ACTA GEOGRAPHICA
SLOVENICA GEOGRAFSKIZBORNIK
2024
64
3
0101661851779
ISSN 1581-6613
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24ACTA GEOGRAPHICA SLOVENICA
GEOGRAFSKI ZBORNIK
64-3 • 2024
Contents
Borut Stojilković, valentina Brečko GruBar
Discharge regimes of Slovenian rivers: 1991–2020 7
radomir BodiroGa, tijana Banjanin, dajana vukojević ateljević,
Simon kerma
The trends in viticulture and winemaking in the context of wine tourism development
in Bosnia and Herzegovina 33
anđela vrkić, ante Blaće
Land use changes in Southern Croatia (Dalmatia) since the beginning of the 20th century 49
nuri erkin Öçer, dilek küçük matci, uğur avdan
Monitoring the impact of the Corona pandemic on nitrogen dioxide emissions at large
scales via Google Earth Engine 75
Zala virant, janez oSojnik, andreja koZmuS
Environmental responsibility and communication in selected companies in the Podravska
statistical region 97
Sai-leung nG, ching-Hua tien
Mapping the landscape of recent research on agricultural geography (2013–2022) 111
aleš Smrekar, jernej tiran, katarina Polajnar Horvat
Unveiling the cultural ecosystem services of urban green spaces:
A case study of Ljubljana, Slovenia 135
naslovnica 64-3_naslovnica 49-1.qxd  25.11.2024  7:21  Page 1
ACTA GEOGRAPHICA
SLOVENICA GEOGRAFSKIZBORNIK
2024
64
3
0101661851779
ISSN 1581-6613
A
C
TA
 G
E
O
G
R
A
P
H
IC
A
 S
LO
V
E
N
IC
A
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G
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Z
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R
N
IK
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64
-3
 •
20
24ACTA GEOGRAPHICA SLOVENICA
GEOGRAFSKI ZBORNIK
64-3 • 2024
Contents
Borut Stojilković, valentina Brečko GruBar
Discharge regimes of Slovenian rivers: 1991–2020 7
radomir BodiroGa, tijana Banjanin, dajana vukojević ateljević,
Simon kerma
The trends in viticulture and winemaking in the context of wine tourism development
in Bosnia and Herzegovina 33
anđela vrkić, ante Blaće
Land use changes in Southern Croatia (Dalmatia) since the beginning of the 20th century 49
nuri erkin Öçer, dilek küçük matci, uğur avdan
Monitoring the impact of the Corona pandemic on nitrogen dioxide emissions at large
scales via Google Earth Engine 75
Zala virant, janez oSojnik, andreja koZmuS
Environmental responsibility and communication in selected companies in the Podravska
statistical region 97
Sai-leung nG, ching-Hua tien
Mapping the landscape of recent research on agricultural geography (2013–2022) 111
aleš Smrekar, jernej tiran, katarina Polajnar Horvat
Unveiling the cultural ecosystem services of urban green spaces:
A case study of Ljubljana, Slovenia 135
naslovnica 64-3_naslovnica 49-1.qxd  25.11.2024  7:21  Page 1
ACTA GEOGRAPHICA SLOVENICA
64-3
2024
ISSN: 1581-6613
UDC: 91
2024, ZRC SAZU, Geografski inštitut Antona Melika
International editorial board/mednarodni uredniški odbor: Zoltán Bátori (Hungary), David Bole (Slovenia), Marco Bontje (the Netherlands),
Mateja Breg Valjavec (Slovenia), Michael Bründl (Switzerland), Rok Ciglič (Slovenia), Špela Čonč (Slovenia), Lóránt Dénes Dávid
(Hungary), Mateja Ferk (Slovenia), Matej Gabrovec (Slovenia), Matjaž Geršič (Slovenia), Maruša Goluža (Slovenia), Mauro Hrvatin
(Slovenia), Ioan Ianos (Romania), Peter Jordan (Austria), Drago Kladnik (Slovenia), Blaž Komac (Slovenia), Jani Kozina (Slovenia),
Matej Lipar (Slovenia), Dénes Lóczy (Hungary), Simon McCarthy (United Kingdom), Slobodan B. Marković (Serbia), Janez Nared
(Slovenia), Cecilia Pasquinelli (Italy), Drago Perko (Slovenia), Florentina Popescu (Romania), Garri Raagmaa (Estonia), Ivan Radevski
(North Macedonia), Marjan Ravbar (Slovenia), Aleš Smrekar (Slovenia), Vanya Stamenova (Bulgaria), Annett Steinführer (Germany),
Mateja Šmid Hribar (Slovenia), Jure Tičar (Slovenia), Jernej Tiran (Slovenia), Radislav Tošić (Bosnia and Herzegovina), Mimi Urbanc
(Slovenia), Matija Zorn (Slovenia), Zbigniew Zwolinski (Poland)
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Executive editor/odgovorni urednik: Drago Perko (ZRC SAZU, Slovenia)
Chief editors/področni urednik (ZRC SAZU, Slovenia):
• physical geography/fizična geografija: Mateja Ferk, Matej Lipar, Matija Zorn
• human geography/humana geografija: Jani Kozina, Mateja Šmid Hribar, Mimi Urbanc
• regional geography/regionalna geografija: Matej Gabrovec, Matjaž Geršič, Mauro Hrvatin
• regional planning/regionalno planiranje: David Bole, Maruša Goluža, Janez Nared
• environmental protection/varstvo okolja: Mateja Breg Valjavec, Aleš Smrekar, Jernej Tiran
Editorial assistants/uredniška pomočnika: Špela Čonč, Jernej Tiran (ZRC SAZU, Slovenia)
Journal editorial system manager/upravnik uredniškega sistema revije: Jure Tičar (ZRC SAZU, Slovenia)
Issued by/izdajatelj: Geografski inštitut Antona Melika ZRC SAZU
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Co-published by/sozaložnik: Slovenska akademija znanosti in umetnosti
Address/naslov: Geografski inštitut Antona Melika ZRC SAZU, Gosposka ulica 13, p. p. 306, SI – 1000 Ljubljana, Slovenija;
ags@zrc-sazu.si
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Front cover photography: Sveta Gora, a settlement with a franciscan monastery overlooking the Soča valley, renowned as a Marian
pilgrimage site, is located near the Slovenia-Italy border, at the intersection of Alpine, Medditerranean and Dinaric landscapes
(photograph: Jure Tičar).
