COVID-19 andEnvironment

Impacts of a Global Pandemic
Edited by

Sachiko Kodera and Essam Rashed
Printed Edition of the Special Issue Published in IJERPH

www.mdpi.com/journal/ijerph

COVID-19 and Environment: Impacts
of a Global Pandemic

COVID-19 and Environment: Impacts
of a Global Pandemic

Editors
Sachiko Kodera
Essam Rashed

MDPI ‚ Basel ‚ Beijing ‚ Wuhan ‚ Barcelona ‚ Belgrade ‚ Manchester ‚ Tokyo ‚ Cluj ‚ Tianjin

Editors
Sachiko Kodera

Essam Rashed

Department of Electrical and

Graduate School of

Mechanical Engineering

Information Science

Nagoya Institute of

University of Hyogo

Technology

Kobe

Nagoya-shi

Japan

Japan

Editorial Office
MDPI
St. Alban-Anlage 664052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journalInternational Journal of Environmental Research and Public Health (ISSN 1660-4601) (available at: www.
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Contents
Preface to ”COVID-19 and Environment: Impacts of a Global Pandemic” . . . . . . . . . . . . vii
Xin Dong, Shili Yang and Chunxiao Zhang
Air Pollution Increased the Demand for Gym Sports under COVID-19: Evidence from Beijing,
China
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 12614, doi:10.3390/ijerph191912614 .

1

Chunli Wei, Qingqing Li, Ziyi Lian, Yijun Luo, Shiqing Song and Hong Chen
Variation in Public Trust, Perceived Societal Fairness, and Well-Being before and after
COVID-19 Onset—Evidence from the China Family Panel Studies
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 12365, doi:10.3390/ijerph191912365 . 17
Ahmed Karam, Abdelrahman E. E. Eltoukhy, Ibrahim Abdelfadeel Shaban and El-Awady
Attia
A Review of COVID-19-Related Literature on Freight Transport: Impacts, Mitigation Strategies,
Recovery Measures, and Future Research Directions
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 12287, doi:10.3390/ijerph191912287 . 31
Sonia Venancio-Guzmán, Alejandro Ivan Aguirre-Salado, Carlos Soubervielle-Montalvo
and José del Carmen Jiménez-Hernández
Assessing the Nationwide COVID-19 Risk in Mexico through the Lens of Comorbidity by an
XGBoost-Based Logistic Regression Model
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 11992, doi:10.3390/ijerph191911992 . 59
Zhong Chen and Dongping Shi
The Atmospheric Environment Effects of the COVID-19 Pandemic: A Metrological Study
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 11111, doi:10.3390/ijerph191711111 . 79
Fang Wang, Jin-Ming Wu, Yi-Chieh Lin, Te-Wei Ho, Hui-Lin Lin and Hsi-Yu Yu et al.
Coronavirus Disease Pandemic Effect on Medical-Seeking Behaviors Even in One
Resource-Competent Community: A Case Controlled Study
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 10822, doi:10.3390/ijerph191710822 . 95
Ka-Huen Yip, Yuk-Chiu Yip and Wai-King Tsui
The Lived Experiences of Women without COVID-19 in Breastfeeding Their Infants during thePandemic: A Descriptive Phenomenological Study
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 9511, doi:10.3390/ijerph19159511 . . . 105
Nobuyuki Wakui, Mayumi Kikuchi, Risa Ebizuka, Takahiro Yanagiya, Chikako Togawa andRaini Matsuoka et al.
Survey of Pharmacists’ Knowledge, Attitudes, and Practices (KAP) concerning COVID-19
Infection Control after Being Involved in Vaccine Preparation: A Cross-Sectional Study
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 9035, doi:10.3390/ijerph19159035 . . . 123
Biruk G. Mesfin, Daniel(Jian) Sun and Bo Peng
Impact of COVID-19 on Urban Mobility and Parking Demand Distribution: A Global Review
with Case Study in Melbourne, Australia
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 7665, doi:10.3390/ijerph19137665 . . . 137v

