Content uploaded by Abduldaem S. Alqasemi
Author content
All content in this area was uploaded by Abduldaem S. Alqasemi on Sep 03, 2021
Content may be subject to copyright.
Impact of COVID-19 lockdown upon the air quality and surface urban
heat island intensity over the United Arab Emirates
Abduldaem S. Alqasemi
a,
⁎, Mohamed E. Hereher
b,c
, Gordana Kaplan
d
,
Ayad M. Fadhil Al-Quraishi
e
, Hakim Saibi
f
a
Geography and Urban Sustainability, College ofHumanities & Social Science, UAEU, Al-Ain, United Arab Emirates
b
Geography Department, College of Arts and Social Sciences, Sultan Qaboos University, Muscat, Oman
c
Environmental Sciences Dept., Faculty of Science, Damietta University, New Damietta, Egypt
d
Institute of Earth and Space Sciences, EskisehirTechnical University,Eskisehir, Turkey
e
Surveying and Geomatics Engineering Department, Faculty of Engineering, Tishk International University, Erbil, Kurdistan Region, Iraq
f
Geology Department, College of Science, United Arab Emirates University, Al-Ain, United Arab Emirates
HIGHLIGHTS
•COVID-19 pandemic impact lockdown
events in NEUAE greatly reduced the
NO
2
, AOD and SUHII concentrations.
•All Emirates in NEUAE showed decline
in NO2, AOD and SUHII concentrations
during lockdown period.
•The average NO2, AOD and SUHII con-
centrations were decreased by 23.7%,
3.7% and 19.2%, respectively.
•Validation for results showed a signifi-
cant linear correlations between esti-
mated and measured values.
GRAPHICAL ABSTRACT
abstractarticle info
Article history:
Received 13 October 2020
Received in revised form 14 November 2020
Accepted 5 December 2020
Available online 25 December 2020
Editor: Prof. Pavlos Kassomenos
Keywords:
COVID-19
NO
2
AOD
SUHII
Lockdown
Northern emirates
The 2019 pandemic of Severe Acute Respiratory Syndrome-Corona Virus Diseases (COVID-19) has posed a sub-
stantial threat to public health and major global economic losses. The Northern Emirates of the United Arab
Emirates (NEUAE) had imposed intense preventive lockdown measures. On the first of April 2020, a lockdown
was implemented. It was assumed, due to lower emissions, that the air quality and Surface Urban Heat Island
Intensity (SUHII) had been strengthened significantly. In this research, three parameters for Nitrogen Dioxide
(NO
2
), Aerosol Optical Depth (AOD), and SUHII variables were examined through the NEUAE. we evaluated
the percentage of the change in these parameters as revealed by satellite data for 2 cycles in 2019 (March 1st
to June 30th) and 2020 (March 1st to June 30th). The core results showed that during lockdown periods, the
average of NO
2
, AOD, and SUHII levels declined by 23.7%, 3.7%, and 19.2%, respectively, compared to the same
period in 2019. Validation for results demonstrates a high agreement between the predicted and measured
values. The agreement was as high as R
2
=0.7, R
2
=0.6, and R
2
=0.68 for NO
2
, AOD, and night LST, respectively,
indicating significant positive linear correlations. The current study concludes thatdue to declining automobile
and industrial emissions in the NEUAE, the lockdown initiatives substantially lowered NO
2
, AOD, and SUHII. In
addition, the aerosols did not alter significantly since they are often linked to the natural occurrence of dust
Science of the Total Environment 767 (2021) 144330
⁎Corresponding author.
E-mail address: a.alqasemi@uaeu.ac.ae (A.S. Alqasemi).
https://doi.org/10.1016/j.scitotenv.2020.144330
0048-9697/© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Contents lists available at ScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
storms throughout this time of the year. The pandemic is likely to influence several policy decisions to introduce
strategies to control air pollution and SUHII. Lockdownexperiences may theoretically play a key role in the future
as a possiblesolution for air pollution and SUHII abatement.
© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://
creativecommons.org/licenses/by/4.0/).
1. Introduction
The emergent Coronavirus disease 2019 (COVID-19) is a transmitta-
ble disorder characterized by severe acute respiratory syndrome
coronavirus-2 (SARS-CoV-2) (Islam et al., 2020). By massive human-
to-human transmission, COVID-19 has deeply hit the world and
prompted a spike in the human mortality rate and massive economic
casualties around the world (Bukhari and Jameel, 2020). The number
of cases of Covid-19 globally hit approximately 34 million by the first
of October 2020, while the number of deaths reached 1 million (WHO,
2020a). In late 2019, the first case of COVID-19 was identified in China
and since then has spread very quickly throughout the world (Q. Li
et al., 2020). On 11 March 2020, the World Health Organization
(WHO) announced the novel coronavirus disease as a pandemic
(WHO, 2020b). The WHO COVID-19 dashboard (https://covid19.who.
int/) can be used to find the specifics of global COVID-19 cases. The
first COVID-19 infection was reported in the United Arab Emirates
(UAE) on 29 January 2020, and the first death was recorded on 21
March 2020. Since then, as per the Ministry of Health and Prevention
(https://www.mohap.gov.ae/), there has been an alarming surge in ac-
tive and death cases due to COVID-19.
In people with cardiovascular and respiratory disorders, the mortal-
ity risk of COVID-19 is substantially higher (Archer et al., 2020;Isaifan,
2020;Y. Zhu et al., 2020). Cardiovascular and respiratory disorders are
also closely related to air pollution. Emissions leading to respiratory
health conditions from primary pollutants containing particulate matter
(aerosols) and gases such as nitrogen dioxide (NO
2
) also have adverse
environmental effects like soil and water acidification (Griffinetal.,
2019;Mulenga and Siziya, 2019;Xu et al., 2020). The WHO (2020c) re-
ports that approximately 4.2 million inhabitants die worldwide per year
from factors primarily related to air pollution, which is currently in-
creased, related to COVID-19 infected patients (Conticini et al., 2020;
Wu et al., 2020). Also, recent worldwide studies on the effect of climate
on the spread of the COVID-19 (Briz-Redón and Serrano-Aroca, 2020),
showed that temperature and humidity are not crucial factors in the
COVID-19 transmission, while precipitation, radiation, and wind speed
have not been investigated in details.
Urban Heat Island (UHI) is among the most noticeable and fre-
quently reported urbanization climatological consequences, by which
urban and suburban regions are hotter than surrounding areas (rural/
nonurban) (Hu and Brunsell, 2013;Miles and Esau, 2017). The negative
influence of UHI is extensively described in the literature. For example,
UHI causes rises in energy demand (Alghamdi and Moore, 2015), which
implicitly leads to global climate change (Alghamdi and Moore, 2015),
environmental degradation (Lin et al., 2017), air pollution (L. Zhu
et al., 2020), impact to human comfort and health (Schwarz et al.,
2011), and a degradation of ecosystem function (Keeratikasikorn and
Bonafoni, 2018). All of these also play a significant role in the rise in
the rate of COVID-19 cases (H. Li et al., 2020;Mukherjee and Debnath,
2020) and leads to heat-related deaths (Cui and De Foy, 2012;Lowe,
2016). UHI is measured based on air temperature, while satellite-
derived Land Surface Temperature (LST) data is used to estimate the
surface UHI (SUHI). Moreover, the calculation of SUHI Intensity
(SUHII) requires observations from paired LST locations in both urban
and rural/nonurban areas (Cuietal.,2019). LST is described as the sur-
face temperature of the Earth's skin, playing an important role in the in-
terchange of heat and energy amongland surfaces and the atmosphere
to assess changes in the environment (Moradi et al., 2018).