Fotografija na naslovnici: Sveta Gora, naselje s frančiškanskim samostanom nad dolino Soče, ki je znano po marijanskem romarskem
središču, leži na meji Slovenije in Italije ter na stiku alpskih, sredozemskih in dinarskih pokrajin (fotografija: Jure Tičar).
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Acta geographica Slovenica, 64-3, 2024, 75–95
MONITORING THE IMPACT OF THE
CORONA PANDEMIC ON NITROGEN
DIOXIDE EMISSIONS AT LARGE SCALES
VIA GOOGLE EARTH ENGINE
Nuri Erkin Öçer, Dilek Küçük Matcı, Uğur Avdan
The monitoring of vast expanses is a key function of Earth observation satellites.
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76
DOI: https://doi.org/10.3986/AGS.13454
UDC: 528.8:913”2019/2021”
Creative Commons CC BY-NC-ND 4.0
Nuri Erkin Öçer1, Dilek Küçük Matcı1, Uğur Avdan1
Monitoring the impact of the Corona pandemic on nitrogen dioxide emissions
at large scales via Google Earth Engine
ABSTRACT: Advances in Earth observation capabilities and the expanded accessibility of data provide
the opportunity to monitor air pollution on a global scale. The Google Earth Engine (GEE) enables the
efficient conduct of such large-scale research. This article examines the impact of the COVID-19 pandemic
on NO2 emissions at various supranational scales, with particular consideration of the Human Development
Index of the countries, using GEE. The findings for the first three months of 2020 indicating a reduction
in emissions of more than 4% per month, demonstrate that not only were the restrictions and closures
imposed by governments effective in the global decline of NO2 levels, but also voluntary restrictions imposed
by people on their own mobility with the motive of protection from the pandemic.
KEYWORDS: remote sensing, Earth observation, Sentinel-5P, tropospheric NO2, Google Earth Engine,
Human Development Index
Spremljanje vpliva pandemije koronavirusa na emisije dušikovega dioksida
v velikem merilu s programom Google Earth Engine
POVZETEK : Napredek v zmogljivostih opazovanja Zemlje in večja dostopnost podatkov omogočata spreml-
janje onesnaženosti zraka na svetovni ravni. Google Earth Engine (GEE) omogoča učinkovito izvajanje
takšnih obsežnih raziskav. Ta članek z uporabo GEE preučuje vpliv pandemije covida-19 na emisije NO2
na različnih nadnacionalnih ravneh, s posebnim upoštevanjem indeksa človekovega razvoja v državah.
V prvih treh mesecih leta 2020 je prišlo do zmanjšanja za več kot 4 % na mesec, kar kaže, da pri global-
nem zmanjšanju ravni NO2 niso bile učinkovite le omejitve in zaprtja, ki so jih uvedle vlade, temveč tudi
prostovoljne omejitve, ki so jih ljudje uvedli za lastno mobilnost z motivom zaščite pred pandemijo.
KLJUČNE BESEDE: daljinsko zaznavanje, opazovanje Zemlje, Sentinel-5P, troposferski NO2, Google Earth
Engine, indeks človekovega razvoja
The article was submitted for publication on December 14th, 2023.
Uredništvo je prejelo prispevek 14. decembra 2023.
1 Eskisehir Technical University, Earth and Space Sciences Institute, Eskisehir, Turkey
neocer@eskisehir.edu.tr (https://orcid.org/0000-0001-7404-7686), dkmatci@eskisehir.edu.tr
(https://orcid.org/0000-0002-4078-8782), uavdan@eskisehir.edu.tr (https://orcid.org/0000-0001-
7873-9874)
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1 Introduction 
First reported in Wuhan, China, in January 2020, the COVID-19 epidemic spread rapidly around the world.
Soon after the outbreak, almost every country introduced various measures to contain the spread of the
disease, including travel restrictions and curfews, with varying degrees of severity (Hale et al. 2021; Singh
and Chauhan 2020). Some governments implemented a complete quarantine, while others opted for par-
tial lockdowns or mobility restrictions (Zhang et al. 2021). For instance, while the Chinese central government
implemented a complete shutdown in Wuhan, the Turkish government enacted a nationwide but partial
shutdown (Bacak et al. 2020; Lian et al. 2020). The implementation of these measures has resulted in a notable
decline in human mobility. Since the majority of air pollutants are of anthropogenic origin and closely relat-
ed to human mobility, there has also been a significant reduction in air pollution during the pandemic
period. For example, Tobías et al. (2020) observed a notable decline in PM10, NO2, SO2 and CO levels in
Barcelona during the one-month quarantine period. Isaifan (2020) also demonstrated a significant reduc-
tion in NO2 and carbon emissions associated with quarantines in China. Karuppasamy et al. (2020) observed
a 55% reduction in NO2 during the period of quarantine in India. Another study (Otmani et al. 2020) revealed
reductions of 75%, 49% and 96% in PM10, SO2 and NO2, respectively, in Morocco. Moreover, a significant
reduction in air pollution was reported in Iran during the outbreak (Nemati et al. 2020).
The majority of the studies mentioned above employed air quality monitoring stations (terrestrial instru-
ments) to assess air quality. Such instruments are capable of taking point measurements with high temporal
resolution, rendering them suitable for use in situations where ambient change is rapid. Nevertheless, they
lack convenience and practicality, as they necessitate the deployment of a substantial number of instru-
ments to collect data on extensive areas. For instance, Karuppasamy et al. (2020) employed data from over
12,000 stations in a global-scale study. In contrast, airborne or spaceborne remote sensing instruments
are capable of providing information on large areas of the Earth’s surface, albeit at less frequent intervals.