Nobuyuki Wakui, Nanae Noguchi, Kotoha Ichikawa, Chikako Togawa, Raini Matsuoka andYukiko Yoshizawa et al.
Psychological and Physical Changes Caused by COVID-19 Pandemic in Elementary and JuniorHigh School Teachers: A Cross-Sectional Study
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 7568, doi:10.3390/ijerph19137568 . . . 153
Marta Baselga, Juan J. Alba and Alberto J. Schuhmacher
The Control of Metabolic CO2 in Public Transport as a Strategy to Reduce the Transmission of
Respiratory Infectious Diseases
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 6605, doi:10.3390/ijerph19116605 . . . 167
Jeadran Malagón-Rojas, Daniela Mendez-Molano, Julia Almentero, Yesith G. Toloza-Pérez,
Eliana L. Parra-Barrera and Claudia P. Gómez-Rendón
Environmental Effects of the COVID-19 Pandemic: The Experience of Bogotá, 2020
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 6350, doi:10.3390/ijerph19106350 . . . 187
Zivile Pranskuniene, Rugile Grisiute, Andrius Pranskunas and Jurga Bernatoniene
Ethnopharmacology for Skin Diseases and Cosmetics during the COVID-19 Pandemic in
Lithuania
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 4054, doi:10.3390/ijerph19074054 . . . 199
Nawfel Mosbahi, Jean-Philippe Pezy, Jean-Claude Dauvin and Lassad Neifar
COVID-19 Pandemic Lockdown: An Excellent Opportunity to Study the Effects of Trawling
Disturbance on Macrobenthic Fauna in the Shallow Waters of the Gulf of Gabès (Tunisia, Central
Mediterranean Sea)
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 1282, doi:10.3390/ijerph19031282 . . . 211
Priscilla Gomes da Silva, José Gonçalves, Ariana Isabel Brito Lopes, Nury Alves Esteves,
Gustavo Emanuel Enes Bamba and Maria São José Nascimento et al.
Evidence of Air and Surface Contamination with SARS-CoV-2 in a Major Hospital in Portugal
Reprinted from: Int. J. Environ. Res. Public Health 2022, 19, 525, doi:10.3390/ijerph19010525 . . . 229
Yuyang Zhao and Fernando Bacao
How Does Gender Moderate Customer Intention of Shopping via Live-Streaming Apps during
the COVID-19 Pandemic Lockdown Period?
Reprinted from: Int. J. Environ. Res. Public Health 2021, 18, 13004, doi:10.3390/ijerph182413004 . 243
Heesup Han, Chen Che and Sanghyeop Lee
Facilitators and Reducers of Korean Travelers’ Avoidance/Hesitation Behaviors toward China
in the Case of COVID-19
Reprinted from: Int. J. Environ. Res. Public Health 2021, 18, 12345, doi:10.3390/ijerph182312345 . 267

viPreface to ”COVID-19 and Environment: Impacts of a
Global Pandemic”
COVID-19 outbreak was first reported in China in 2019 and spread worldwide in early 2020.
With the spread of COVID-19 and the corresponding negative impact on different life aspects, it
has become important to understand the ways to deal with the pandemic as a part of daily life.
Numerous studies on COVID-19 have investigated meteorological factors, clinical features, andpublic health interventions that affect infection and morbidity. However, the problems still exists
and more investigation is required. Challenges are the limitation of accurate/complete data, theeffect of restrictions, and variabilities of medical service quality in different countries. In addition,
circumstances are dynamically changing due to policies, vaccines, and the advent of new variants allover the world. The investigation of correlation between COVID-19 and environmental factors basedon long-term analysis and data curation would lead to better sustainable management of healthcare
resources and government policies.
This Special Issue aims to collect articles of high academic standards investigating the links
between the COVID-19 pandemic and environmental factors. We have received several manuscripts,
from which 17 papers have passed the peer-review process and are presented here as a reprint.
Sachiko Kodera and Essam Rashed
Editorsvii

International Journal ofEnvironmental Researchand Public Health
ArticleAir Pollution Increased the Demand for Gym Sports under
COVID-19: Evidence from Beijing, China
Xin Dong 1 , Shili Yang 2 and Chunxiao Zhang 1, *1
2*School of Information Engineering, China University of Geosciences, Beijing 100083, China
Beijing Meteorological Observation Centre, Beijing Meteorological Bureau, Beijing 100089, China
Correspondence: zcx@cugb.edu.cn; Tel.: +86-186-1821-9549

Abstract: Air pollution may change people’s gym sports behavior. To test this claim, first, we used
big data crawler technology and ordinary least square (OLS) models to investigate the effect of
air pollution on people’ gym visits in Beijing, China, especially under the COVID-19 pandemic of
2019–2020, and the results showed that a one-standard-deviation increase in PM2.5 concentration(fine particulate matter with diameters equal to or smaller than 2.5 µm) derived from the land use
regression model (LUR) was positively associated with a 0.119 and a 0.171 standard-deviation increasein gym visits without or with consideration of the COVID-19 variable, respectively. Second, using
spatial autocorrelation analysis and a series of spatial econometric models, we provided consistentevidence that the gym industry of Beijing had a strong spatial dependence, and PM2.5 and its spatial
spillover effect had a positive impact on the demand for gym sports. Such a phenomenon offers us a
new perspective that gym sports can be developed into an essential activity for the public due to thisavoidance behavior regarding COVID-19 virus contact and pollution exposure.
Keywords: air pollution; PM2.5 concentration; gym sports; spatial econometric model; COVID-19
Citation: Dong, X.; Yang, S.; Zhang,
C. Air Pollution Increased theDemand for Gym Sports under

