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Full Length Article
Impact of coronavirus disease (COVID-19) on gaseous pollutants and
particulate matter in a hot arid climate
Jasem A. Albanai
a,*
, Maryam Shehab
b
, Arie Vatresia
c
, Marium Jasim
d
, Hassan Al-Dashti
e
,
Mohamed F. Yassin
d
a
School of Geography and the Environment, University of Oxford, Oxford, UK
b
Environmental Public Authority, Kuwait
c
Informatics Engineering, Engineering Faculty, Universitas Bengkulu, Indonesia
d
Environment &Life Sciences Center, Kuwait Institute for Scientic Research, Safat, Kuwait
e
Meteorology Department, Directorate General of Civil Aviation, Kuwait
ARTICLE INFO
Keywords:
Kuwait
Generalized additive model
Air quality index
GeoHealth
Kernel density
Inverse distance weighted
ABSTRACT
The outbreak of the coronavirus disease (COVID-19) has had a signicant impact on the global community,
affecting various aspects of life. One of the unintended consequences of the pandemic is its effect on air quality.
This study uses the state of Kuwait as a case study to investigate the impact of the COVID-19 pandemic on levels
of gaseous pollutants (NO2, O3, SO2, H2S, CO) and particulate matter 2.5 and 10
μ
m (PM
2.5
and PM
10
) in a hot
arid region. Hourly data for COVID-19 infected cases and gaseous pollutants and particulate matter were
collected from January to December 2020. The Openair model with the R package was used to analyze gaseous
pollutants and particulate matter data with an applied air quality index (AQI). Stochastic models with time series
analysis - Kernel density were employed to investigate how COVID-19 infection can affect the changes of gaseous
pollutants and particulate matter. In addition, the spatial interpolation approach was estimated using the inverse
distance weighted (IDW). The Generalized Additive Model (GAM) was used to analyze the daily data of the
gasses and particle occurrences in a hot, dry climate. The results showed a signicant decrease in levels of
gaseous pollutants and particulate matter after implementing preventive measures to control the spread of the
virus. This reduction benets public health and the environment in the region, indicating that the preventive
measures taken to address the COVID-19 pandemic have contributed to reducing environmental pollution and
mitigating its adverse effects.
1. Introduction
The COVID-19 pandemic has caused a signicant impact on the
world, including changes in daily human activities and their environ-
mental impact. One of the main consequences of the pandemic is the
implementation of preventive measures such as lockdowns and social
distancing, which has reduced economic activities and transportation.
These measures may have resulted in changes in environmental pollu-
tion levels, especially gaseous pollutants and particulate matter.
Previous studies found that air pollution concentrations are associ-
ated with the increase in morbidity and mortality correlated to respi-
ratory diseases (Adamkiewicz et al., 2004;Cui et al., 2003), leading
researchers to investigate the effect of air pollution on COVID-19
infection. Recent studies found a correlation between the increase of
PM
2.5
and NO2 and the risk of increasing transmission of COVID-19
(Sasidharan et al., 2020). A study conducted in Italy, Spain, France,
and Germany reported that long-term exposure to higher concentrations
of NO2 might contribute to mortality caused by COVID-19 (Ogen, 2020).
Long-term exposure to PM
2.5
by 1
μ
g/m3 was also signicantly associ-
ated with an increase of 8% in COVID-19 mortality (Wu et al., 2020). On
short-term exposure, Zhu et al. (2020) reported that pollutants, namely
PM
2.5
, PM
10
, CO, NO2, and O3, are signicantly associated with the
infection of COVID-19 but not for SO2 in China (Zhu et al., 2020).
In Kuwait, natural and anthropogenic air pollution sources are both
local (e.g., oil reneries, trafc, dust, chemical manufacturing, water
treatment, and electricity plants) and transported (e.g., dust, fossil fuel
burning from Iraq), causing concentrations of some pollutants to exceed
the WHO guidelines limits, such as PM
10
and PM
2.5
(Al-Hemoud et al.,
* Corresponding author.
E-mail addresses: jasem.albanai@ouce.ac.uk,Albanay.com@gmail.com (J.A. Albanai).