These tools provide valuable, easily accessible and reliable data for studies in a multitude of fields (Avdan
et al. 2021; Kuruca et al. 2021; Matcı and Avdan 2020; Matci et al. 2020; Praticò et al. 2021; Tok and Kaya
2014; Zhe 2018). One such tool is the TROPOspheric Monitoring Instrument (TROPOMI), developed
by the European Space Agency with the objective of monitoring and predicting air quality, ozone, radia-
tion and climate. It is an instrument on the Copernicus Sentinel-5 Precursor satellite that provides high
spatial and temporal resolution data on tropospheric concentrations of ozone (O3), methane (CH4), formalde-
hyde (HCHO), carbon monoxide (CO), nitrogen dioxide (NO2) and sulphur dioxide (SO2) in netCDF
(Network Common Data Form) format since July 10th, 2018. All Sentinel-5P data is freely available from
The Copernicus Data Space Ecosytem in near real-time and offline. 
One Sentinel-5P image encompasses approximately 100 million square kilometres of the Earth’s sur-
face and occupies over 500 megabytes of memory space. This implies that a one-year survey of the entire
Earth utilising Sentinel-5P products as data would necessitate approximately 65,000 images and in excess
of 30 terabytes of storage. Conducting such a survey in the traditional manner, by accessing, downloading
and processing data one at a  time, is a  highly time-consuming, resource-intensive, and error-prone
process. However, these issues can be overcome using Google Earth Engine (GEE), a cloud-based computing
platform designed primarily for the analysis of Earth-scale environmental data. It combines petabytes of
satellite imagery and geospatial datasets and allows users to easily access, process and visualize them (Kumar
and Mutanga 2019). GEE has been applied and its capabilities explored in various fields related to earth
sciences (Jalayer et al. 2023; Nejad et al. 2022; Nghia et al. 2022; Waleed et al. 2023; Xiong et al. 2017). The
impact of the coronavirus pandemic on air pollution parameters such as tropospheric NO2 levels has also
been investigated in this way using Sentinel-5P data by Sannigrahi et al. (2021) and Sharifi and Felegari
(2022). These city-scale studies, which also utilised in-situ data from ground stations, demonstrated improve-
ments in air quality in various cities during the pandemic. 
Although the effects of pandemic lockdowns on NO2 concentrations and distributions have been demon-
strated at various scales, there is no study examining the changes in countries with different levels of development.
In order to conduct such a study, it is necessary to determine the category boundaries according to a devel-
opment index, rather than an administrative criterion. One such index is the Human Development Index
(HDI), a measure of the level of development of countries, which has been calculated by the United Nations
Development Programme (UNDP) for almost every country since 1990. A number of parameters, includ-
ing life expectancy, health, access to education and per capita income, are taken into account in the calculation
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Nuri Erkin Öçer, Dilek Küçük Matcı, Uğur Avdan, Monitoring the impact of the Corona pandemic on nitrogen dioxide …
of the HDI. The HDI has been employed in a multitude of academic contexts, including investigations
into its correlation with health, obesity, CO2 emissions, and the economy (Ataey et al. 2020; Long et al. 2020;
Sarkodie and Adams 2020).
The primary objective of this study is to ascertain the extent to which tropospheric NO₂ concentra-
tions are influenced by pandemic measures implemented by countries with disparate Human Development
Index (HDI) categories. In order to achieve this objective, tropospheric NO2 concentrations for the entire
Earth were obtained using Sentinel-5P data through GEE for the years 2019, 2020 and 2021. The NO₂ lev-
els are then evaluated and interpreted for countries grouped by HDI. Furthermore, the study encompasses
the temporal variation of NO₂ concentrations over the specified period across the globe, geographical con-
tinents, and three neighbouring Southern European countries with disparate HDI categories (Slovenia,
Croatia and Bosnia and Herzegovina).
2 Materials and methods
In this study, tropospheric NO2 concentrations over the study areas between 2019 and 2021 were deter-
mined by utilising Sentinel-5P’s TROPOMI NRTI NO2: Near Real-Time Nitrogen Dioxide data, provided
by the Copernicus Data Space Ecosystem through GEE. The product calculates tropospheric NO2 con-
centrations by subtracting stratospheric contributions from the total columns. The TROPOMI/Sentinel-5P
instrument, with a swath width of 108° (approximately 2,600 km on the ground), provides daily coverage
of over 95% of the Earth’s surface. The spatial resolution of the product is 5.5 km in the satellite flight direc-
tion and 3.5 km in the perpendicular direction at nadir. However, data released prior to August 6th, 2019 had
a resolution up to 7.0 km in the flight direction. Statistical analyses of the comparison between TROPOMI
and ground-based measurements (e.g., ZSL-DOAS SAOZ NO2 data) demonstrate an excellent correlation
(correlation coefficient = 0.94) between the two data sets. Furthermore, the histogram of the differences
exhibits an almost Gaussian shape, with a small negative bias for TROPOMI (Verhoelst et al. 2020). The
methodology in this study, developed entirely on the GEE platform, is schematised in Figure 1 and has
been applied on various large-scale study areas. The methodology comprises three principal stages: data
acquisition, pre-processing and processing. The process commences with the introduction of the study
area to the software. In the study, the boundaries of the study areas are in geospatial vector data (shape-
file) format. The boundaries of the HDI classes were delineated using the borders of each country and the
respective HDI values. Subsequently, Sentinel-5P Level 3 data is accessed from the GEE data archive and
the NRTI NO2 band is selected. Due to the fact that pixels in Level 2 data are defined by latitude and lon-
gitude, it is difficult to combine multiple images in this type of data. Conversely, Level 3 data products are
obtained by resampling the Level 2 ones to regular spatial pixel grids, rendering them suitable for com-
bining. In the following stage, a time series of NRTI NO2 is constructed by collecting the data acquired
within the specified temporal range. The study employs monthly and annual time intervals. The process
concludes with the calculation of the average of the time series, producing a map of the time-averaged NO2
values of the study area.
The present study examines tropospheric NO₂ levels in the following study areas: the Earth, the con-
tinents, countries grouped according to HDI, and the triad of Slovenia, Croatia, and Bosnia and Herzegovina.