Distinct mitigation initiatives such as social distancing, cluster and
whole lockdowns, comprehensive travel bans, mass quarantines, etc.
have been introduced globally to mitigate the COVID-19 pandemic
risk. Such risk mitigation initiatives have had a significant effect on
socio-political ties and economic development at local and global levels
(Ranjan et al., 2020a). Nevertheless, due to the reduction of
anthropogenic-based pollutants, such precautionary strategies to pre-
vent COVID-19 transmission have significantly enhanced air quality.
The most critical challenge in the 21st century is the degradation of air
quality worldwide due to the different sorts of anthropogenic interven-
tions (Mehdipour and Memarianfard, 2017;Motesaddi et al., 2017). At
such a moment, lockdown episodes enforced for the COVID-19 pan-
demic mitigation resulted in anti-environmental activities to cease as a
byproduct. As a result, the level of air quality in the various continents
of the Earth significantly improved since the COVID-19 pandemic
began. Tobías et al. (2020) recorded a 45%, 51%, 31%, and 19% decline
in the level of PM
10
,NO
2
,SO
2
, and CO, respectively, over Barcelona in
Spain within the lockdown span of a month. Isaifan (2020) recorded a
substantialdecreaseinNO
2
and carbon emissions(30% and 25%, respec-
tively) inChina, which correlated to industrial lockdowns. Karuppasamy
et al. (2020) documented a 55% contraction in NO
2
in India during the
lockdown. A recent analysis in the Middle East has also shown a de-
crease in the number of air pollutants in Morocco of 75%, 49%, and 96%
for PM
10
,SO
2
, and NO
2
, respectively (Otmani et al., 2020). Moreover,
in Iran, Nemati et al. (2020) recorded noticeable reductions in air pollu-
tion during the pandemic. Likewise, during the COVID-19 pandemic,
many other recent studies show a significant decline in the level of air
pollutants such as NO
2
,SO
2
,PM
10
,PM
2.5
, CO, etc. globally (Archer
et al., 2020;Collivignarelli et al., 2020;Dantas et al., 2020;Islam et al.,
2020;Kaplan and Avdan, 2020;Kerimray et al., 2020;Nakada and
Urban, 2020;Ranjan et al., 2020a;Wang and Su, 2020). However,
while studies generally showed a reduction in the primary pollutants,
some studies showed no reduction in air pollution. For example, Q. Li
et al. (2020),L. Li et al. (2020) and H. Li et al. (2020) investigated the
air quality changes during the COVID-19 lockdown over 41 cities in
the Yangtze River Delta Region. The results showed a decrease in some
of the main pollutants, but still reported high PM
2.5
and increasing
trend during the lockdown by over 20%. These contradictory results em-
phasize the need for continued detailed investigations on the topic.
The aforementioned studies concentrate primarily on evaluating the
level of air pollutants during the pandemic scenario of COVID-19. Al-
though a considerable correlation between air pollutants and LST was
identified in some research (Alseroury, 2015;Feizizadeh and Blaschke,
2013;Hashim and Sultan, 2010;Kahya et al., 2016;Weng and Yang,
2006), the SUHII variability during the pandemic scenario has not yet
been investigated. Like other nations, the UAE forced the shutdown of in-
dustries, public transit, airlines, vehicles, and other anthropogenic opera-
tions in all Emirates from the 1st April to the end of June 2020 to mitigate
COVID-19 spread. Additionally, the UAE imposed a curfew daily between
8pmand6amforresidents(https://www.wam.ae). A full or partial lock-
down is hypothesized to lead to improved air quality and reduce the
SUHII since the lockdown is associated with many anthropogenic activi-
ties that directly impact the level of emissions. To our knowledge, no
studies have examined the impacts of COVID-19 lockdowns on air qual-
ity and SUHII throughout the UAE. This current study was undertaken to
investigate the potential impacts of COVID-19 lockdown operations on
NO
2
, Aerosol Optical Depth (AOD), and SUHII in the Northern Emirates
of the UAE (NEUAE). This study also aims to validate satellite data over
A.S. Alqasemi, M.E. Hereher, G. Kaplan et al. Science of the Total Environment 767 (2021) 144330
2
the study area using ground station records. Concentrating on the
NEUAE, this work directly addresses the impact of lockdowns on air qual-
ity and SUHII, and is a resource for the science community and environ-
mental protection policymakers, as well as resource in the development
of an action plan to improve air quality and SUHII.
2. Data and methods
2.1. Study area
The United Arab Emirates (UAE) consists of seven federation
Emirates (States): Abu Dhabi(ADH), and six northern Emirates, includ-
ing Ras Al Khaymah (RAK), Dubai (DUB), Umm Al Qaywayn (UMQ),
Sharjah(SHJ),Fujayrah(FUJ),andAjman(AJM)(Fig. 1a). The Northern
Emirates of the UAE (NEUAE) were chosen as a study area located ap-
proximately between latitudes 26° 365 to 26° 335 N and longitudes
54° 530 to 56° 240 E. Topography of the study area is mostly flat, with
a mountain chain reaching an altitude of ~1800 m in the northeastern
and eastern parts (Fig. 1b). The NEUAE is typically an arid region with
a humid environment, located inthe Arabian Peninsula's Eastern corner.
It bordersthe ArabianGulf to the north and theGulf of Oman tothe east
(Fig. 1). The annual average air temperature is about 28 °C. It is much
hotter (up to 45 °C) in the summer season (June to August) and colder
(down to 10 °C) in the winter season (December to February) (https://
www.ncm.ae/en/climate-reports-yearly.html?id=26). In the hot
months, dust storms generally occur (Barbulescu and Nazzal, 2020).
Over 80% of the yearly rainfall occurs in the winter season (FAO, 2008).
Around 71% of the UAE population is based in the NEUAE. Around
50% of the population resides in DUB, followed by 31% in SHJ, and only
1% resides in UMQ, as per the Federal Competitiveness and Statistics
Authority (https://fcsa.gov.ae/ar-ae/Pages/home.aspx). Besides that, as
documented by the United Nations (2019), more than 86.5% of the
NEUAE people live in urban areas situated on the coastline (Fig. 1c). Fur-
thermore, over the past two centuries, fast and pervasive economic and
political change has caused increased population explosion, accelerated
urbanization, higher energy consumption, and increased vehicular and
industrial emissions (Alawadi et al., 2018). This all adds to a net increase
in all anthropogenic activities.
2.2. Data
Between March to June 2019 and 2020, two different air pollutants
NO
2
and AOD, and night LST data were collected for the NEUAE. The
average monthly NO
2
and AOD data were acquired from Google Earth En-
gine (GEE). NO
2
is measured by the Sentinel-5p TROPOMI (Tropospheric
Monitoring Instrument) mission of Copernicus ESA. While AOD and night
LST were obtained by MODIS MAIAC (MCD19A2) and MODIS Aqua
(MYD11A2), respectively. Due to the finite temporal scope of Sentinel-
5p data, it is worth noting that the baseline was 2019. A summary of
the datasets used in this study is shown in Table 1.