Contents lists available at ScienceDirect
Kuwait Journal of Science
journal homepage: www.sciencedirect.com/journal/kuwait-journal-of-science
https://doi.org/10.1016/j.kjs.2024.100351
Received 8 November 2023; Received in revised form 8 December 2024; Accepted 9 December 2024
Kuwait Journal of Science 52 (2025) 100351
Available online 10 December 2024
2307-4108/© 2024 The Authors. Published by Elsevier B.V. on behalf of Kuwait University. This is an open access article under the CC BY license
(
http://creativecommons.org/licenses/by/4.0/ ).
2019;Alolayan et al., 2012;Alomair et al., 2013;Neelamani et al., 2015;
Brown et al., 2008), where between February 2004 and October 2005 p.
m.
2.5
was found to be 53
μ
g/m3, exceeding WHO guidelines. According
to Alolayan et al. (2012), high levels of PM
2.5
from local anthropogenic
sources include trafc, combustion of oil, and emissions from photo-
chemical works. However, more than 50% of ambient PM
2.5
is caused by
natural sources, mostly from transported sources. Anthropogenic
transported sources, including trafc and smelters, also contribute to
Kuwait’s ambient PM
2.5
.
Studies on air pollution before COVID-19 reached Kuwait and before
procedures were applied to limit human contact show that they can
exceed guidelines limits. According to Alhaddad et al. &Ramadan et al.
(Alhaddad et al., 2015;Safar et al., 2019;Ramadan et al., 2019), oil
elds, trafc, and reneries contribute signicantly to higher concen-
trations of SO2, VOCs, NO, CO, and CO2. In another study, SO2 was
found to have high concentrations exceeding US EPA standards. That
increase in population density, motor vehicles, industrial activities, and
power plants contribute to higher concentrations of SO2 and NOx
(Al-Baroud et al., 2017). In contrast, Brown et al. (Brown et al. (2008)
found that the higher concentrations of Pb, Zn, Cu, and Mn in urban
areas are related to trafc. Ozon (O3) concentrations in Kuwait were
found to be at their highest during summer (Al-Rashidi et al., 2018;
Yassin et al., 2018,2021), where they were found to be the highest in
urban areas (20–25
μ
g/m3) and during spring (i.e., March and April)
and late summer (i.e., September) (Abdul-Wahab et al., 2000). Sulfur
dioxide (SO2) and nitrogen dioxide (NO2) pollutants levels were found
to be higher during summer (i.e., June–September) due to emissions
from power plants created by using air conditioning extensively during
these months (Abdul-Wahab et al., 2000) and also found to be the
highest in urban areas, where the NO2 tropospheric vertical column is
53% above the median urban level of 28.13 ×1014 molecules cm-2, and
SO2 is 490 % above the average urban level (Barkley et al., 2018). In a
study by Al-Awadhi &Yassin (Al-Awadhi and Yassin, 2010), SO2 hourly
concentration in residential areas was found to be 380
μ
g/m3, exceeding
the hourly air quality limit values for both the Kuwait Environmental
Public Authority (KEPA) (170 ppb, 444
μ
g/m3) and EU standards (350
μ
g/m3). A study by Al-Awadhi &Yassin (Alsaber et al., 2020) in Kuwait
found that human exposure to NO2 and SO2 signicantly correlates with
rheumatoid arthritis. The carbon monoxide (CO) highest mean was
found during October (1.43 ppm), February (0.93 ppm), and June (0.74
ppm) (Abdul-Wahab et al., 2000). Another study predicted that emission
levels of CO were expected to increase by 71.8% by the year 2030
compared to 2015 levels (Alsaber et al., 2020).
Studies around the world have been conducted to investigate the
effect of COVID-19 on air quality; some of these studies found that
during COVID-19, air pollutants concentrations decreased, such as NO2
(Chossiere et al., 2021;JI and CHANG, 2020;Sannino et al., 2020), PM
10
(Anil and Alagha, 2021;Aljahdali et al., 2021;S¸ahin, 2020;Broomandi
et al., 2020), both PM
10
and PM
2.5
(JI and CHANG, 2020;Collivignarelli
et al., 2020;Garg et al., 2020;Kerimray et al., 2020;Mahato et al., 2020;
Fig. 1. The State of Kuwait, the urban area the Kuwait and KEPA air quality monitoring stations locations and information.