Figure 2 provides a visual representation of the study areas. The investigation encompasses almost the entire
Earth (including seas and oceans) with the exception of Antarctica. In the HDI-related part of the study,
countries were examined by dividing them into categories of Very High, High, Medium and Low developed
in accordance with the UNDP Reports catalogue for the year 2019 (the Others category was omitted). The
list of countries, as classified by HDI, is presented in Table 1. 
The study was conducted on a computer equipped with an Intel i9 7900X CPU (Central Processing
Unit), an Nvidia GTX 1080 GPU (Graphics Processing Unit), 128 GB of Random-Access Memory (RAM),
and a 1000 Mbps internet connection speed. In order to calculate the one-year average NO₂ for the largest
study area, that of the Earth, a total of 64,188 Sentinel-5P images were used. The processing time was approx-
imately 60 seconds. 
The limitations of the methodology employed in this study can be considered from two distinct per-
spectives: data resolution and computational complexity. As the method is reliant on TROPOMI data for
the measurement of atmospheric NO2 emissions, the spatial resolution of the data is 3.5 × 7.0 km
2 until
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End product:
Map of monthly or annual
average of NRTI NO
of study area
2
Take average:
calculate the mean average
of time series
Get the time series of NO :2
Time series of imagery
Filter:
imagery that fits
into desired time interval
by date:
Select band:
NRTI NO2
Select data:
Sentinel 5P level 3
Introduce study area:
Region of interest
Filter:
NRTI NOby band: 2
Data ources :
Google earth engine data archive
Figure 1: The workflow of the presented method.
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AFRICA
ASIA
EUROPE
NORTH AMERICA
OCENIA
SOUTH AMERICA
HDI CLASS
VERY HIGH
HIGH
MEDIUM
LOW
OTHER
0 10.0005.000
km
B O S N I A &
H E R Z E G O V I N A
S L O V E N I A
C R O AT I A
SLOVENIA
CROATIA
BOSNIA &
HERZEGOVINA
0 300150
km
a)
b)
c)
Content by: Dilek Küçük Matcı and Nuri Erkin Öçer
Map by: Nuri Erkin Öçer
Source: a) and c) Esri, 2024; b) UNDP, 2019
±
±
Figure 2: The study areas: a) the geographic continents, b) countries grouped according to HDI, c) the triad of Slovenia, Croatia, Bosnia and
Herzegovina.
August 6th, 2019 and 3.5 × 5.0 km2 thereafter. Therefore, it is not possible to capture changes at finer scales.
Nevertheless, these data, which are highly correlated with reliable ground measurements in previous stud-
ies, are still useful for examining relative variability and trend analysis between years and months. 
Furthermore, the algorithm employed in this study is only capable of handling shapefiles of the study
areas up to a specific fineness of resolution, contingent on the size of RAM available. In this study, the algo-
rithm, which was executed on a computer with 128 GB of RAM, was capable of processing shapefiles with
a maximum resolution of 500 metres. For finer, more detailed shapefiles, the available memory was insuf-
ficient.
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Acta geographica Slovenica, 64-3, 2024
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Andorra
Argentina
Australia
Austria
Bahamas
Bahrain
Barbados
Belarus
Belgium
Brunei Darussalam
Bulgaria
Canada
Chile
Costa Rica
Croatia
Cyprus
Czechia
Denmark
Estonia
Finland
France
Georgia
Germany
Greece
Hong Kong, China (SAR)
Hungary
Iceland
Ireland
Israel
Italy
Japan
Kazakhstan
Korea (Republic of)
Kuwait
Latvia
Liechtenstein
Lithuania
Luxembourg
Malaysia
Malta
Mauritius
Montenegro
Netherlands
New Zealand
Norway
Oman
Palau
Panama
Poland
Portugal
Qatar
Romania
Russian Federation
Saudi Arabia
Serbia
Singapore
Slovakia
Slovenia
Spain
Sweden
Switzerland
Turkey
United Arab Emirates
United Kingdom
United States
Uruguay
Albania
Algeria
Antigua and Barbuda
Armenia
Azerbaijan
Belize
Bolivia 
Bosnia and Herzegovina
Botswana
Brazil
China
Colombia
Cuba
Dominica
Dominican Republic
Ecuador
Egypt
Fiji
Gabon
Grenada
Indonesia
Iran (Islamic Republic of)
Jamaica
Jordan
Lebanon
Libya
Maldives
Marshall Islands
Mexico
Moldova (Republic of)
Mongolia
North Macedonia
Palestine, State of
Paraguay
Peru
Philippines
Saint Kitts and Nevis
Saint Lucia
Saint Vincent and the Grenadines
Samoa
Seychelles
South Africa
Sri Lanka
Suriname
Thailand
Tonga
Trinidad and Tobago
Tunisia
Turkmenistan
Ukraine
Uzbekistan
Venezuela (Bolivarian Rep. of)
Viet Nam
Angola
Bangladesh
Bhutan
Cabo Verde
Cambodia
Cameroon
Comoros
Congo
El Salvador
Equatorial Guinea
Eswatini (Kingdom of)
Ghana
Guatemala
Guyana
Honduras
India
Iraq
Kenya
Kiribati
Kyrgyzstan
Lao People's Democratic Rep.
Micronesia (Federated States of)
Morocco
Myanmar
Namibia
Nepal
Nicaragua
Pakistan
Papua New Guinea
Sao Tome and Principe
Solomon Islands
Syrian Arab Republic
Tajikistan
Timor-Leste
Vanuatu
Zambia
Zimbabwe
Afghanistan
Benin
Burkina Faso
Burundi
Central African Republic
Chad
Congo (Dem.Rep. of the)
Côte d'Ivoire
Djibouti
Eritrea
Ethiopia
Gambia
Guinea
Guinea-Bissau
Haiti
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mozambique
Niger
Nigeria
Rwanda
Senegal
Sierra Leone
South Sudan
Sudan
Tanzania (United Republic of)
Togo
Uganda
Yemen
Korea (Dem. People's Rep. of)
Monaco
Nauru
San Marino
Somalia
Tuvalu
Table 1: The countries as grouped according to HDI for the year 2019.