2.2.1. TROPOMI/Sentinel-5p data (NO
2
)
TROPOMI was launched on 13th October 2017 as a passive
hyperspectral nadir-viewing imager aboard the Sentinel-5 Precursor
satellite, which is also recognized as Sentinel-5P (Veefkind et al.,
2012). Sentinel-5P is a near-polar orbiting sun-synchronous satellite
positioned at an altitude of 817 km in an ascending node with an equa-
tor crossing time at 13:30 (local time) offering daily worldwide cover-
age. Furthermore, since July 2018, TROPOMI delivered calibrated data
from its nadir-viewing spectrometer that measures reflected sunlight
in the ultraviolet, visible, near-infrared, and shortwave infrared with
seven bands, where the fourth band spectral range is 405–500 nm,
which could be used for NO
2
monitoring (Venter et al., 2020). Recent
works have shown that measurements of TROPOMI are quite well
Fig. 1. Location map of the study area: (a) study area and major factories, (b) elevations and spatial distribution of meteorological and air quality stations, and (c) urban areas, and reference.
A.S. Alqasemi, M.E. Hereher, G. Kaplan et al. Science of the Total Environment 767 (2021) 144330
3
associated with actual ground measures of NO
2
(Griffin et al., 2019;
Lorente et al., 2019). TROPOMI products included in this research are
L3 offline version products. Band four's spectral and spatial resolutions
are 0.55 nm and 5.5 × 3.5 km, respectively, and the signal to noise
ratio is also massively enhanced (Cheng et al., 2019).
2.2.2. MODIS data (AOD and LST)
In a near-polar solar-synchronous circular orbit, the Moderate
Resolution Imaging Spectroradiometer (MODIS) was launched onboard
NASA's Aqua and Terra satellites. Local crossing times are approximately
01:30 and 13:30 for the Aqua satellite and 10:30 and 22:30 for the
Terra satellite. MODIS has a 2330 km (cross-track) swath and provides
near-global coverage daily. MODIS is an imaging radiometer with 36
wavebands, covering the wavelength spectrum from the visible to the
thermal infrared. AOD data was possessed from the cloud-masked
MCD19A2-v6 product, which is a MODIS Terra and MODIS Aqua com-
bined AOD retrieved with the Multi-Angle Implementation Atmospheric
Correction (MAIAC) algorithm (Lyapustin et al., 2018). This dataset has
previously been used effectively to map ground-level PM
2.5
concentra-
tions (Wei et al., 2019). In this analysis, AOD at 550 nm (i.e., the green
band) was utilized because of its superior accuracy as shown by previous
studies (Lyapustin and Wang, 2018). Concerning night LST data, for 2019
and 2020, MODIS Aqua's MYD11A2-v6 performance entails an 8-day
composite of LST data at night from 1 March to 30 June 2019 and 2020.
In the long time series, the 8-day LST composite products will undervalue
the quantity of gaps created by clouds or other unwanted circumstances,
that are beneficial for SUHII studies to conduct a spatial comparison (Hu
and Brunsell, 2013;Schwarz et al., 2011). Modern Time LST products
(v6) have resolved past version accuracy issues. Additionally, measure er-
rors and validation assessments in bare land and arid regions currently
suggest their use in these areas (Lu et al., 2018). For the present work,
MAIAC AOD and night LST data on a 1 km nadir resolution were retrieved
from the NASA Land Processes Distributed Active Archive Center
(Table 1).
2.2.3. SRTM data (DEM)
The SRTM DEM data was downloaded from the USGS Earth Explorer
(Table 1) to pick a suitable reference location for SUHII calculation. The
area's topography is predominantly flat but increases slowly from the
northwest to southeast and east, reaching approximately 1830 m
(Fig. 1b). The Digital Elevation Model (DEM) 30 m resolution was
used for this study. The DEM model was obtained from the Shuttle
Radar Topography Mission (SRTM).
Table 1
Summary of the datasets used in this study.
Data source Parameter Spatial resolution Temporal resolution Data access link
Sentinel5p TROPOMI NO
2
3.5 × 5 km Daily
MODIS MAIAC AOD 1 × 1 km Daily https://lpdaac.usgs.gov/
MODIS/ AQUA Night LST 1 × 1 km 8-day https://lpdaac.usgs.gov/
SRTM DEM 30 × 30 m –https://earthexplorer.usgs.gov/
NCM NO
2
&PM
2.5
–Monthly –
DM Ta
min
–Monthly –
Fig. 2. Workflow flowchart adopted in the present study.
A.S. Alqasemi, M.E. Hereher, G. Kaplan et al. Science of the Total Environment 767 (2021) 144330
4
2.2.4. Meteorological data
The air quality and meteorological records utilized in our study were
collected from the Municipality of Dubai (DM) and the National Center
of Meteorology (NCM) for validation.From the aforementioned sources,
three parameters were obtained: NO
2
,PM
2.5
, and minimum air temper-
ature (Ta
min
) for each of the years 2019 and 2020, from March to June.
Ta
min
was chosen for the highest degree of matching with MODIS
night LST overpass time, which is closer to Ta
min
(Alqasemi et al.,
2020). Likewise, PM
2.5
was selected as AOD, which is considered a
proxy for PM
2.5
(Fan et al., 2020;Venter et al., 2020).
2.3. Methods
2.3.1. Data pre-processing
Usually, the products MCD19A2 and MYD11A2 were available in the
sinusoidal grid projection that was re-projected to the WGS 1984 geo-
graphic coordinate system. In addition, a mosaic of two tiles was created
with MYD11A2 data. The scale factor was multiplied by both MCD19A2
and MYD11A2. Then, we subtracted 273.15 to produce a night LST in
Celsius, from the reported Kelvin. In the process of producing a seamless
dataset, every unrealistic LST data, values above 100 °C and/or below
−50 °C, or any value outside the acceptable range was labeled as no
data and ignored. Only reliable pixels and high-quality data were used
as per the layer of Quality Assessment (QA). Missing values were
amended using the mean value of each series. Afterward, night LST data
was aggregated to a monthly period by averaging the 8-day composite
data to obtain monthly average night LST. As for TROPOMI-Sentinel-5P,
NO
2
was resampled to 1 × 1 km to ensure conformity with a spatial res-
olution of MODIS data (i.e., AOD and night LST). Finally, the boundary
polygon that defined the study area was used to clip all datasets.
As a preventive strategy for COVID-19, the three months of April,
May, and June (AMJ) were averaged as one period applied for all
datasets throughout the NEUAE. The March month was collected and
processed for pre-lockdown in 2020 for NO
2
, AOD, and night LST. Simi-
larly, forthe identical timespans as the lockdown and pre-lockdown pe-
riods, the mean of datasets was obtained for 2019 for comparative
analysis, as well as utilized for evaluatingthe correlation with measured
data from ground stations.
2.3.2. SUHII calculation
The night SUHII is a distinctive feature of arid regions, as cited by
previous researchers (Alahmad et al., 2020;Clinton and Gong, 2013;
Lazzarini et al., 2013). Additionally, the lowest surface temperature is
reported at midnight, making Aqua night data an optimal choice near
the SUHII maxima. SUHII is typically measured from the LST contrasts
between urban and surroundingareas (will be cited here as reference).
Consequently, choosing a reliable reference is important. In different
tests, however, the approaches for determining the reference differed
extensively in various studies. The reference should not be influenced
by urban, high altitude, vegetation, or water (Hu et al., 2019). Therefore,
Fig. 3. Average concentrations of (a) NO
2
, (b) AOD, and (c) SUHII in the NEUAE before and during the COVID-19 lockdown, compared to the same 2019 time period.