J.A. Albanai et al. Kuwait Journal of Science 52 (2025) 100351
2
Menut et al., 2020;Shakoor et al., 2020;Tobías et al., 2020), CO, SO2
(Sannino et al., 2020;Garg et al., 2020), NO and NOx, NH3, and C6H6
(Alsaber et al., 2020). However, concentrations of some pollutants
increased, such as O3 (Sannino et al., 2020;Garg et al., 2020), SO2
(Sannino et al., 2020), and PM
10
(Shakoor et al., 2020;Hashim et al.,
2020), due to either anthropogenic sources or dust events.
A study on pollution from trafc near schools found the highest
levels of CO and NO2 during the morning and afternoon on weekdays
and lower levels on weekends; maximum daily mean concentrations
reached 2.6 ppm for CO, and 46.7 ppb for NO2 during February
(Albassam et al., 2009). Measurements taken from the period
2012–2017 show that daily concentrations of SO2, NO2, and PM
10
have
exceeded the KEPA guidelines in both residential and industrial areas,
where these pollutants in the residential areas are coming from indus-
trial activities and vehicles. Meteorological conditions (e.g., tempera-
ture and humidity) also affect the increasing concentrations of these
pollutants (Al-Hurban et al., 2021). H2S concentrations are found to be
higher in urban areas near petroleum work facilities (Al-Salem and
Khan, 2008).
Environmental pollution is a serious issue that affects public health
and the environment. The COVID-19 pandemic provides a unique op-
portunity to assess the impact of human activities on air pollution levels.
Understanding the changes in air pollution levels during the pandemic
can inform policy decisions to reduce environmental pollution levels
and improve public health. This study aims to investigate the impact of
the COVID-19 pandemic on levels of gaseous pollutants (nitrogen di-
oxide (NO
2
), ozon (O
3
), sulfur dioxide (SO
2
), hydrogen sulde (H
2
S),
and carbon monoxide (CO)) and particulate matter 2.5 and 10
μ
m (PM
2.5
and PM
10
) in a hot arid region. This study aims to analyze daily data
collected before and after the virus outbreak to determine changes in
pollutant levels and assess the effectiveness of the implemented pre-
ventive measures in reducing environmental pollution levels. The results
of this study will contribute to a better understanding of the impact of
the COVID-19 pandemic on the environment and inform policy de-
cisions aimed at reducing environmental pollution levels.
2. Methodology
2.1. Study area
Kuwait is a country located in the Middle East, bordered by Iraq and
Saudi Arabia (Albanai et al., 2022). The country has a high population
density, and residential areas are distributed nationwide (Fig. 1). Most
of Kuwait’s lands are at, and with its location under the inuence of
subtropical high pressure, it is thus one of the hottest regions in the
world (Hassan et al., 2024;Albanai, 2020). The temperature in the
summer exceeds 50 ◦C. At the same time, it drops to below 7◦in the
winter, and thus it is characterized by a wide temperature range be-
tween summer and winter (Albanai, 2021a). Kuwait has a hot, arid
climate which is characterized by airborne dust. Dust event is one of the
Fig. 2. The monthly geographical distribution of CO in 2020 in the State of Kuwait from January to December 2020.
J.A. Albanai et al. Kuwait Journal of Science 52 (2025) 100351
3
common weather and climate phenomena in Kuwait (Albanai, 2021b,
2021c). Belt dust storms intensify in the summer, along with the
north-western winds, while these strong dust-laden winds ease in the
winter. Kuwait includes six governorates: Al-Asimah, Hawalli,
Al-Farawaniyah, Al-Jahra, Mubarak Al-Kabeer, and Al-Ahmadi. The
study area for this research is the residential areas across the country,
where all of these residents are concentrated in Kuwait’s urban areas,
where almost all of the activities are concentrated.
2.2. Data description
The data on the outbreak of COVID-19 infected cases were collected
by the Ministry of Health of the State of Kuwait. In contrast, the air
quality data used in this study during the outbreak of COVID-19 were
collected from eighteen air quality monitoring stations (Fig. 1) across
Kuwait, which belong to KEPA (epa.gov.kw). These stations were
established to monitor air quality in Kuwait and maintain the current
rates of various air quality variables at safe and permissible limits. The
stations are distributed geographically to different regions of the coun-
try, where most of them are located in the residential suburbs of the
governorates of the State of Kuwait (Fig. 1). The stations measure the
different concentrations of air quality measurement variables, both
natural and those that emit through human activity. The variables
include readings of nitrogen dioxide, sulfur dioxide, ground-level ozone,
carbon monoxide, hydrogen sulde, and particulate matter (PM
2.5
and
PM
10
). Additionally, meteorological data for 2020 (including air tem-
perature, wind direction and speed, humidity, perception, air pressure,
and visibility) was used for the Kuwait Airport station of the Meteoro-
logical Department (met.gov.kw) to analyze air quality data.