1. Introduction

COVID-19: Evidence from Beijing,

Many Chinese megacities are extremely polluted, and the problem of serious haze has
aroused widespread concern among the public [1,2]. Air pollution not only significantly
causes great harm to public health [3,4], it also affects people’s daily life [5,6]. Air pollution
may inhibit outdoor physical activity, as people have engaged in avoidance behavior [7,8].
Some studies have demonstrated that air pollution has negative effects on the frequency oftravel to indoor amenities such as restaurants, shopping areas and movie theatres [9,10]
and outdoor venues such as public parks [11].
Notably, research related to indoor air pollution concentration in gyms is dominatedby evidence from some developed countries, which generally show that people may inhale
more pollutants while exercising in indoor gyms [12–14]. In cities of the developing world,
however, the role of gym sports is under-researched. The existing literature, mainly focusing
on the impact of air pollution on the frequency of gym physical activity, remains rare.
To further investigate how gym sports can be affected by air pollution and actual
external environmental factors, this study focuses on the effect of air pollution under the
COVID-19 pandemic. As is known, the COVID-19 pandemic is currently one of the greatest
challenges to human life and health and economic development [15]. Beginning in 2020,
starting in China, the coronavirus rapidly spread around the world due to globalization,
which caused great harm to travel behavior [16]. The strictest social travel restrictions
imposed by the Chinese government in response to the first outbreak of the pandemic
included restricting all outdoor activities and closing all recreational facilities, including
public indoor sports places [17,18]. Although lockdown restrictions started to lift in many
cities around the world, regular pandemic prevention and control measures have remained
unchanged in China, such as transport suspension [19] and 14 days of quarantine whentraveling [20,21], referring to the post-pandemic era.

China. Int. J. Environ. Res. Public
Health 2022, 19, 12614. https://doi.org/10.3390/ijerph191912614
Academic Editors: Sachiko Kodera
and Essam A. Rashed
Received: 30 August 2022
Accepted: 27 September 2022
Published: 2 October 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1