A.S. Alqasemi, M.E. Hereher, G. Kaplan et al. Science of the Total Environment 767 (2021) 144330
5
in this study, the reference was defined as bare land using chronological
images from Google Earth Pro (GEP) with elevation lower than 50 m as
determined by the 30 m DEM dataset, to prevent the cooling effect on
the SUHII quantification. Finally, by subtracting the average night LST
of the reference from the night LST of all pixels, the SUHII was calculated
for every pixel utilizing the following equation (Eq. (1)):
SUHII ¼LSTPX−LSTRF ð1Þ
where LST
PX
is the night LST of all pixels, and LST
RF
is the mean night LST
of the reference.
2.3.3. Change rate (concentration)
It is critical to understand the change in the NO
2
, AOD, and SUHII
concentrations throughout the lockdown period. After retrieving the
data on pollutants and SUHII, the Spatiotemporal pattern of average
levels of NO
2
, AOD, and SUHII was categorized into four groups;
(i) pre-lockdown (March 2020); (ii) during the lockdown period
(AMJ, 2020); (iii) the same Pre-lockdown dates (March 2019); (iv)
the same 2019 lockdown dates. Furthermore, the change rates were cal-
culated utilizing Eq. (2) to reflect the percentage of change in the study
area's NO
2
, AOD, and SUHII levels during the lockdown period related to
the past year (i.e., 2019) for the same period, whichwere also compared
to pre-lockdown in March 2020. The change rate of variation between
March and AMJ throughout 2019 and 2020 was also examined.
Change rate %ðÞ¼ X2020−X2j019ðÞ=X2019ðÞ⁎100 ð2Þ
where, Xis NO
2
, AOD, or SUHII for the selected periods in 2019 and 2020.
Also, the correlation was analyzed by calculating the regression coeffi-
cients (R
2
) among stations and satellite data based on the availability of
the metrological and air quality station data. The flowchart for the meth-
odological workflow is presented concisely in Fig. 2.Thesequenceofdata
collection, pre-processing, clipping, resampling, data extractions, and
SUHII measurement, change rate, and validation are described in Fig. 2.
For the processing, analysis, and exhibition, the ESRI ArcGIS™version
10.4 software framework and Microsoft Office Excel were used.
3. Results and discussion
3.1. Nitrogen dioxide (NO
2
)
A dramatic drop in NO
2
concentration was recorded during the lock-
down period (i.e., AMJ, 2020) in the NEUAE due to the COVID-19. Fig. 4
Fig. 4. Spatiotemporal distribution of average NO
2
over the NEUAE: (a) March 2019, (b) average AMJ 2019, (c) pre-Lockdown (March 2020), (d) during Lockdown (AMJ 2020),
(e) percentage of changebetween March 2019 and 2020, and (f) percentage of change between average AMJ 2019 and 2020.
A.S. Alqasemi, M.E. Hereher, G. Kaplan et al. Science of the Total Environment 767 (2021) 144330
6
depicts the substantial spatiotemporal changes in NO
2
levels through-
out lockdown and pre-lockdown in the NEUAE. The analysis showed
that the six Emirates experienced a decline in NO
2
levels because of re-
stricted transportation and factories' closure. This result is compatible
with other earlier studies undertaken in various regions around the
globe (Baldasano, 2020;Dantas et al., 2020;Islam et al., 2020;Kaplan
and Avdan, 2020;Sharma et al., 2020). Fig. 3a indicates that RAK expe-
rienced the highest decline in NO
2
levels among the six Emirates
(18.6%), followed by UMQ (~18%), AJM (13%), and FUJ (11.6%) com-
pared with the average NO
2
levels throughout lockdown with the aver-
age levels immediately pre-lockdown, for other Emirates relative to
DUB (7.6%) and SHJ (5.4%). In addition, our study indicates that, as
shown in Fig. 3a, the average decline in NO
2
concentration throughout
NEUAE was substantial 12.2%. This decline should be noted that NO
2
emission is closely connected to fuel combusting within the factories
area (Figs. 1a&4). Therefore, the limitationson these areas' operations
are hypothesized to correlate to the substantial drop in NO
2
level
throughout the lockdown period. Additionally, within March 2019, the
NO
2
level was lower than March 2020, as shown in Fig. 3aandc,
which may be due to higher precipitation during March 2019.
A substantial decrease in NO
2
is visible in the study area by comparing
the NO
2
values during the 2020 lockdown period with the identical 2019
time period. The maximum calculated decline was for UMQ (27.8%)
followed by RAK (26.8%), SHJ (~26%), FUJ (24.3%), AJM (19.8%) and
DUB (18.7%). The average reduction was 23.7% with the whole study
area (Fig. 3a). These results are in accordance with the results of previ-
ously published research. Islam et al. (2020) recorded identical outcomes
for Bangladesh, Agarwal et al. (2020) recorded an average reduction in
NO
2
during the lockdown in China by 49% and in Mumbai (India) by
more than 76%. In the Middle East (Morocco), NO
2
levels were decreased
during the lockdown phase as well (Sekmoudi et al., 2020). This decline
in NO
2
concentrations shows that lockdown initiatives related to the
COVID-19 pandemic significantly impacted NO
2
concentrations in the
NEUAE as well as elsewhere.
3.2. Aerosol optical depth (AOD)
The average AOD values during the lockdown period were lower
than the mean AOD values in 2019 for the equivalent time frame, as
shown in Fig. 5. Theanalysis indicates that all six Emirates encountered
Fig. 5. Spatiotemporal distribution of average AOD over the NEUAE: (a) March 2019, (b) average AMJ 2019, (c) pre-lockdown (March 2020), (d) during lockdown (AMJ 2020),
(e) percentage of changebetween March 2019 and 2020, and (f) percentage of change between average AMJ 2019 and 2020.
A.S. Alqasemi, M.E. Hereher, G. Kaplan et al. Science of the Total Environment 767 (2021) 144330
7
a decline in AOD concentration because of restricted aerosol sources,
specifically, burning biomass, emissions from factories, vehicles, heavy
transport, machinery (Ranjan et al., 2020b), and dust (Khuzestani
et al., 2017). Therefore, a reduction in AOD due to the restrictions of in-
dustrial and automobile movement is reasonable. The spatiotemporal
variations in AOD concentrations in the NEUAE (Fig. 5)showthatAJM
with 5.7% and UMQ with 5.3% were the largest declines, followed by
SHJ with 3.4%, FUJ and RAK, both with 3.1%, and, in Dubai, the lowest
drop, 1.7%. The rate of change was 3.7% throughout the overall study
area (NEUAE), as shown in Fig. 3b. Similarly, a common declining
trend of AOD has been recorded in China (Fan et al., 2020;Filonchyk
et al., 2020), India (Gautam, 2020;Pathakoti et al., 2020;Ranjan et al.,
2020b), and South Asia (Zhang et al., 2020).