2.3. Spatial analysis
The monthly averages of the air quality variables for 2020 were
extracted in Kuwait’s urban area. An inverse distance weighted (IDW)
spatial derivation model was built based on the monthly averages
extracted from January and December to compare the spatial and tem-
poral distribution. The model was applied using ArcGIS Pro 2.1 software
depending on the number of points entered, using only the geographical
area covered by Kuwait’s urban area as a specic range for the deriva-
tion. The IDW is a way to estimate an unknown point value from several
surrounding known point values. The best results for this model are
obtained when the distribution of control points (samples) is of high
density and has a wide spatial spread over the study area concerned to
simulate all existing spatial differences; otherwise, the model results
may be affected (Watson and Philip, 1985). The IDW technique provides
better accuracy in the spatial enhancement of the raster model with
conditions of high values of the coefcient of variation, strong anisot-
ropy, and spatial structure (Chaplot et al., 2006). The following algo-
rithm is used to calculate the IDW model:
Fig. 3. The monthly geographical distribution of NO
2
in 2020 in the State of Kuwait from January to December 2020.
J.A. Albanai et al. Kuwait Journal of Science 52 (2025) 100351
4
zx0=Σn
i=1xi/hβ
ij +Σn
i=11∕hβ
ij (1)
Where z(x0) is the output value, xiis the value of control known points,
hij is the separation distance between the interpolated value and the
control points value, and ßis the weighting power, and nis the total
number of the control points (samples) values.
2.4. Statistical analysis
The daily data of the gases and particulate matter with the meteo-
rological conditions during the outbreak of COVID-19 were analyzed
using the Openair model with the R programming package and the
Generalized Additive Modelling (GAM) algorithm (Carslaw, 2019;Car-
slaw and Ropkins, 2012;Ropkins and Carslaw, 2012). The Openair
model is a tool within the openair package in the R programming lan-
guage that is designed for air quality modeling. Analyzing trends is
crucial for managing air quality, providing insights into historical pat-
terns, and evaluating strategies for pollution control. Openair offers
three functions for trend analysis: (a) MannKendall, (b) smoothTrend,
and (c) LinearRelation. MannKendall utilizes methods based on Hirsch
et al. (1982) and Helsel &Hirsch (Helsel and Hirsch, 1993) to assess
monotonic trends. The function utilizes a fundamental approach to
trend detection, which is further enhanced by several methods. Firstly,
bootstrap techniques are integrated to estimate uncertainty (Johnson,
2001). Additionally, the estimation of slope and its uncertainty makes
use of the Sen method (Sen, 1968), incorporating bootstrap and
block-bootstrap techniques to quantify slope uncertainties. The method
has been expanded to incorporate block bootstrap simulation for
addressing autocorrelation, as outlined by Kunsch (1989). SmoothTrend
utilizes a generalized additive model (GAM) tted to monthly averaged
data, employing techniques and functions from the mgcv package
(Wood, 2004,2017). Both functions within Openair include the capa-
bility to deseasonalize data before analysis, leveraging the stl function in
the stats package (Cleveland). SmoothTrend, in particular, is valuable
for identifying clear non-linear patterns in time trends and under-
standing the specic types of changes that have occurred. The line-
arRelation function employs a rolling window linear regression method
to visualize the degree of change in the relations between two species
over longer periods.
On the other hand, the Air Quality Index (AQI) was also used to
analyze Kuwait’s air quality. The AQI is calculated based on the levels of
different gas pollutants and particulate matter present in the air during
the outbreak of COVID-19. The AQI is typically reported on a scale from
0 to 500, with higher values indicating more polluted air. The AQI can
be divided into categories, ranging from “good”to “hazardous,”based
on pollution level. The Index was calculated using the following rela-
tionship that the US Environmental Protection Agency developed to
calculate the AQI for each pollutant:
Ip=IHi −ILo
BPHI −BPLo (Cp−BPLo)+ILo (2)
Fig. 4. The monthly geographical distribution of H
2
S in 2020 in the State of Kuwait from January to December 2020.