Ve
ry
 H
ig
h
Ve
ry
 H
ig
h
Hi
gh
Hi
gh
M
ed
iu
m
M
ed
iu
m
Lo
w
Ot
he
rs
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3 Results and discussion
The results obtained by the method employed are presented in this section in the form of maps and tables.
The maps illustrate the distributions of the annual averages of tropospheric NO2 column concentrations,
while the tables show the annual and the monthly average values over each study area. The results are pre-
sented in the following order: the Earth, the geographical continents, the countries grouped according to
HDI, and the triad of Slovenia, Croatia, Bosnia and Herzegovina.
3.1 NO2 emission across the Earth
The distribution of the annual averages of the Earth’s tropospheric NO2 column concentrations for the
years 2019, 2020 and 2021, are presented in Figure 3. The maps, each of which is an average of more than
64,000 images (occupying more than 30 terabytes of memory) recorded by the satellite throughout the study
period, reveal the concentrations and distributions of tropospheric NO₂ worldwide before, during and after
the pandemic, respectively. A visual comparison of the data reveals a significant decrease in the average NO₂
emission in the pandemic year (2020). This result is corroborated by Table 2, which presents the annual
averages of tropospheric NO₂ concentrations for the study period across all study areas. For the Earth, the
total NO₂ concentration decreased by 3.4% in 2020 (51.2 μmol/m²) in comparison to 2019 (53.0 μmol/m²),
and then increased by 2.9% in 2021 (52.7 μmol/m²) in comparison to 2020. This represents a net decrease
of 0.6% in the average concentration of NO2 in 2021 in comparison to 2019.
The outcomes are in line with the results of the study of Saha et al. (2022), which compiled worldwide
research on air quality parameters. They reported a significant improvement in global air pollution lev-
els during the quarantine period and indicated the extent to which concentrations of major air pollutants,
such as NO2, SO2, CO and particulate matter, decreased in major countries of the world. Furthermore,
Cooper et al. (2022) employed a method that enabled them to quantify changes in NO₂ concentrations
across more than 200 cities. Their results demonstrated that countries with stringent lockdown policies
exhibited NO₂ concentration levels that were on average approximately 30% lower than in those without.
Furthermore, the study revealed that the sensitivity of atmospheric NO₂ to closures exhibited variability
across countries and emission sectors.
Figure 3: The global distribution of annual averages of NO2 column concentration for the years 2019 (top), 2020 (middle), and 2021 (bottom). p p. 83
Table 2: The annual averages of NO2 column concentrations for the Earth, the continents, the countries grouped according to HDI and the triad of
Slovenia, Croatia, Bosnia and Herzegovina for the years of 2019, 2020 and 2021, and the change between years.
NO2 Column Density (μmol/m
2) Yearly Change (%)
Study Area 2019 2020 2021 2020–2019 2021–2020 2021–2019
The Earth 53.0 51.2 52.7 –3.4 2.9 –0.6
Africa 51.8 51.3 54.0 –1.0 5.3 4.2
Asia 65.5 61.5 65.8 –6.1 7.0 0.5
Europe 73.9 67.5 73.7 –8.7 9.2 –0.3
North America 56.8 54.1 55.8 –4.8 3.1 –1.8
Oceania 52.7 53.2 51.8 0.9 –2.6 –1.7
South America 45.2 46.8 47.4 3.5 1.2 4.8
Very High HDI 61.4 58.3 61.0 –5.1 4.7 –0.6
High HDI 60.2 58.0 61.3 –3.7 5.7 1.9
Medium HDI 57.4 56.3 59.8 –2.0 6.2 4.1
Low HDI 49.6 48.9 52.4 –1.5 7.2 5.6
Slovenia 80.7 76.0 83.9 –5.9 10.5 4.0
Croatia 74.4 69.9 76.6 –6.1 9.6 3.0
Bosnia and Her. 68.9 64.6 71.2 –6.3 10.3 3.4
Th
e C
on
tin
en
ts
HD
I C
las
se
s
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83
 NO2 Column Concentration (μmol/m2)
Scale: 1: 90,000.000
Content By: Dilek Küçük Matcı
and  Nuri Erkin Öçer
Map By: Nuri Erkin Öçer
Source: Copernicus Data
Space Ecosystem, 2024
50 90 180
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The variation in the monthly averages of NO2 emissions for the Earth is presented in Table 3 and Figure 4.
Accordingly, the mean NO2 emissions for each month of 2020 were consistently lower than those for each
month of 2019. Moreover, the results demonstrate that NO2 levels were lower than the pre-pandemic values
even before the countries adopted the pandemic closures and restriction measures, namely before mid-March.
During the course of 2020, which encompassed a series of closures and the implementation of stringent mea-
sures, the tropospheric NO2 concentration exhibited a decrease of 3.4% per month in comparison to the
preceding year. In contrast, prior to the implementation of government measures, namely in the first three
months of 2020, there was a monthly decrease of 4.0% in comparison to the corresponding period of the
previous year. This can be interpreted as a result of individuals voluntarily limiting their movements and spend-
ing more time at home in order to protect themselves from the effects of pandemic. In 2021, following the
relaxation of restrictions, monthly averages increased in all months except January in comparison to 2020.
Table 3: The monthly averages of tropospheric NO2 global density for the period from January 2019 to December 2021.
NO2 column density (μmol/m
2)
Month 2019 2020 2021
January 55.3 53.9 53.7
February 52.3 49.7 50.6
March 49.1 46.8 48.2
April 51.0 48.6 50.2
May 55.3 52.3 54.5
June 58.4 55.4 57.1
July 55.9 54.4 57.0
August 51.6 51.2 53.2
September 48.1 47.4 49.1
October 49.3 48.3 49.6
November 53.8 50.9 52.9
December 55.5 55.3 55.9
–8
–6
–4
–2
0
2
4
6
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2020 2019– 2021 2020–
Month
N
O
C
ol
u
m
n
 C
h
an
ge
 (
%
)
2
Figure 4: The percentage differences between 2020–2019 and 2021–2020 of global monthly averages of tropospheric NO2 concentrations for the same month.
