is significant at the 0.01 level (1-tailed)
* Correlation is significant at the 0.05 level (1-tailed)
PM2.5 PM10 SO2NO2O3Temp RH WS WD
Location 1: Dwarka
PM2.5 1 0.95** 0.45 0.91** 0.61* −0.42 0.16 −0.72 0.63**
PM10 0.95** 1 .640* 0.79** 0.66* −0.32 0.79 0.48 −0.58*
SO20.45 0.64* 1 0.25 0.72* −0.07 −0.23 −0.03 −0.29
NO20.91** 0.79** 0.25 1 0.56 −0.43 0.10 −0.99 0.72*
O30.61* 0.66* 0.72** 0.56 1 −0.41− −0.13 0.14 −0.32
Temp −0.42 0.32 −0.07 −0.43 −0.41 1 −0.57 0.02 0.51
RH 0.16 0.79 −0.23 0.10 −0.13 −0.57 1 −0.19 −0.22
WS −0.72 0.48 −0.03 −0.99 0.14 0.02 −0.19 1 0.40
WD 0.63** −0.58* −0.29 0.72** −0.32 0.51 −0.22 0.40 1
Location 2: Knowledge park
PM2.5 1 0.89** 0.34 0.48 0.30 0.39 0.11 −0.52 -
PM10 0.89** 1 .477 0.62* . 0.25 0.35 0.26* −0.59 -
SO20.34 0.47 1 0.54* 0.49* −0.09 −0.27 −0.28 -
NO20.48 0.62* 0.54* 1 0.390 0.22 0.17 −0.90 -
O30.30 0.25 0.49* 0.39 1 −0.002 0.05 0.25 -
Temp 0.39 0.35 −0.09 0.22 −0.002 1 0.31 0.11 -
RH 0.11 0.26* −0.27 0.17 0.05 0.31 1 0.75** -
WS −0.52 −0.59 −0.28 −0.90 0.25 0.11 0.75* 1 -
Location 3: Sector 125
PM2.5 1 0.84** 0.13 0.77** 0.19 - 0.39 -
PM10 0.84** 1 0.11 0.55 0.22 - −0.11 - -
SO20.13 0.11 1 −0.39 −0.42 - 0.61 - -
NO20.77** 0.55 −0.39 1 0.22 - −0.35 - -
O30.19 0.22 −0.42 0.22 1 - −0.27 - -
RH 0.39 −0.11 0.61 −0.35 −0.27 - 1 - -
Location 4: Vivek Vihar
PM2.5 1 0.94** 0.65* 0.42 0.71 - 0.22 −0.48 −0.29
PM10 0.94** 1 0.58* 0.63* −0.05 - 0.28 0.38 −0.23
SO20.65* 0.58* 1 0.26 −0.004 - 0.34 0.22 −0.49
NO20.42 0.63* 0.26 1 −0.20 - −0.06 0.06 −0.19
O30.71 −0.05 −.004 −0.20 1 - −0.34 0.45 −0.93
RH 0.22 0.28 0.34 −0.06 −0.34 - 1 −0.78 −0.36
WS −0.48 0.38 0.22 0.06 0.45 - −0.78 1 −0.41
WD −0.29 −0.23 −0.49 −0.19 −0.93 - −0.36 −0.41 1
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Table 10 Non-carcinogenic and carcinogenic risk assessment from PM2.5 for adults
S.
no.