During the average AMJ, the level of AOD increased significantly com-
pared to March (Fig. 5). This may be due to the fact that rainfall through-
out these months (AMJ) is scarce, and the gusty winds ensure that dust
storms become frequent (Barbulescu and Nazzal, 2020;Karagulian
et al., 2019). Furthermore, according to Al Otaibi et al. (2019),AODtypi-
cally rises over the gulf nations in hot summer months. Consequently,
there is generally a rise in the AOD level in AMJ when compared to
March. However, the change rate between March and the average AMJ
in 2020 decreased relative to the past year for all six Emirates, as shown
in Fig. 3b. In 2019, the change rate over the NEUAE was 26.3%, whereas
it dropped in 2020 to 3.6%. Notably, AOD emission is linked with the
major industries, particularly in Dubai (Figs. 1a, 5). Furthermore, Dubai re-
corded the smallest reduction in AOD. The most populous and established
region of the UAE is the Emirate of Dubai, which includes the most indus-
trialized regions. Therefore, certain additional considerations may explain
the relatively small reduction in Dubai.
3.3. Surface Urban Heat Island Intensity (SUHII)
SUHII specifically portrays urbanized areasand mountains, as shown
in Fig. 3(a–d). A drop in SUHII levels was also recorded, like NO
2
and
AOD (Fig. 3f), also attributed to the partialor full nationwide lockdown.
The result reveals that during the specific time frame, nighttime SUHII
levels over this duration were comparatively less than the 2019 levels
(Figs. 3c&6). The decline was generally identified in all Emirates, vary-
ing from 12.3% to 28.6%, in which the average drop is 19.2% across the
whole study area.
The maximum SUHII concentrations are found in FUJ (28.6%) and
RAK (23%) as displayed in Fig. 3c, which may be due to the elevations
Fig. 6. Spatiotemporaldistribution of SUHII over the NEUAE: (a) March2019, (b) average AMJ2019, (c) pre-Lockdown (March2020), (d) during Lockdown (AMJ2020), (e) percentage of
change between March 2019 and 2020, and (f) percentage of change between average AMJ 2019 and 2020.
A.S. Alqasemi, M.E. Hereher, G. Kaplan et al. Science of the Total Environment 767 (2021) 144330
8
and type of rocks. Fig. 6(a–d) also reveals that SUHII increases during
AMJ relative to March, related to heat emission in hot months (e.g., air
conditioning system). Nevertheless, the change rate in 2020 between
March and AMJ is less than in 2019. SUHII change rate dropped from
18.3% in 2019 to 6.7% in 2020 over the NEUAE, as exhibited in Fig. 3c.
Overall, the results indicate a reduction in SUHII values as a result of
the shutdown of anthropogenic activities and sources of heat emissions
such as industrial processes, power plants, flight, and transport.
3.4. Validation
In this section, the validations of derived data from satellites with the
measured data from actual ground stations are investigated. The com-
parisons between derived NO
2
, AOD, and night LST plotted against the
measured data NO
2
,PM
2.5,
and minimum air temperature (Ta
min
), re-
spectively, are presented in Fig. 7. Based on the availability of ground
station data, the validation of the NO
2
and PM
2.5
data were made
using thirteen and five air quality monitoring stations measurements,
respectively, from March to June 2019. Ta
min
data covers the whole pe-
riod (March–June) in 2019 and 2020 at four metrological stations
(Fig. 1b). Night Aqua LST data is validated with Ta
min
because the
Aqua satellite crosses the equator at night, close to the Ta
min
; therefore,
this is the most appropriate temperature measurement for validation
purposes. In addition, as mentioned before, the AOD is considered a
proxy for (PM
2.5
); thus, AOD is plotted versus PM
2.5
.
Our comparisons show that the TROPOMI Sentinel-5P NO
2
is highly
correlated with the air quality monitoring stations data with R
2
=0.70,
as shown in Fig. 7a. Likewise, Fig. 7(b) shows the scatter plot of mea-
sured PM
2.5
versus MODIS MAIAC AOD. The statistical analyses showed
a high coefficient of determination (R
2
= 0.60). Similarly, a high
correlation was found between the MODIS Aqua night LST and the
Ta
min
from station measurements (R
2
= 0.68), as displayed in Fig. 7c.
Overall, a good agreement is found between satellite data and actual
measured data; hence, the satellite observations are a valuable resource
for studying air pollution and SUHII over large geographic regions.
4. Conclusions
The impact of anthropogenic activities lockdown due to the COVID-
19 pandemic on air quality and SUHII in the NEUAE was studied by ex-
amining NO
2
, AOD, and SUHII levels and evaluating variations in spatial
distribution. To demonstrate how restrictive anthropogenic activities
throughout the COVID-19 lockdown minimized the air pollutants and
SUHII in NEUAE, satellite data of different parameters were used and
compared between 2019 and 2020. As predicted, the current investiga-
tion discovered that NO
2
, AOD, and SUHII concentrations across the
NEUAE decreased during the pandemic lockdown. The largest average
drop was in NO
2
(23.7%) followed by SUHII (19.2%) and AOD (3.7%)
throughout the lockdown period compared with the same period
in 2019.
This study showed that the satellite derived measurements of the se-
lected airpollutants and SUHII data are highly correlated with the actual
measured data. Therefore, satellite data is a significant and reliable re-
source for researchingair quality and SUHII because of the spatial cover-
age and cost-effectiveness of the data, especially for developing
countries like the UAE. We conclude that our study has established a
benchmark paradigm that will potentially assist the authorities con-
cerned with the potential management of air quality and SUHII in the
UAE by decision-makers, particularly on industrial and vehicle pollution
restrictions. The drawbacks of this work are that some datasets from
Fig. 7. The scatter plots forvalidation of the derived data fromsatellites and measured data from ground-measuring stations; (a)measured NO
2
and derived NO
2
, (b) measured PM
2.5
and
AOD, and (c) minimum air temperature (Ta
min
) and night LST.
A.S. Alqasemi, M.E. Hereher, G. Kaplan et al. Science of the Total Environment 767 (2021) 144330
9
ground stations were incomplete. Additionally, the baseline was only a
single year (i.e., 2019) due to the limited temporal scale of TROPOMI/
Sentinel-5P data, and therefore the outcomes may change slightly
when providing extra datasets. Further study is recommended to exam-
ine the correlation of air pollutants and SUHII with COVID-19 cases in
the UAE.
CRediT authorship contribution statement
Abduldaem S. Alqasemi: Conceptualization, Methodology,
Software, Validation, Formal analysis, Data curation, Writing- Original
draft preparation and editing, Visualization, Investigation. Mohamed
E. Hereherb: Conceptualization, Supervision, Writing- Reviewing and
Editing. Gordana Kaplan: Conceptualization, Formal analysis, Software,
Writing- Reviewing and Editing. Ayad M. Fadhil Al-Quraishi:Supervi-
sion, Writing- Reviewing and Editing. Hakim Saibi: Supervision,
Writing- Reviewing and Editing. All authors have read and approved
the final version of the manuscript for publication.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influ-
ence the work reported in this paper.
Acknowledgements
The authors would like to thank the anonymous reviewers for their
constructive comments on an earlier version of this paper. The authors
are also grateful to theUnited Arab Emirates University for fundingthis
work. We Thank the workers on the frontline against coronavirus.
References
Agarwal, A., Kaushik, A., Kumar, S., Mishra, R.K., 2020. Comparative study on air quality
status in Indian and Chinese cities before and during the COVID-19 lockdown period.
Air Qual. Atmos. Health 13, 1167–1178. https://doi.org/10.1007/s11869-020-00881-
z.
Al Otaibi,M., Farahat, A., Tawabini, B., Omar, M.H., Ramadan, E.,Abuelgasim, A., Singh,R.P.,
2019. Long-term aerosoltrends and variabilityover Central Saudi Arabiausing optical
characteristics from Solar Village AERONET measurements. Atmosphere 10, 752.
https://doi.org/10.3390/atmos10120752.