64-3_acta49-1.qxd  25.11.2024  7:23  Page 84
3.2 NO2 emissions on the continents 
The annual averages of NO₂ emissions by continent for the years 2019, 2020 and 2021 are presented in
Table 2. The ranking in 2019, from the highest to the lowest, is as follows: Europe, Asia, North America,
Oceania, Africa and South America. The ranking remains unchanged in 2020, but Oceania regresses one
place and Africa rises one in 2021. In comparison to 2019, NO2 emissions in 2020, the year during which
pandemic restrictions were most strictly applied, decreased by 8.7% in Europe, 6.1% in Asia, 4.8% in North
America, 1.0% in Africa, while they increased by 0.9% in Oceania and 3.5% in South America. In 2021,
NO₂ emissions exhibited an increase of 9.2% in Europe, 7.0% in Asia, 3.1% in North America, 5.3% in
Africa, and 1.2% in South America in comparison to 2020. The only decrease was observed in Oceania,
where emissions decreased by 2.6%. Between 2021 and 2019, NO2 emissions decreased by 0.3% in Europe,
1.8% in North America and 1.7% in Oceania, while it increased 0.5% in Asia, 4.2% in Africa, and 4.8% in
South America. South America was the only continent whose averages exhibited an increase in both years.
Table 4 presents the monthly averages of NO₂ column densities for the continents from January 2019
to December 2021. With regard to Africa, it can be observed that NO2 emissions decreased until August
2020 and then increased continuously until the end of the study period (Figure 5a). For Asia, NO2 emis-
sions exhibited a decrease for all months except December in 2020, followed by an increase for all months
except December in 2021 (Figure 5b). In Europe, NO₂ emissions exhibited a decrease until December 2020,
followed by a continuous increase (Figure 5c). Among all the continents, the greatest change in NO2 lev-
els between consecutive years was observed in Europe, with a 40% increase between February 2020 and
2021. For North America, a downward trend is observed across all of 2020, with the exception of November,
and an upward trend across 2021, with the exception of January, February and November (Figure 5d). In
contrast to the other continents, the tropospheric NO₂ values in Oceania and South America did not decline
until May 2020, in comparison to the values observed in 2019. From that point onwards, the values exhib-
ited a variable trend in Oceania and South America (Figure 5e and 5f). Moreover, the magnitudes of change
were relatively modest in comparison to those observed in other continents.
The results demonstrate that human-induced NO2 emissions have undergone corresponding changes
across continents during the pandemic process, particularly in relation to the extent of their industrialisa-
tion based on fossil fuels. In the study of Cooper et al. (2022), the emission estimates for 2020, which were
made by considering the ten-year period prior to the pandemic, were compared with the new situation
resulting from the actual pandemic for the continents. Accordingly, the largest discrepancy from the pro-
jected values was identified for Europe, corroborating the findings of our investigation.
3.3 NO2 emissions in countries as grouped according to HDI
The annual changes of NO₂ emissions for countries in categories classified according to the HDI are pre-
sented in Table 2. Accordingly, in 2019, the category ranking from high to low is Very High, High, Medium
and Low. The ranking remains unchanged in 2020, but in 2021 the total emissions of the High category
exceed those of the Very High category. In 2020, when pandemic restrictions were the most stringent, there
was a reduction in NO2 emissions across all categories in comparison to 2019. The decline in the Very High
category was 5.1%, in the High category 3.7%, in the Medium category 2.0%, and in the Low category 1.5%.
In 2021, NO₂ emissions exhibited an increase in all categories when compared to 2020. The increase was
4.7% in the Very High category, 5.7% in the High category, 6.2% in the Medium category, and 7.2% in the
Low category. A comparison of the 2021 averages with those of 2019 reveals that total NO₂ emissions
decreased only in the Very High category (0.6%) and increased in all other categories (High: 1.9%, Medium:
4.1%, Low: 5.6%) in 2021. The results of the 2020–2019 period indicate that the reduction in NO2 emis-
sions is more pronounced in regions with a higher development index. Conversely, the results for the period
2021–2020 indicate a greater rate of increase in emissions in regions with a lower HDI. This suggests that
the severity of the closures may be increasing in line with the development level.
Acta geographica Slovenica, 64-3, 2024
85
Figure 5: The monthly percentage differences of NO2 column concentrations of a) Africa, b) Asia, c) Europe, d) North America, e) Oceania, f) South America
between consecutive years, 2020–2019 and 2021–2020. p p. 86
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–40
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0
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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Month
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 (
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 (
%
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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Month
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2020 2019– 2021 2020–
Month
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Ta
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Table 5 presents the monthly averages of NO₂ column densities for countries belonging to different
HDI categories for the period from January 2019 to December 2021. The NO₂ emissions in the Very High
category exhibited a downward trend for the majority of months in 2020, with the exception of September
and December. Conversely, an upward trend was observed for all months in 2021, with the exception of
December (Figure 6a). In this category, the greatest reductions and increases in NO₂ emissions for the study
period were observed in February 2020 and February 2021, respectively. In the High category, NO₂ emis-
sions exhibited a pronounced decline in the initial months of 2020, followed by an upward trend from
September of the same year. This upward trend persisted until December 2021, as illustrated in Figure 6b.
Among all categories, the greatest reduction and increase in emissions for the entire study period were
observed in the High category in January 2020 and January 2021, respectively. In the Medium and Low
HDI categories, a reduction in emissions was observed in 2020 until September. Subsequently, emissions
exhibited an upward trend until the end of 2021 (Figures 6c and 6d).
Following the implementation of measures to combat the epidemic, there was an uninterrupted decline
in emissions over an extended period. However, this trend was reversed in September 2020, with emissions
increasing in all categories except the Very High category. The reversal of this trend commenced in December
2020 for the Very High category, suggesting that the relaxation of lockdowns in countries in this category
lagged behind by a few months.
A substantial majority of studies analysing the impacts of the pandemic on pollutant emissions have
examined the situation at the scale of cities and countries. In contrast, Li et al. (2022), employing a data-
driven approach, analysed regions without limiting them to administrative boundaries. Their findings
indicated that these regions can be divided into three distinct clusters according to their pollution levels.