Months Time
dura-
tion
Dwarka Knowledge park Sector 125 Vivek Vihar
Satellite Ground based Satellite Ground based Satellite Ground based Satellite Ground based
ADD HQ ECR ADD HQ ECR ADD HQ ECR ADD HQ ECR ADD HQ ECR ADD HQ ECR ADD HQ ECR ADD HQ ECR
1October Week
1
1.67 3.06 1.07 8.3 15.3 5.36 2.1 3.85 1.3 4.6 8.5 3.0 2.1 3.85 1.3 10.8 19.7 6.9 2.1 3.85 1.3 10.6 19.5 6.9
2Week
2
1.77 3.25 1.14 7.6 13.9 4.88 1.7 3.16 1.1 8.7 16.0 5.6 1.7 3.16 1.1 8.5 15.7 5.5 1.7 3.16 1.1 10.1 18.4 6.5
3Week
3
1.24 2.27 0.80 2.6 4.8 1.70 1.4 2.56 0.9 1.1 2.0 0.7 1.4 2.56 0.9 6.5 11.9 4.2 1.4 2.56 0.9 2.5 4.5 1.6
4Week
4
2.80 5.13 1.80 5.4 10.0 3.49 2.4 4.44 1.6 5.7 10.5 3.7 2.4 4.\4 1.6 9.0 16.6 5.8 2.4 4.44 1.6 3.4 6.2 2.2
5Novem-
ber
Week
5
6.24 11.44 4.01 12.2 22.4 7.85 5.8 10.65 3.7 11.4 21.0 7.4 5.8 10.65 3.7 15.7 28.9 10.1 5.8 10.65 3.7 14.3 26.2 9.2
6Week
6
4.03 7.40 2.59 15.0 27.5 9.65 3.8 6.90 2.4 14.7 27.0 9.5 3.8 6.90 2.4 15.8 29.0 10.2 3.8 6.90 2.4 19.6 36.0 12.6
7Week
7
2.47 4.54 1.59 11.3 20.8 7.30 2.3 4.14 1.5 9.0 16.6 5.8 2.3 4.14 1.5 6.6 12.1 4.3 2.3 4.14 1.5 12.8 23.5 8.2
8Week
8
1.34 2.47 0.86 11.4 21.0 7.37 1.4 2.56 0.9 10.5 19.3 6.8 1.4 2.56 0.9 8.7 15.9 5.6 1.4 2.56 0.9 15.0 27.5 9.7
9Decem-
ber
Week
9
0.91 1.68 0.59 10.6 19.5 6.85 1.0 1.78 0.6 8.4 15.4 5.4 1.0 1.78 0.6 6.7 12.2 4.3 1.0 1.78 0.6 12.6 23.2 8.1
10 Week
10
0.43 0.79 0.28 7.0 12.9 4.53 0.4 0.69 0.2 5.4 10.0 3.5 0.4 0.69 0.2 4.8 8.9 3.1 0.4 0.69 0.2 8.8 16.2 5.7
11 Week
11
0.11 0.20 0.07 9.2 16.9 5.92 0.1 0.20 0.1 6.7 12.2 4.3 0.1 0.20 0.1 6.6 12.0 4.2 0.1 0.20 0.1 11.1 20.3 7.1
12 Week
12
0.22 0.39 0.14 12.8 23.6 8.27 0.3 0.49 0.2 11.2 20.5 7.2 0.3 0.49 0.2 10.1 18.5 6.5 0.3 0.49 0.2 17.7 32.4 11.4
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Table 11 Non-carcinogenic and carcinogenic risk assessment from PM2.5 for children
S. no. Months Time Duration Dwarka Knowledge park Sector 125 Sector 125
Satellite Ground based Satellite Ground based Satellite Ground based Satellite Ground based
ADD ECR ADD ECR ADD ECR ADD ECR ADD ECR ADD ECR ADD ECR ADD ECR
1 October Week 1 20.4 1.25 101.9 6.26 25.6 1.6 56.5 3.5 25.6 1.6 131.5 8.1 25.6 1.6 130.2 8.0
2 Week 2 21.7 1.33 92.7 5.69 21.0 1.3 106.5 6.5 21.0 1.3 104.5 6.4 21.0 1.3 123.0 7.5
3 Week 3 15.1 0.93 32.2 1.98 17.1 1.0 13.2 0.8 17.1 1.0 79.6 4.9 17.1 1.0 30.2 1.9
4 Week 4 34.2 2.10 66.4 4.08 29.6 1.8 69.7 4.3 29.6 1.8 110.5 6.8 29.6 1.8 41.4 2.5