Alahmad, B., Tomasso, L.P., Al-Hemoud, A., James, P., Koutrakis, P., 2020. Spatial distribu-
tion of land surface temperatures in Kuwait: Urban Heat and Cool Islands. Int.
J. Environ. Res. Public Health 17, 1–11. https://doi.org/10.3390/ijerph17092993.
Alawadi, K., Khanal, A., Almulla, A., 2018. Land, urban form, and politics: a study on
Dubai’s housing landscape and rental affordability. Cities 81, 115–130. https://doi.
org/10.1016/j.cities.2018.04.001.
Alghamdi, A.S., Moore,T.W., 2015. Detecting temporalchanges in Riyadh’s Urban Heat Is-
land. Pap. Appl. Geogr. 1, 312–325. https://doi.org/10.1080/23754931.2015.1084525.
Alqasemi,A., Hereher, M., Al-Quraishi, A., Saibi, H., Aldahan, A., Abuelgasim, A., 2020. Re-
trieval of monthly maximum and minimum air temperature using MODIS Aqua land
surface temperature data over theUnited Arab Emirates (UAE). Geocarto Int. https://
doi.org/10.1080/10106049.2020.1837261.
Alseroury, F.A., 2015. TheEffect of Pollutantson Land Surface Temperature Around Power
Plant. pp. 17–21.
Archer, C.L., Cervone,G., Golbazi, M., Fahel, N. Al, Hultquist, C., 2020. Changes in Air Qual-
ity and Human Mobility in the U.S. During the COVID-19 Pandemic.
Baldasano, J.M., 2020. COVID-19 lockdown effects on air quality by NO2 in the cities of
Barcelona and Madrid (Spain). Sci. Total Environ. 741, 140353. https://doi.org/
10.1016/j.scitotenv.2020.140353.
Barbulescu, A., Nazzal, Y., 2020. Statistical analysis of dust storms in the United Arab
Emirates. Atmos. Res. 231, 104669. a.alqasemi@uaeu.ac.ae https://doi.org/10.1016/j.
atmosres.2019.104669.
Briz-Redón, Á., Serrano-Aroca, Á., 2020. The effect of climate on the spread of the COVID-19
pandemic: a review of findings, and statistical and modelling techniques. Prog. Phys.
Geogr. Earth Environ. 44, 591–604. https://doi.org/10.1177/0309133320946302.
Bukhari,Q., Jameel, Y., 2020.Will coronavirus pandemic diminish by summer? SSRN Elec-
tron. J. https://doi.org/10.2139/ssrn.3556998.
Cheng, L.,Tao, J., Valks, P., Yu, C.,Liu, S., Wang, Y., Xiong, X., Wang, Z.,Chen, L., 2019. NO2
retrieval from the environmental trace gasesmonitoring instrument (EMI): prelimi-
nary results and intercomparison with OMI and TROPOMI. Remote Sens. 11. https://
doi.org/10.3390/rs11243017.
Clinton, N., Gong, P., 2013. MODIS detected surface urban heat islands and sinks: global
locations and controls. Remote Sens. Environ. 134, 294–304. https://doi.org/
10.1016/j.rse.2013.03.008.
Collivignarelli, M.C., Abbà, A., Bertanza, G., Pedrazzani, R., Ricciardi, P., Carnevale Miino,
M., 2020. Lockdown for CoViD-2019 in Milan: what are the effects on air quality?
Sci. Total Environ. 732, 139280. https://doi.org/10.1016/j.scitotenv.2020.139280.
Conticini, E., Frediani, B., Caro, D., 2020. Can atmospheric pollution be considered a co-
factor in extremely high level of SARS-CoV-2 lethality in northern Italy? Environ.
Pollut. 261, 114465. https://doi.org/10.1016/j.envpol.2020.114465.
Cui, Y.Y., De Foy, B., 2012. Seasonal variations of the urban heat island at the surface and
the near-surface and reductions due to urban vegetation in Mexico City. J. Appl.
Meteorol. Climatol. 51, 855–868. https://doi.org/10.1175/JAMC-D-11-0104.1.
Cui, Y., Xiao, X., Doughty, R.B., Qin, Y., Liu, S., Li, N., Zhao, G., Dong, J., 2019. The relation-
ships between urban-rural temperature difference and vegetation in eight cities of
the Great Plains. Front. Earth Sci. 13, 290–302. https://doi.org/10.1007/s11707-018-
0729-5.
Dantas, G., Siciliano, B., França, B.B., da Silva,C.M., Arbilla, G., 2020. The impact of COVID-
19 partial lockdown on the air quality of the city of Riode Janeiro, Brazil. Sci.Total En-
viron. 729, 139085. https://doi.org/10.1016/j.scitotenv.2020.139085.
Fan, C., Li, Y., Guang, J., Li, Z., Elnashar, A., Allam, M., de Leeuw, G., 2020. The impact of the
control measures during the COVID-19 outbreak on air pollution in China. Remote
Sens. 12. https://doi.org/10.3390/rs12101613.
FAO, 2008. AQUASTAT Country Profile –United Arab Emirates. Food and Agriculture Or-
ganization of the United Nations (FAO), Rome.
Feizizadeh, B., Blaschke, T., 2013. Examining Urban Heat Island relations to land use and
air pollution: multiple endmember spectral mixture analysis for thermal remote
sensing. IEEE J. Selected Top. Appl. Earth Obs. Remote Sens. 6, 1749–1756. https://
doi.org/10.1109/JSTARS.2013.2263425.
Filonchyk, M., Hurynovich, V., Yan, H., Gusev, A., Shpilevskaya, N., 2020. Impact assess-
ment of COVID-19 on variations of SO2, NO2, CO and AOD over East China. Aerosol
Air Qual. Res. 20, 1530–1540. https://doi.org/10.4209/aaqr.2020.05.0226.
Gautam, S.,2020. The influence of COVID-19 on air quality in India: a boonor inutile. Bull.
Environ. Contam. Toxicol. 1.
Griffin, D., Zhao, X., McLinden, C.A., Boersma, F., Bourassa, A., Dammers, E., Degenstein, D.,
Eskes, H., Fehr, L., Fioletov, V., Hayden, K., Kharol, S.K., Li, S., Makar, P., Martin, R.V.,
Mihele, C., Mittermeier, R.L., Krotkov, N., Sneep, M., Lamsal, L.N., ter Linden, M., van
Geffen, J., Veefkind, P., Wolde, M., 2019. High-resolution mapping of nitrogen dioxide
with TROPOMI: first results and validation overthe Canadian Oil Sands. Geophys. Res.
Lett. 46, 1049–1060. https://doi.org/10.1029/2018GL081095.
Hashim, B.M., Sultan, M.A., 2010. Using remote sensing dataand GIS to evaluate air pollu-
tion and theirrelationship with landcover and land use in Baghdad City. Iran. J. Earth
Sci. 2, 20–24.
Hu, L., Brunsell, N.A., 2013. The impact of temporal aggregation of land surface tempera-
ture data forsurface urban heatisland (SUHI) monitoring. Remote Sens. Environ. 134,
162–174. https://doi.org/10.1016/j.rse.2013.02.022.
Hu, X., Zhao, Z., Zhang, L., Liu, Z., Li, S., Zhang, X., 2019. A high-temperature risk assess-
ment model for maize based on MODIS LST. Sustainability (Switzerland) 11.
https://doi.org/10.3390/su11236601.