The findings of the study indicated that the level of restriction measures in the cluster with the highest
emissions was more stringent than in the other clusters, and NO2 emissions in this cluster declined more
than in the others. A comparison of the results of our study with those of this study revealed that a sig-
nificant number of countries in the Very High and High HDI categories corresponded to the cluster designated
as poor (with the highest emissions) in this study.
3.4 NO2 emissions in Slovenia, Croatia, and Bosnia and Herzegovina 
In the last phase of the study, tropospheric NO2 levels were monitored in three neighbouring southern
European countries: Slovenia, Croatia, Bosnia and Herzegovina (Figure 7a). Slovenia (population approx-
imately 2.1 million) and Croatia (population approximately 3.8 million) are classified as belonging to the
Table 5: The monthly averages of NO2 column densities of the HDI categories for the period from January 2019 to December 2021.
NO2 column density (μmol/m
2)
Very High HDI High HDI Medium HDI Low HDI
Month 2019 2020 2021 2019 2020 2021 2019 2020 2021 2019 2020 2021
Jan. 56.9 50.7 53.3 63.0 51.7 59.8 53.7 49.5 52.7 48.8 43.7 48.7
Feb. 55.5 47.6 52.8 59.8 49.3 53.7 53.8 49.9 53.8 48.7 43.6 47.1
Mar. 56.6 51.3 54.7 61.1 54.6 58.2 57.7 52.5 58.5 49.9 45.7 50.0
Apr. 63.6 59.4 62.7 61.0 57.6 60.9 58.8 53.9 58.9 51.3 47.2 50.8
May 70.8 65.1 68.6 62.5 58.9 61.3 61.4 57.4 59.8 52.3 49.5 52.7
June 74.8 70.0 72.2 63.3 60.1 63.1 65.0 61.7 64.6 55.2 52.3 55.7
July 71.8 69.6 72.2 62.0 60.9 64.1 64.8 62.0 66.9 54.6 52.2 56.8
Aug. 66.2 65.5 67.3 61.5 60.4 65.0 61.6 61.4 65.9 51.5 51.4 55.9
Sep. 59.1 59.3 61.3 58.9 61.3 65.1 58.2 61.4 64.9 48.7 51.4 55.3
Oct. 55.3 54.0 57.2 57.6 60.4 62.7 53.8 57.3 60.6 45.8 50.1 53.1
Nov. 53.1 50.2 53.7 56.3 57.3 61.0 50.8 54.0 56.3 44.7 49.4 52.0
Dec. 52.8 56.6 56.3 55.0 63.1 60.6 49.4 54.3 54.1 43.9 49.7 50.5
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–20
–15
–10
–5
0
5
10
15
20
–20
–15
–10
–5
0
5
10
15
20
–20
–15
–10
–5
0
5
10
15
20
–20
–15
–10
–5
0
5
10
15
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2020 2019– 2021 2020–
Month
N
O
C
o
lu
m
n
 C
h
an
ge
 (
%
)
2
a)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2020 2019– 2021 2020–
Month
N
O
C
o
lu
m
n
 C
h
an
ge
 (
%
)
2
b)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2020 2019– 2021 2020–
Month
N
O
C
o
lu
m
n
 C
h
an
ge
 (
%
)
2
c)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2020 2019– 2021 2020–
Month
N
O
C
o
lu
m
n
 C
h
an
ge
 (
%
)
2
d)
Figure 6: The monthly percentage differences of NO2 column concentrations of a) Very High b) High c) Medium d) Low developed countries between
consecutive years, 2020–2019 and 2021–2020.
Very High HDI category, while Bosnia and Herzegovina (population approximately 3.2 million) classified
as belonging to the High HDI category. The distributions of annual average tropospheric NO₂ levels in
these three countries for the pre-pandemic year (2019), pandemic year (2020) and post-pandemic year
(2021) are presented in Figures 7b, 7c and 7d, respectively. The annual average of NO₂ over Bosnia and
Herzegovina is consistently lower than the other two countries, in line with the values presented in Table 2.
The impact of pandemic measures on NO₂ emissions is evident when comparing Figures 7b and 7c for
all three countries. Table 2 also indicates that there was a decrease of 5.9% for Slovenia, 6.1% for Croatia
and 6.3% for Bosnia and Herzegovina in 2020 compared to 2019. In 2021, the increases in NO2 emissions
following the removal of measures are revealed by comparing Figures 7c and 7d. The annual averages
increased by 10.5% for Slovenia, 9.6% for Croatia and 10.3% for Bosnia and Herzegovina in 2021 compared
Figure 7: a) Slovenia-Croatia-Bosnia and Herzegovina triad on the map and their annual average of tropospheric NO2 column densities for the year
b) 2019, c) 2020, d) 2021. p p. 90
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Nuri Erkin Öçer, Dilek Küçük Matcı, Uğur Avdan, Monitoring the impact of the Corona pandemic on nitrogen dioxide …
90
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64-3_acta49-1.qxd  25.11.2024  7:23  Page 90
to 2020, as indicated by Table 2. A comparison of the 2021 averages with those of 2019 reveals that there
were net increases of 4.0% in Slovenia, 3.0% in Croatia and 3.4% in Bosnia and Herzegovina.
The monthly averages of the NO2 column densities for these countries for the months from January
2019 to December 2021 are presented in Table 6 and the monthly percentage differences of the NO2 col-
umn concentrations of each country between the consecutive years 2020–2019 and 2021–2020 are presented
in Figure 8. According to information reflected in reports by organisations such as the Inter-university
Consortium for Political and Social Research (ICPSR) and the Organisation for Economic Cooperation
and Development (OECD), in response to the initial cases of coronavirus that emerged in early March
2020 and the subsequent rapid spread of the virus, the governments of all three countries implemented
a series of measures with the aim of halting the spread from mid-March onwards. These measures includ-
ed the closure of educational institutions, limitations on public gatherings, the closure of cafes, restaurants
and non-essential shops, and the imposition of travel restrictions. The implementation of these measures,
which significantly restrict human mobility (Brezina et al. 2021), has resulted in a downward trend in human-
induced NO2 emissions in the atmosphere during the pandemic year, as illustrated in Figure 8. Following
the control of the outbreak, the measures were eased in Bosnia and Herzegovina at the end of April and
in Slovenia and Croatia from mid-May. Consequently, the reduction in emissions has slowed down.