5 November Week 5 76.3 4.68 149.3 9.16 71.0 4.4 140.1 8.6 71.0 4.4 192.7 11.8 71.0 4.4 174.9 10.7
6 Week 6 49.3 3.03 183.5 11.26 46.0 2.8 180.2 11.1 46.0 2.8 193.3 11.9 46.0 2.8 240.0 14.7
7 Week 7 30.2 1.86 138.7 8.52 27.6 1.7 110.5 6.8 27.6 1.7 80.9 5.0 27.6 1.7 156.5 9.6
8 Week 8 16.4 1.01 140.1 8.60 17.1 1.0 128.9 7.9 17.1 1.0 105.9 6.5 17.1 1.0 183.5 11.3
9December Week 9 11.2 0.69 130.2 7.99 11.8 0.7 102.6 6.3 11.8 0.7 81.5 5.0 11.8 0.7 154.5 9.5
10 Week 10 5.3 0.32 86.1 5.29 4.6 0.3 66.4 4.1 4.6 0.3 59.2 3.6 4.6 0.3 107.8 6.6
11 Week 11 1.3 0.08 112.4 6.90 1.3 0.1 81.5 5.0 1.3 0.1 80.2 4.9 1.3 0.1 135.5 8.3
12 Week 12 2.6 0.16 157.2 9.65 3.3 0.2 136.8 8.4 3.3 0.2 123.6 7.6 3.3 0.2 216.3 13.3
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Table 12 Non-carcinogenic and carcinogenic risk assessment from PM10 for adults and children
S.No. Months Time
duration
Dwarka Knowledge Park Sector 125 Vivek Vihar
Chid Adult Chid Adult Chid Adult Chid Adult
ADD HQ ADD HQ ADD HQ ADD HQ ADD HQ ADD HQ ADD HQ ADD HQ
1 October Week 1 185.4 2.8 15.2 2.8 167.7 2.5 13.7 2.5 141.4 2.1 11.6 2.1 177.5 2.7 14.5 2.7
2 Week 2 162.4 2.4 13.3 2.4 140.1 2.1 11.4 2.1 193.3 2.9 15.8 2.9 157.8 2.4 12.9 2.4
3 Week 3 123.0 1.8 10.1 1.8 78.2 1.2 6.4 1.2 208.4 3.1 17.0 3.1 94.0 1.4 7.7 1.4
4 Week 4 174.9 2.6 14.3 2.6 150.6 2.3 12.3 2.3 232.1 3.5 19.0 3.5 119.7 1.8 9.8 1.8
5Novem-
ber
Week 5 275.5 4.1 22.5 4.1 214.4 3.2 17.5 3.2 259.1 3.9 21.2 3.9 257.1 3.9 21.0 3.9
6 Week 6 313.0 4.7 25.6 4.7 288.7 4.3 23.6 4.3 274.2 4.1 22.4 4.1 353.8 5.3 28.9 5.3
7 Week 7 242.0 3.6 19.8 3.6 184.8 2.8 15.1 2.8 155.2 2.3 12.7 2.3 235.4 3.5 19.2 3.5
8 Week 8 225.5 3.4 18.4 3.4 139.4 2.1 11.4 2.1 194.0 2.9 15.9 2.9 315.6 4.7 25.8 4.7
9Decem-
ber
Week 9 213.7 3.2 17.5 3.2 182.1 2.7 14.9 2.7 169.6 2.5 13.9 2.5 265.0 4.0 21.7 4.0
10 Week 10 152.5 2.3 12.5 2.3 115.7 1.7 9.5 1.7 122.3 1.8 10.0 1.8 176.2 2.6 14.4 2.6
11 Week 11 191.3 2.9 15.6 2.9 147.9 2.2 12.1 2.2 138.7 2.1 11.3 2.1 219.6 3.3 18.0 3.3
12 Week 12 248.5 3.7 20.3 3.7 211.1 3.2 17.3 3.2 238.7 3.6 19.5 3.6 322.2 4.8 26.3 4.8
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As O3 adheres to a distinct pattern, with its concen-
tration peaking during daylight hours, when exposed
to sunlight and decreasing during the night (Targino
etal., 2023), in the studied region, O3 showed higher
concentration in Sector 125 and Knowledge park as
compared to Chinese cities (Filonchyk etal., 2016).