J.A. Albanai et al. Kuwait Journal of Science 52 (2025) 100351
5
Where, Ip=the index for pollutant p, Cp=the rounded concentration of
pollutant p, and IHi =the AQI value corresponding to the breaking point
high (BP Hi). ILo =the AQI value corresponding to the breaking point
low (BP Lo), BPHI =the breakpoint that is greater than or equal to Cp,
and BPLo =the breakpoint that is less than or equal to Cp.
3. Results and discussion
3.1. Spatial analysis of pollutants during the outbreak of COVID-19
The monthly distribution of CO pollutants is shown in Fig. 2. It can be
seen that the distribution of CO over Kuwait shows the highest rate of CO
in February before the pandemic and lockdown happened in Kuwait.
The amount of CO was signicantly reduced during the following
months up to June 2020. The distribution of CO itself is almost the same
over several districts in Kuwait. After the middle part of the year, the
concentration of CO increased in some parts of the urban area and
continued to spread all over the metropolitan area around Kuwait up to
the end of the year.
At the same time, the distribution of NO2 pollutants is shown in
Fig. 3, which shows that the highest concentration of NO2 happened in
January when human activity was still very high, and the vehicle’s
mobility was still considered active. The concentration was reduced by
the time up to June, as the CO decreased from the air. This happened
while human mobility was also declining due to the pandemic.
Furthermore, the rate of NO2 was also increasingly high in September,
when cancellation of the curfew was imposed in all regions of the State
of Kuwait from August 30, 2020, when anthropogenic activities started
to return to normal, including transportation. NO2 was reduced during
the lockdown and curfew and increased before pandemic precautions
were applied. When the lockdown was canceled, it only showed the
highest level in February and October, with a signicantly high level in
Fahaheel, an industrial and urban area due to anthropogenic sources
(Barkley et al., 2018). As in previous years, CO levels showed an increase
during February before pandemic precautions were implemented
(Yassin et al., 2018). These levels subsequently declined due to the
reduction in anthropogenic sources following the start of curfew re-
strictions. However, as lockdown measures in certain areas were lifted,
CO levels began to rise again with the resumption of services, including
taxi operations, partial re-opening of government and private sectors
(with less than 50% of employees), and the reopening of hotels, resorts,
and social care homes. In previous years, both NO2 (Tobías et al., 2020)
and CO (Tobías et al., 2020;Alhaddad et al., 2015) were found to be at
the highest levels due to trafc (Alhaddad et al., 2015;Tobías et al.,
2020) and oil renery activities (Alhaddad et al., 2015). NO2 levels also
increased in previous years due to the emissions from power plants
produced by air conditioning (Yassin et al., 2018).
In contrast, the distribution of H2S is shown in Fig. 4, which shows
that the concentration of H2S is relatively stable in certain areas where
the activity of oil and well completion happened. The highest
Fig. 5. The monthly geographical distribution of O
3
in 2020 in the State of Kuwait from January to December 2020.
J.A. Albanai et al. Kuwait Journal of Science 52 (2025) 100351
6
concentration of H2S can be seen in June 2020, when oil activity has
been increasingly active and massive. The H2S concentration is near the
area of A and B and spread with low concentration over the surrounding
area. Continuous exposure to H2S has the potential to cause health
problems. In addition to affecting and causing danger to human health,
H2S also affects metal equipment because H2S is corrosive to metals. In
practice, these conditions can occur in pipelines or metal tanks for oil
and gas sector activities, so special handling is needed to avoid corro-
sion, which will result in cracks or leaks. In addition, H2S will also cause
ferrous sulde (FeS) to rust on ferrous metals. The FeS is pyrophoric,
which, when it reacts with oxygen in the air, will produce heat. H2S
increased due to oil renery activities; before the pandemic, the levels of
this pollutant were high in urban areas near petroleum activities
(Albassam et al., 2009).