Nevertheless, following the relaxation of restrictions, the number of infected individuals in Slovenia and
Croatia increased exponentially from October, leading to a further tightening of measures in November.
Consequently, NO2 emissions for these two countries fell rapidly again in November. A comparison of 2020
and 2019 November emission values confirms this result. In Bosnia and Herzegovina, the number of infect-
ed cases continued to increase linearly, and there was no further tightening of measures. Consequently,
the decline in NO2 emissions in Bosnia and Herzegovina has been considerably less pronounced than in
the other two countries. Following the relaxation of restrictions in 2021, there was a notable increase in
human mobility across all three countries, which led to a corresponding rise in NO2 emissions.
Acta geographica Slovenica, 64-3, 2024
91
Table 6: The monthly averages of NO2 column densities of Slovenia, Croatia, Bosnia and Herzegovina for the period from January 2019 to December 2021.
NO2 column density (μmol/m
2)
2019 2020 2021
Slovenia Croatia Bosnia and Slovenia Croatia Bosnia and Slovenia Croatia Bosnia and
Herzegovina Herzegovina Herzegovina
Jan 76.5 67.4 61.0 82.0 62.1 52.0 88.5 74.4 66.4
Feb 89.9 71.8 67.7 74.6 65.4 57.9 77.9 62.0 54.1
Mar 81.9 74.0 66.6 72.2 67.9 63.6 85.4 75.1 70.2
Apr 89.9 85.2 77.1 69.6 69.3 68.0 85.8 81.9 74.9
May 91.1 85.2 76.6 77.3 73.3 68.7 78.5 74.7 71.7
Jun 83.3 83.4 80.6 75.9 74.4 71.0 80.7 80.1 76.6
Jul 81.3 81.3 77.6 77.4 75.7 73.0 78.1 77.8 76.0
Aug 78.3 78.3 75.4 78.0 76.9 72.7 77.3 76.6 73.7
Sep 79.0 74.6 70.5 75.5 73.4 68.8 79.5 76.8 72.2
Oct 72.8 67.2 63.7 71.3 68.4 63.1 85.5 81.0 78.3
Nov 75.1 63.1 55.3 60.9 55.7 53.4 91.8 81.3 72.3
Dec 69.6 60.8 54.8 96.7 75.7 62.9 98.1 77.1 68.3
Figure 8: The monthly percentage differences of NO2 column concentrations of a) Slovenia b) Croatia c) Bosnia and Herzegovina between consecutive
years, 2020–2019 and 2021–2020. p p. 92
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Nuri Erkin Öçer, Dilek Küçük Matcı, Uğur Avdan, Monitoring the impact of the Corona pandemic on nitrogen dioxide …
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2020 2019– 2021 2020–
Month
–25
–15
–5
0
5
15
25
35
45
55
a) Slovenia
N
O
C
o
lu
m
n
 C
h
an
ge
 (
%
)
2
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2020 2019– 2021 2020–
Month
–25
–15
–5
0
5
15
25
35
45
55
b) Croatia
N
O
C
o
lu
m
n
 C
h
an
ge
 (
%
)
2
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2020 2019– 2021 2020–
Month
–25
–15
–5
0
5
15
25
35
45
55
c) Bosnia and Herzegovina
N
O
C
o
lu
m
n
 C
h
an
ge
 (
%
)
2
64-3_acta49-1.qxd  25.11.2024  7:23  Page 92
4 Conclusions
In this study, TROPOMI data were accessed and processed through Google Earth Engine (GEE) in order
to monitor and evaluate the effects of the COVID-19 pandemic on tropospheric NO2 concentrations and
distributions at various supranational scales. The study examines the levels of NO₂ in countries grouped
according to the Human Development Index (HDI), as well as the temporal variation of NO₂ across the
globe, continents and the triad of Slovenia, Croatia, and Bosnia and Herzegovina. 
The results of the study indicate a notable decline in NO₂ levels across all study areas during the pan-
demic period in comparison to the pre-pandemic period. The decline commenced even before the
implementation of restrictions and closures by governments. In contrast to other studies, the results of
our study indicate that the observed reductions in emissions in the period before the implementation of
the restrictions, i.e. in the first three months of 2020, cannot be attributed solely to the adoption of the
measures. Instead, it appears that individuals are also adopting behaviours to protect themselves from the
disease, such as avoiding social contact and limiting their own mobility in the community, and that emis-
sions are starting to fall as a result.
Following a prolonged period of decline, emissions began to increase across all HDI categories and
on most continents (with the exception of Oceania and South America) in response to the relaxation and
removal of the measures and associated increased human mobility. The increase trend commenced three
months earlier in the High, Medium and Low HDI categories than in the Very High category, indicating
an earlier relaxation of lockdowns in these countries. Another noteworthy finding of the study is that dur-
ing the period of restrictions, the decline in NO2 emissions increases as the development index increases.
Furthermore, the results for the period following the lifting of restrictions indicate that the rate of increase
in emissions is greater in areas with a lower HDI. These two observations were interpreted as the severi-
ty of closures increases as the level of development increases. 
In the final phase of the study, tropospheric NO2 levels were monitored in Slovenia, Croatia, and Bosnia
and Herzegovina before, during and after the pandemic. Although the pre-pandemic level of emissions
in Bosnia and Herzegovina, which is in the High HDI category, was significantly lower than in the other
two Very High HDI countries, it demonstrated similar trends to the pandemic emissions in the other two
countries. However, variations in outcomes were also observed in relation to the timing of the implementation
of measures.
The results demonstrate that a study requiring the access of thousands of data and the use of terabytes
of memory can be successfully conducted through a cloud-based software such as GEE. The processes that
would have required considerable resources, time, and labour, and would have been error-prone without
GEE, were executed efficiently through this platform in the course of the research.
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