Int. J. Environ. Res. Public Health 2022, 19, 12614

2 of 18

lift in many cities around the world, regular pandemic prevention and control measures
have remained unchanged in China, such as transport suspension [19] and 14 days ofInt. J. Environ. Res. Public Health 2022, 19, 12614
quarantine when traveling [20,21], referring to the post-pandemic era.
Recently, a significant number of studies have shown that the pandemic has reduced
the frequency
people traveling
toof
both
outdooractivities[22,23] andindoorRecently,ofa significant
numberstudies
havephysicalshown thatthe pandemic
has reduced
gym
sports[24].
Inaddition,somescholarshave
foundchangesinairqualitybecauseofthe frequency of people traveling to both outdoor physical activities [22,23] and indoor
thetravelrestrictions
duringthe
COVID-19
pandemic[25,26].
Gym
exercisebehavior
gym sports [24]. In addition, some scholars have found changes in air quality becauseisof
affectedby airpollution,during
and wasaffected
duringthe COVID-19
pandemic[27].isthe travel
restrictions
theespecially
COVID-19
pandemic[25,26].
Gym exercise
behavior
Nevertheless,is still anda lackof especiallystudies thathave analyzed
theCOVID-19
impact ofpandemic
air pollution
affected by airthere
pollution,wasaffected
during the[27].
on
gym sports behavior
under
theof
impactof thatthe pandemic
in spatiotemporal
Nevertheless,there is stilla lackstudieshave analyzed
the impact ofdimensions.
air pollution
The purposeof this study
to impact
fill the gap
aforementioned
by examining thiscase inon gymsports behavior
underistheof thepandemic in spatiotemporaldimensions.
Beijing,
China.
Beijing,asthecapitalof
China,
with
its
vast
territory,
dense
population
The purpose of this study is to fill the gap aforementioned by examining this case inandcomplexterrain,makes
pollutiona serious
threatBeijingwasthe secondBeijing,
China.
Beijing,as theaircapitalof China,
with its
vast [28].
territory,
dense
population
andlargestcityterrain,in China
withairgym
exercisersin 2020,
after[28].
Shanghai
paper, wecomplexmakes
pollutiona serious
threatBeijing[29].
was Inthethissecond-largest
useddata crawlingtechniques
panel after
regression
modelsexaminethe effectofcity bigin China
with gym
exercisersandin 2020,
Shanghai[29].toIn
this paper,
we usedPM2.5
concentration
conductedandby landregression(LUR)tomodels[30,31]on gymbig data
crawling techniques
paneluse
regression
modelsexaminethe effectof visits
PM2.5
concentration
conducted
land use
regression(LUR) models[30,31]on gymvisits during
during
COVID-19from 1 byJanuary2019
to 31 December
2020. We
divided2019–2020
into
COVID-19
from
1 January
to 31 December
2020.levels
We divided
2019–2020
into five
five
COVID-19
periods
due to2019
air pollution
prevention
publishedby the Chinese
COVID-19 periods
duetheto airpollution period
prevention
the Chinese
governgovernment, including
Pre-COVID(the levels
periodpublished
before theby
pandemic,spanning
ment,
includingthetoPre-COVID(the period beforethepandemic,spanningfromfrom
1 January
2019
24 January period2020), COVID-Lockperiod(theperiod during
the out1
January2019
to24
January2020),
COVID-Lockperiod(theperiodduringtheoutbreakbreak of the pandemic and the blockade of non-essential travel, spanning from 25 January
pandemic
andCOVID-Recover-I
the blockade of non-essential
travel,during
spanningfrom 25prevention
January to
toof25theFebruary2020),
period (the period
pandemic
25
February2020),
COVID-Recover-I
period(theperiodduringpandemic
prevention
andand control downgrade, spanning from 26 January to 10 June 2020), Xinfadi-COVID pecontrol
downgrade,spanningfrom
26
Januaryto10June2020),
Xinfadi-COVID
period(theriod (the period of another outbreak of the pandemic in Xinfadi, spanning from 11 June
periodofanotheroutbreakofthepandemicin
Xinfadi,spanning
from
11
June
to
6
Augustto 6 August 2020) and COVID-Recover-II period (the period when the pandemic outbreak
2020)
and
COVID-Recover-II(theperiodwhen 2020),
the pandemic
outbreak
alert
was
alert
was
lifted,spanning from 7period
August
to 31
Decemberabbreviatedas P1–P5(Figlifted,
spanningfrom7
Augustto31
December2020),
abbreviatedasP1–P5(Figure1)[32].
ure 1) [32]. Gym comments crawled from the platform “Meituan.com” were used to deGym
comments
crawledfromtheplatform“Meituan.com”
were
used
to
describe
Beijing’s
scribe Beijing’s gym visits in P1–P5 of the COVID-19 pandemic.
gym visits in P1–P5 of the COVID-19 pandemic.

Figure 1. Timeline of five periods of the COVID-19 pandemic of Beijing, China.
Figure 1. Timeline of five periods of the COVID-19 pandemic of Beijing, China

In this study, we aimed to not only access the effect of air pollution on people’s gym
In this study, we aimed to not only access the effect of air pollution on people’s gym
visits under five different COVID-19 periods, but also to evaluate the spatial spillover effectvisits under five different COVID-19 periods, but also to evaluate the spatial spillover efof air pollution on the entire development of the gym industry from the perspective of afect
of air pollution
the entirethis
developmentof the gym
industry two
fromresearch
the perspective
space–timeanalysis.onTherefore,
study was designed
to examinequestions:of(1)a How
space–timeanalysis.
Therefore,
this
study
was
designedtoexaminetwo researchdoes air pollution affect people’s demand for gym sports under COVID-19?
(2) Isquestions:(1) Howdoeseffectair pollution
affectof people’s
demandforsports?
gym sports under
there a spatial
spilloveron the impact
air pollution
on gymCOVID-19?
(2) Is there
spatialspillovereffect about
on thethe
impactof ofairairpollution
Our findings
fromathespace–timeanalysis
impactpollutiononongym
gym
sports?
sports have contributed to the literature on air pollution and avoidance behavior. At theOurfindings
from thetospace–timeanalysis
theof air
gym
same
time,
it is necessary
pay attention
to theabout
impactofimpact
COVID-19
onpollution
people’s on
behavior
sportshave
contributedtotheliteratureonairpollution
andavoidance
behavior.
Attheregarding gym sports, which has been well documented as a new behavioral moderator
same
time,
it
is
necessary
topayattentiontotheimpactof
COVID-19
onpeople’sbehavior
regarding avoiding outdoor exposure of the virus when exercising.
regarding gym sports, which has been well documented as a new behavioral moderator
2. Materials
and Methods
regarding
avoiding
outdoor exposure of the virus when exercising.
2.1. Gym Data
This study aimed to test daily gym activity changes in Beijing that were associatedwith air pollution and climate conditions under the COVID-19 period. The study areaand spatial distribution of 2452 gyms are shown in Figure 2. Because a lack of direct
evidence on consumer mobility limited our ability to characterize local gym consumption