While the distribution of O3 is shown in Fig. 5. The concentration of
O3 is still considered low in January 2020. The concentration of O3 is no
more than 20 ppm all over the area. But this condition changed as the
time up to June 2020, when the concentration of O3 in some areas can
reach the maximum number of 40 ppm in some areas in the north, while
the area in the south has not signicantly increased and is still around
the number below 25 ppm. The condition of the three concentrations
changed from July to December 2020, where the O3 concentration also
decreased and showed the lowest value again in December 2020. This
may happen as the temperature and the weather in Kuwait City have
changed over the years, affecting the concentration of O3. O3
concentrations uctuated and showed a decrease in summer, where it
used to be high during this time in previous years (Ramadan et al., 2019;
Al-Baroud et al., 2017;Al-Rashidi et al., 2018;Yassin et al., 2018,2021);
this shows that a drop in trafc signicantly decreased O3.
In comparison, the distribution of sulfur dioxide (SO2) is shown in
Fig. 6. It can be seen that the lowest concentration of SO2 in Kuwait is in
November 2020, when the weather is relatively cold. The high con-
centration of SO2 is only happening in some areas where human activity
increases, especially in June, September, and October. SO2 was found to
be low in November due to a decrease in temperature and found to be
high in some areas, especially in June, September, and October, due to
anthropogenic activities, where curfew hours decreased, and then were
canceled in all of Kuwait for a partial return to everyday life. This proved
that the high levels of SO2 in earlier years were due to increased pop-
ulation, trafc, power plant work, and industrial activities (Safar et al.,
2019). In contrast, in previous years, it used to be found in high con-
centrations in residential areas exceeding the limits of the guidelines
(Yassin et al., 2021), especially in summer, from the emissions produced
from excessive use of air conditioning in power plants (Yassin et al.,
2018).
On the other hand, the distribution of particulate matter is shown in
Figs. 7 and 8. The concentration of PM
2.5
in the air in Kuwait City
showed the highest part in Jahra. The concentration of this particle was
signicantly high in February, April, June, August, September,
November, and December. In contrast, the eastern part of Kuwait
Fig. 6. The monthly geographical distribution of SO
2
in 2020 in the State of Kuwait from January to December 2020.
J.A. Albanai et al. Kuwait Journal of Science 52 (2025) 100351
7
showed medium concentrations, with relatively low levels observed in
January through March and May. The northern area, however, consis-
tently displayed relatively high concentrations throughout the year
(Fig. 9). PM
2.5 was
found to be low from January to March and May due
to decreasing anthropogenic activities as a result of the lockdown and
curfew. PM
10
concentrate showed the lowest concentration in February
and relatively stabilized in November (Fig. 10). PM
10
was found to be at
its lowest during February; this is a result of the curfew and banning
celebrations during National Day and Liberation Day of Kuwait, which
decreased anthropogenic activities, where in previous years it used to
exceed the KEPA guidelines (Hashim et al., 2020).
From the previous Figs. 2–8, it can be concluded that the concen-
tration of CO showed the highest rate all over the area in February.
While the concentration of H2S is relatively dynamic and different in
every area, the highest rate showed in June in the areas of Ahmadi,
Shuwaikh, and Saad Al-Abdullah. The concentration of NO2 showed the
highest rate in February and October and showed a signicant high in
the area of Fahaheel when it reached 96.39 ppm. The rate of O3 is also
dynamic and different for each area but showed the highest concen-
tration rate in June when it reached 63.1 in Mansouria. Furthermore, the
concentration of SO2 also showed a dynamic value over time and
showed the highest peak of concentration in June in Ahmadi. SO2
appearance every year shows that the peak of occurrence in Kuwait
happened in June and October. The rate also indicates that the event of
SO2 increases at the end and the beginning of next year. H2S shows an
increasing trend throughout the year and keeps increasing to the
beginning of next year. The occurrence of O3 shows a peak in June 2020
and decreases in another month. This happened during the six months of
2020, while the beginning of the year shows an increasing pattern. The
appearance of O3 seems to return to its beginning value at the end of the
year. NO appears to have an increasing design during the year 2020. CO
also has a growing pattern in 2020 and shows the highest value in
November 2020, while the lowest point occurrence was in May 2020
when the lockdown began. The PM
10
shows ve outliers during the year
2020 and shows stable form from the beginning of the year. The PM
2.5
shows a slightly increasing pattern.