The high levels of O3 may be due to photochemical
generation of O3 due to higher concentration of NO2.
For a meteorological point of view, The PM10 concen-
tration showed strong correlations with meteorological
parameters (T (°C), RH (%)). The trace gases showed the
least correlation with meteorological parameters except
in some cases of WS and WD. WS (ms−1), WD) as com-
pared to PM2.5 in most of the ground-based monitoring
stations. As per the results, most air pollutants recorded
a positive correlation with RH and a negative correlation
with WD. The variability in the elemental concentration
of particulate matter and trace gases for a particular loca-
tion is significantly impacted by wind direction (WD)
and other meteorological parameters (Mushtaq et al.,
2023; Goyal etal., 2023). In the studied region, WD is a
prominent factor and poses a vital effect to alter the con-
centration of these particulate matter and trace gases by
transporting these from one location to another (Fig 5).
For health risk assessment, it has been observed
that the mean HQ values for both satellite and ground-
based measurements were greater than 1 and 10 and
depict higher non-carcinogenic risk from the exposure
of PM2.5 and PM10 in the studied region. Umpteen
ground-based measurements of ELCR were higher
than the standard recommended values (1 × 10−6) of
USEPA indicating that both age groups are in chronic
risk from PM2.5 exposures. Similar findings, along with
elevated values of HQ and ELCR in the ambient air,
have been documented in prior studies conducted in the
USA, Iran, Thailand, and Saudi Arabia (Tiwari etal.,
2023; Lestiani etal., 2023; Amnuaylojaroen and Para-
sin 2023; Algarni etal., 2021; Yunesian etal., 2019).
Conclusion
The ground-based derived monitoring concentrations
of PM2.5 and PM10 were higher than those of satellite-
based measurements. It has been noted that the satel-
lite monitoring is beneficial for tracking the long-range
transport of particulate matter from diverse regions.
Nevertheless, it does not offer details about the spe-
cific pollution levels at the ground. The concentration
of SO2 and NO2 was higher in the satellite-based
monitoring. It has been observed that monitoring
sensors based on satellites produced nearly identical
results for particulate matter and trace gases in loca-
tions with shorter distances (20–25 km) as Knowledge
park III, Vivek Vihar, and Sector 125 regions. Overall,
the measured particulate matter and trace gases from
all of the monitoring stations have not shown any pat-
tern of correlation with meteorological parameters,
and further, the correlations only depend upon local
geographical conditions and regional anthropogenic
activities. As per the health point of view, the mean
hazardous quotient (HQ) and excess lifetime cancer
risk (ELCR) were greater than their standard recom-
mended values (USEPA 2018, USEPA 2009a, 2009b),
indicating the high carcinogenic and non-carcinogenic
risk for inhabitants of the studied region.
For a thorough evaluation of air quality across the
entire region, it is advisable to employ a blend of satel-
lite (where ground-based measurements are not possi-
ble) and ground-based measurements. From a health risk
assessment perspective, utilizing ground-based moni-
toring instruments is essential to guarantee the accu-
rate evaluation of health hazards. Subsequent research
efforts should prioritize reducing the measurement gaps
between satellite and ground-based measurements.
Author contribution Zainab Mushtaq, Pargin Bangotra, and
Alok Sagar Gautam conceptualized the manuscript. Zainab
Mushtaq, Pargin Bangotra, Suman, Karan Singh, Yogesh
Kumar, and Poonam Jain carried out experimental work in
the field and laboratory. Pargin Bangotra performed statisti-
cal and graphical analysis of data. Pargin Bangotra and Zainab
Mushtaq wrote the manuscript. Zainab Mushtaq, Pargin Ban-
gotra, Suman, Manish Sharma, Alok Sagar Gautam, Sneha
Gautam, Yogesh Kumar, and Poonam Jain carried out the
interpretation of results. All authors contributed extensively to
a discussion about this work and in reviewing the manuscript.
Data availability The data generated and analyzed in this
study are available from the corresponding author upon reason-
able request.
Declarations
Competing interests The authors declare no competing interests.
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