Isaifan,R.J., 2020. The dramatic impact ofCoronavirus outbreak on air quality: has it saved
as much as it has killed so far? Glob. J. Environ. Sci. Manag. 6, 275–288. https://doi.
org/10.22034/gjesm.2020.03.01.
Islam, M.S., Tusher, T.R., Roy, S., Rahman, M., 2020. Impacts of nationwide lockdown due
to COVID-19 outbreak on air quality in Bangladesh: a spatiotemporal analysis. Air
Qual. Atmos. Health https://doi.org/10.1007/s11869-020-00940-5.
Kahya, C., Bektas Balcik, F., Burak Oztaner, Y., Guney, B., 2016. Determining land surface
temperature relations with land use-land cover and air pollution. EGU General As-
sembly Conference Abstracts, EGU General Assembly Conference Abstracts pp.
EPSC2016-16489.
Kaplan, G., Avdan, Z., 2020. COVID-19: Spaceborne Nitrogen Dioxide Over Turkey. Eskişe-
hir Techn. Univ. J. Sci. Technol. A Appl. Sci. Eng. https://doi.org/10.18038/
estubtda.724450.
Karagulian, F., Temimi, M., Ghebreyesus, D., Weston, M., Kondapalli, N.K., Valappil, V.K.,
Aldababesh, A., Lyapustin, A., Chaouch, N., Al Hammadi, F., Al Abdooli, A., 2019. Anal-
ysis of a severedust storm and its impact on air quality conditions using WRF-Chem
modeling, satellite imagery, and ground observations. Air Qual. Atmos. Health 12,
453–470. https://doi.org/10.1007/s11869-019-00674-z.
Karuppasamy, M.B., Seshachalam, S., Natesan, U., Ayyamperumal, R., Karuppannan, S.,
Gopalakrishnan, G., Nazir, N., 2020. Air pollution improvement and mortality rate
during COVID-19 pandemic in India: global intersectional study. Air Qual. Atmos.
Health https://doi.org/10.1007/s11869-020-00892-w.
Keeratikasikorn, C., Bonafoni, S., 2018. Satellite imagesand Gaussian parameterization for
an extensive analysis of urban heat islands in Thailand. Remote Sens. 10. https://doi.
org/10.3390/rs10050665.
Kerimray, A., Baimatova, N., Ibragimova, O.P., Bukenov, B., Kenessov, B., Plotitsyn, P.,
Karaca, F., 2020. Assessing air quality changes in large cities during COVID-19 lock-
downs: The impacts of traffic-free urban conditions in Almaty, Kazakhstan. Sci.
Total Environ. 730, 139179. https://doi.org/10.1016/j.scitotenv.2020.139179.
Khuzestani, R.B., Schauer, J.J., Wei, Y., Zhang, L., Cai, T., Zhang, Yang, Zhang, Yuanxun,
2017. Quantification of the sources of long-range transport of PM2.5 pollution in
the Ordos region, Inner Mongolia, China. Environ. Pollut. 229, 1019–1031. https://
doi.org/10.1016/j.envpol.2017.07.093.
Lazzarini,M., Marpu, P.R., Ghedira, H., 2013. Temperature-landcover interactions: the in-
version of urban heat island phenomenon in desert city areas. Remote Sens. Environ.
130, 136–152. https://doi.org/10.1016/j.rse.2012.11.007.
Li, Q., Guan,X., Wu, P., Wang, X.,Zhou, L., Tong, Y., Ren,R., Leung, K.S.M.,Lau, E.H.Y., Wong,
J.Y., 2020a. Early transmission dynamics in Wuhan, China, of novel coronavirus–
infected pneumonia. N. Engl. J. Med. 26 382 (13), 1199–1207.
Li, L., Li, Q., Huang, L., Wang, Q., Zhu, A., Xu, J., Liu, Ziyi, Li, H., Shi, L., Li, R., Azari, M., Wang,
Y., Zhang, X., Liu, Zhiqiang, Zhu, Y., Zhang, K., Xue,S., Ooi, M.C.G., Zhang, D., Chan, A.,
A.S. Alqasemi, M.E. Hereher, G. Kaplan et al. Science of the Total Environment 767 (2021) 144330
10
2020b. Air quality changes during the COVID-19 lockdown over the Yangtze River
Delta Region:an insight into the impactof human activity patternchanges on air pol-
lution variation. Sci. Total Environ. 732, 139282. https://doi.org/10.1016/j.
scitotenv.2020.139282.
Li, H., Xu, X.-L., Dai, D.-W., Huang, Z.-Y., Ma, Z., Guan, Y.-J., 2020c. Air pollution and tem-
perature are associated with increased COVID-19 incidence: a time series study. Int.
J. Infect. Dis. 97, 278–282. https://doi.org/10.1016/j.ijid.2020.05.076.
Lin, P., Gou,Z., Lau, S.S.Y., Qin,H., 2017. The impact of urban design descriptorson outdoor
thermal environment:a literature review. Energies 10, 1–20. https://doi.org/10.3390/
en10122151.
Lorente, A., Boersma, K.F., Eskes, H.J., Veefkind, J.P., van Geffen, J.H.G.M., de Zeeuw, M.B.,
Denier van der Gon, H.A.C., Beirle, S., Krol, M.C., 2019. Quantification of nitrogen ox-
ides emissions from build-up of pollution over Paris with TROPOMI. Sci. Rep. 9,
20033. https://doi.org/10.1038/s41598-019-56428-5.
Lowe, S.A., 2016. An energy and mortality impact assessment of the urban heat islandin
the US. Environ. Impact Assess. Rev. 56, 139–144. https://doi.org/10.1016/j.
eiar.2015.10.004.
Lu, N., Liang, S., Huang, G., Qin, J., Yao, L., Wang, D., Yang, K., 2018. Hierarchical Bayesian
space-time estimation of monthly maximum and minimum surface air temperature.
Remote Sens. Environ. 211, 48–58. https://doi.org/10.1016/j.rse.2018.04.006.
Lyapustin, A., Wang, Y., 2018. MCD19A2 MODIS/Terra+Aqua Land Aerosol Optical Depth
Daily L2G Global 1km SIN Grid V006. NASA EOSDIS Land Processes DAAC https://doi.
org/10.5067/MODIS/MCD19A2.006 (accessed 8.27.20).
Lyapustin, A., Wang, Y., Korkin, S., Huang, D., 2018. MODIS collection 6 MAIAC algorithm.
Atmos. Meas. Tech. 11, 5741–5765. https://doi.org/10.5194/amt-11-5741-2018.
Mehdipour, V., Memarianfard, M., 2017. Application of support vector machine and gene
expression programming on tropospheric ozone prognosticating for Tehran metro-
politan. Civ. Eng. J. 3, 557–567.
Miles, V., Esau, I., 2017. Seasonal and spatial characteristics of Urban Heat Islands (UHIs)
in northern West Siberian cities. Remote Sens. 9. https://doi.org/10.3390/rs9100989.
Moradi, M., Salahi, B., Masoodian, S.A., 2018. On the relationship between MODIS land
surface temperature and topography in Iran. Physical Geography. Taylor & Francis
https://doi.org/10.1080/02723646.2018.1426167.
Motesaddi, S., Hashempour, Y., Nowrouz, P., 2017. Characterizing of air pollution in Teh-
ran: comparison of two air quality indices. Civ. Eng. J. 3, 749–758.