3.2. Air quality index (AQI) during the COVID-19
The AQI measures how polluted the air is during COVID-19. The AQI
is calculated based on the levels of different gas pollutants and partic-
ulate matter in the air (Table 1). It is evident in this table that the
concentration of air pollution in Kuwait, based on the averages, is still
considered good and shows the relatively same each month. However,
the concentration of PM
2.5
showed an acceptable rate for the whole year.
The concentration of PM
10
showed good condition in January,
November, and December while showing an acceptable rate in the rest
of the months.
Fig. 7. The monthly geographical distribution of PM
2.5
in 2020 in the State of Kuwait from January to December 2020.
J.A. Albanai et al. Kuwait Journal of Science 52 (2025) 100351
8
Fig. 8. The monthly geographical distribution of PM
10
in 2020 in the State of Kuwait from January to December 2020.
J.A. Albanai et al. Kuwait Journal of Science 52 (2025) 100351
9
Fig. 9. Trend level of average pollutants (NO
2
, O
3
, SO
2
, H
2
S, CO, PM
2.5
&PM
10
) in every wind direction.
J.A. Albanai et al. Kuwait Journal of Science 52 (2025) 100351
10
Fig. 10. Trend level of average pollutants (NO
2
, O
3
, SO
2
, H
2
S, CO) every hour. PM
2.5
and PM
10
hourly data are not available.
J.A. Albanai et al. Kuwait Journal of Science 52 (2025) 100351
11
3.3. Trend analysis of pollutants during the COVID-19
The Openair model was used for trending analysis of pollutants used
in this study to understand the change in these pollutant concentrations
over time during the outbreak of COVID-19. Fig. 9 shows the trend level
for the pollutants in each wind direction. For the CO pollutant, the
highest was in October, which is between 1.3 and 1.4 ppm from the
south, southeast, and southwest, where the highest concentration for
H2S was between 5.5 and 6 ppb in June and July, respectively, coming
from the north to east. The highest concentration of NO2 was observed
in September and October, which is between 45 and 50 ppb from the
South direction. For the O3, the highest concentration was registered in
June between 40 and 45 ppb from the South East and East directions.
SO2 was recorded with the highest concentration in September and
October; it reached between 12 and 14 ppb from the Northeast and East
direction. PM
10
and PM
2.5
registered the highest readings in March.
Also, PM
10
recorded the highest readings in April; both are trending
from the southwest. At the same time, the trend level for the hourly
average pollutants is shown in Fig. 10. For CO, the highest hourly was in
October, which was between 1.4 and 1.6 ppm, recorded at 8–11 p.m.,
whereas the highest concentration for H2S was between 6.5 and 7 ppb in
June, which it recorded from 1 p.m. to 5 p.m. The highest concentration
for NO2 was observed in September and October, which is higher than
50 ppb recorded from almost 7 p.m. to 11 p.m. For O3, the highest
concentration was registered in May and June. Again, it reached its
highest concentration in September and October. In the two periods, it
reached between 40 and 45 ppb, in May and June, the highest concen-
tration was reached from 11 a.m. to 5 p.m., whereas in September and
October, from 12 p.m. to 5 p.m. For SO2, the highest concentration was
recorded in October. It reached between 14 and 16 ppb from 7 p.m. to 8
p.m. PM
10
and PM
2.5
there was no hourly data that was recorded.
Alternatively, the temporal variation in pollutant concentration
during the COVID-19 outbreak can be assessed by examining the times
of minimum and maximum concentrations, observing trends between
weekdays and weekends to see whether levels tend to rise or fall, and
determining the months when concentrations are typically higher or
lower. Therefore, the overall result for criteria pollutants’time variation
for different concentrations is shown in Fig. 11. The variability of these
pollutant concentrations is conditioned by road trafc and oil rening
emissions. Consequently, it can be noted that the hourly concentration
of CO increased from 6 a.m. till it reached the peak at approximately 7 a.
m. Moreover, again, it starts to grow from 2 p.m. till 10 p.m. The lowest
concentration during the week was observed on the weekend. On the
other hand, the highest monthly concentration was observed during July
and October, when the diurnal variation involving the short-term effect
of local sources was eliminated. The highest day with the CO concen-
tration is Thursday.
H2S reaches its highest hourly concentration from noon until its peak
at 10 p.m., where the highest month was in June; during the diurnal
variation, the highest day was Thursday. The NO2 hourly concentration
was recorded as high from 1 p.m. until its peak at 10 p.m.; the highest
month were September and October. The diurnal variation reached its
peak on Tuesday, Thursday, and Saturday. The O3 concentration
reached its highest level in the summertime due to the reaction of NCH4
with NOx in the presence of light from the sun and the lowest in the
wintertime. Moreover, the peak was reached in May for the PM
2.5
pollutant as its concentration increased by more than 200. A second
signicant peak was reached in March, where the PM
2.5
concentrations
were slightly less than 200. On the other hand, the lowest concentrations
of the same pollutant were shown almost in March and April. Further-
more, for the PM
10
pollutant, the highest peaks were somehow similar to
PM
2.5
where the highest concentrations were reached in May and March,
respectively. However, PM
10
pollutants showed more peaks than PM
2.5
especially from February to May. Again, the lowest concentrations of
PM
10
were shown in January and October. For SO2 pollutants, the
concentration started to increase from 6 a.m. until it reached the peak at
noon. Moreover, the highest monthly concentration was shown in June,
September, and October, where the diurnal variation, which involves
the short-term effect of local sources was eliminated. The day with the
highest CO concentration is Wednesday.
One of the main limitations of this study is the reliance on data, albeit
hourly, for a relatively short time. Providing a multi-year database can
contribute to a better understanding of the impact of COVID-19 on air
quality, as the temporal and spatial patterns can be tracked for a longer
period before and after the pandemic. Also, although the study relies on
a good number of air quality monitoring stations, the IDW model pro-
vides better results when relying on a large number of points in the study
area, where the spatial patterns are more natural. In this context, using
remote sensing data to extract air quality variables that can be used from
space will provide a better view of the spatial and temporal dimensions,
especially when using high spatial and temporal resolution data.
4. Conclusion
The inverse distance weighted (IDW), Air Quality Index (AQI), and
Openair model were used in this study to analyze, interpret, and un-
derstand air quality data during the outbreak of COVID-19 in 2020. The
overall results in this study concluded that during COVID-19 in the year
Table 1
Analysing AQI in Kuwait based on the monthly averages of the six studied gases.
J.A. Albanai et al. Kuwait Journal of Science 52 (2025) 100351
12
2020, concentrations of all the pollutants except PM
2.5
were decreased
when the precautions were applied during the pandemic, primarily
because of the decrease in anthropogenic activities. However, PM
2.5
data showed increased concentrations of the pollutants, primarily
because of the transported dust. This paper proves that anthropogenic
activities contribute to the increase of most air pollutants. Hence, new
regulations and laws should be set to limit and control these activities to
reduce and control the rise in air pollution, which will reduce health
problems, have more clean air quality, and decrease the nancial burden
on several sectors in the country, including the medical sector. We
recommend applying some laws from the precautions used during the
pandemic, such as working from home and shortening working hours for
some jobs. This study presents a spatial-statistical methodology that can
be followed to assist air quality during incidents such as COVID-19.
Fig. 11. Time variation for pollutants (NO
2
, O
3
, SO
2
, H
2
S, CO) per hour, week, and (PM
2.5
and PM
10
) month.
J.A. Albanai et al. Kuwait Journal of Science 52 (2025) 100351
13
CRediT authorship contribution statement
Jasem A. Albanai: Writing –review &editing, Writing –original
draft, Visualization, Supervision, Project administration, Methodology,
Conceptualization. Maryam Shehab: Writing –review &editing,
Writing –original draft, Resources, Formal analysis. Arie Vatresia:
Writing –original draft, Visualization, Formal analysis. Marium Jasim:
Writing –original draft, Visualization. Hassan Al-Dashti: Supervision,
Resources, Data curation. Mohamed F. Yassin: Writing –review &
editing, Supervision, Resources, Conceptualization.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Data availability of data and material
The data that support the ndings of this study are available from
[Environmental Public Authority - Kuwait] but restrictions apply to the
availability of these data, which were used under license for the current
study, and so are not publicly available. Data are however available
from the authority via this email: data@emisk.org upon reasonable
request.
Funding
This research received no specic grant from any funding agency in
the public, commercial, or not-for-prot sectors.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Acknowledgments
The authors thank the Kuwait Environmental Public Authority
(KEPA). Especially, eMISK and air quality monitoring departments.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.kjs.2024.100351.
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