Mukherjee, S., Debnath, A., 2020. Correlation Between Land Surface Temperature and
Urban Heat Island With COVID-19 in New Delhi, India. https://doi.org/10.21203/
rs.3.rs-30416/v1.
Mulenga, D., Siziya, S., 2019. Indoor air pollution related respiratory ill health, a sequel of
biomass use. SciMed. J. 1, 30–37.
Nakada, L.Y.K., Urban, R.C., 2020. COVID-19 pandemic: impacts on the air quality during
the partial lockdown in São Paulo state, Brazil. Sci. Total Environ. 730, 139087.
https://doi.org/10.1016/j.scitotenv.2020.139087.
Nemati, M., Ebrahimi, B., Nemati, F., 2020. Assessment of Iranian nurses’knowledge and
anxiety toward COVID-19 during the current outbreakin Iran. Arch.Clin. Infect.Dis. 15.
Otmani, A., Benchrif, A., Tahri, M., Bounakhla, M., Chakir, E.M., El Bouch, M., Krombi, M.,
2020. Impact of Covid-19 lockdown on PM10, SO2 and NO2 concentrations in Salé
City (Morocco). Sci. Total Environ. 735, 139541. https://doi.org/10.1016/j.
scitotenv.2020.139541.
Pathakoti, M., Muppalla, A., Hazra, S., Dangeti, M., Shekhar, R., Jella, S., Mullapudi, S.S.,
Andugulapati, P., Vijayasundaram, U., 2020. An assessment of the impact of a
nation-wide lockdown on air pollution –a remote sensing perspective over India.
Atmos. Chem. Phys. Discuss. 2020, 1–16. https://doi.org/10.5194/acp-2020-621.
Ranjan, A.K., Patra, A.K., Gorai, A.K., 2020a. Effect of lockdown due to SARS COVID-19 on
aerosol optical depth (AOD) over urban and mining regions in India. Sci. Total Envi-
ron. 745, 141024. https://doi.org/10.1016/j.scitotenv.2020.141024.
Ranjan, A.K., Patra, A.K., Gorai, A.K., 2020b. Effect of lockdown due to SARS COVID-19 on
aerosol optical depth (AOD) over urban and mining regions in India. Sci. Total Envi-
ron. 745, 141024. https://doi.org/10.1016/j.scitotenv.2020.141024.
Schwarz, N.,Lautenbach, S., Seppelt, R., 2011. Exploring indicators for quantifying surface
urban heat islands of European cities with MODIS landsurface temperatures. Remote
Sens. Environ. 115, 3175–3186. https://doi.org/10.1016/j.rse.2011.07.003.
Sekmoudi,I., Khomsi, K., Faieq,S., Idrissi, L., 2020. Covid-19lockdown improvesair quality
in Morocco. arXiv preprint arXiv:2007.05417.
Sharma, S.,Zhang, M., Anshika, Gao,J., Zhang, H., Kota, S.H.,2020. Effect of restricted emis-
sions duringCOVID-19 on air quality in India. Sci. Total Environ. 728, 138878. https://
doi.org/10.1016/j.scitotenv.2020.138878.
Tobías, A., Carnerero, C., Reche, C., Massagué, J., Via, M., Minguillón, M.C., Alastuey, A.,
Querol, X., 2020. Changes in air quality during the lockdown in Barcelona (Spain)
one month into the SARS-CoV-2 epidemic. Sci. Total Environ. 726, 138540. https://
doi.org/10.1016/j.scitotenv.2020.138540.
United Nations, 2019. World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/
420). Department of Economic and Social Affairs Population Division, New York.
Veefkind,J.P., Aben, I., McMullan, K., Förster, H.,de Vries, J., Otter,G., Claas, J., Eskes,H.J., de
Haan, J.F., Kleipool,Q., van Weele, M., Hasekamp, O., Hoogeveen, R.,Landgraf, J., Snel,
R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., Vink, R., Visser, H., Levelt, P.F., 2012.
TROPOMI on the ESA Sentinel-5 precursor: a GMES mission for global observations
of the atmospheric composition for climate, air quality and ozone layer applications.
Remote Sens. Environ. 120, 70–83. https://doi.org/10.1016/j.rse.2011.09.027.
Venter, Z.S., Aunan, K., Chowdhury, S., Lelieveld, J., 2020. COVID-19 lockdowns cause
global air pollution declines with implications for public health risk. medRxiv
2020.04.10.20060673 https://doi.org/10.1101/2020.04.10.20060673.
Wang, Q., Su, M., 2020. A preliminary assessment of the impact of COVID-19 on environ-
ment –a case study of China.Sci. Total Environ.728, 138915. https://doi.org/10.1016/
j.scitotenv.2020.138915.
Wei, J., Huang, W., Li, Z., Xue, W., Peng, Y., Sun, L., Cribb, M., 2019. Estimating 1-km-
resolution PM2.5 concentrations across China using the space-time random forest ap-
proach. Remote Sens. Environ. 231, 111221. https://doi.org/10.1016/j.rse.2019.111221.
Weng, Q., Yang, S., 2006.Urban air pollution patterns, landuse, and thermal landscape: an
examination of the linkage using GIS. Environ. Monit. Assess. 117, 463–489. https://
doi.org/10.1007/s10661-006-0888-9.
WHO, 2020a.WHO Coronavirus Disease (COVID-19) Dashboard [WWWDocument]. URL.
https://covid19.who.int/. (Accessed 13 September 2020).
WHO, 2020b. Coronavirus Disease 2019 (COVID-19) Situation Report –51.
WHO, 2020c. Air pollution [WWW document]. URL. World Health Organizationhttps://
www.who.int/health-topics/air-pollution#tab=tab_1%0AAir. (Accessed 24 July 2020).
Wu, X., Nethery, R.C., Sabath, B.M., Braun, D., Dominici, F., 2020. Exposure to air pollution
and COVID-19 mortality in the United States: a nationwide cross-sectional study.
medRxiv: the preprint server for health sciences 2020.04.05.20054502 https://doi.
org/10.1101/2020.04.05.20054502.
Xu, K., Cui, K.,Young, L.-H., Hsieh, Y.-K., Wang, Y.-F., Zhang, J., Wan, S., 2020.Impact of the
COVID-19 event on air quality in Central China. Aerosol Air Qual. Res. 20, 915–929.
https://doi.org/10.4209/aaqr.2020.04.0150.
Zhang, Z., Arshad, A., Zhang, C., Hussain, S., Li, W.,2020. Unprecedentedtemporary reduc-
tion in global air pollution associated withCOVID-19 forced confinement:a continen-
tal and City scale analysis. Remote Sens. https://doi.org/10.3390/rs12152420.
Zhu, L., Huang, Q., Ren,Q., Yue, H., Jiao, C., He,C., 2020a. Identifying urban hazeislands and
extracting their spatial features. Ecol. Indic. 115, 106385. https://doi.org/10.1016/j.
ecolind.2020.106385.
Zhu, Y., Xie, J., Huang, F., Cao, L., 2020b. Association between short-term exposure to air
pollution and COVID-19 infection: evidence from China. Sci. Total Environ. 727,
138704. https://doi.org/10.1016/j.scitotenv.2020.138704.
A.S. Alqasemi, M.E. Hereher, G. Kaplan et al. Science of the Total Environment 767 (2021) 144330
11