22. Materials and Methods
2.1. Gym Data
This study aimed to test daily gym activity changes in Beijing that were associatedInt. J. Environ. Res. Public Health 2022, 19,
12614
with
air pollution and climate conditions under the COVID-19 period. The study area andspatial distribution of 2452 gyms are shown in Figure 2. Because a lack of direct evidence
on consumer mobility limited our ability to characterize local gym consumption conditions, to a certain
degree,we usedconsumption
recordeddata, which
was usedconditions,to a certain
degree,we online
used online
consumption
recordeddata, which
wasto
represent
the actualof gym visits
accordingto previous[9,33].
Therefore,
used
to represent
thenumber
actual numberof gymvisits according
to studies
previous
studies[9,33].
we used big
technology
and crawled
thecrawled
data from
Chineseleading
local
Therefore,
wedatausedcrawler
big data
crawler technology
andthethedata
from theChineselifestyleinformation
andtrading
platform“Meituan.com”
todescribe
Beijing
residents’leading local lifestyle information and trading platform “Meituan.com” to describe Beijing
gym activities.
The informationof each gymofineach
Beijing
included
its precise
location
inforresidents’
gym activities.
The information
gym
in Beijing
included
its precise
mation informationand its attributes
includingreviews,
ratings
andratings
their perconlocationand itsdata,
attributes
data,users’
includingusers’
reviews,
andcapita
their persumptionfrom 1 January
to 312019
December
2020.
capitaconsumption
from 1 2019
January
to 31 December
2020.

Figure 2. Study area. The spatial distribution of 2452 gyms of 16 administrative districts are indiFigure 2. Study area. The spatial distribution of 2452 gyms of 16 administrative districts are indicatedcated in Beijing, China.
in Beijing, China.

Finally,ourouranalysis
analysisdata
dataincluded
included2452
2452gyms
gymswith
with144,904
144,904comments.
comments.The
Thecommentscomments
Finally,data
weremergedaccordingtothetimelineoffivedifferent
COVID-19
periods
todescribe
data were merged according to the timeline of five different COVID-19 periods to describe
Beijing’s
gym
visitsduring
COVID-19.
This
study
implicitly
assumedthattheprobability
Beijing’s gym visits during COVID-19. This study implicitly assumed that the probabilityof writing a review was not relevant to the air pollution level by using the count of reviews
as a proxy for the count of gym visits.
2.2. PM2.5 Concentration Data
Hourly PM2.5 data in Beijing from 1 January 2019 to 31 December 2020 were obtained
from the Beijing Municipal Environmental Monitoring Center [34]. Since 2013, there havebeen 35 environmental monitoring stations (EMSs) in Beijing (Figure 2). The botanical3

Int. J. Environ. Res. Public Health 2022, 19, 12614

garden EMS was eliminated from the twelve urban EMSs in this study, and 34 EMSs wereused to obtain hourly PM2.5 concentration in this study based on the Ambient Air Quality
Standards (GB 3095-2012) [35]. We averaged the PM2.5 data based on the five different
periods of the COVID-19 pandemic for the subsequent air pollution modeling.
2.3. Land Use Regression (LUR) Model
We used the LUR model to replace traditional spatial interpolation methods to obtainaverage PM2.5 concentrations of P1–P5 more accurately [30,31]. In our study, we divided
the process into two steps as follows, including preparing predictor variables and building
the LUR model.
2.3.1. Predictor Variables
We used average PM2.5 concentrations of P1–P5 and independent variables to build
LUR models, including land use, digital elevation model (DEM), meteorological variables,
road length, population density, remote sensing PM2.5 data, normalized difference vegetation index (NDVI), aerosol optical depth (AOD) and points of interest (POI). These ninedriving factors were selected on the basis of their significant impact on PM2.5 concentrations [36,37]. Overall, this study considered a total of 29 subcategories, which included
103 independent variables within the nine major independent variable categories.
As shown in Table 1, land use data, traffic information and POI data, the three major
vector predictor variables, are included in the LUR model. Land use data were derived
from Esri 2020 Land Cover with a spatial resolution of 10 m (https://livingatlas.arcgis.com/
landcover/, accessed on 5 May 2021). Land use type was divided into cropland, woodland,grassland, water bodies, construction land, and unused land. The specific indicator of theland factor was the ratio of land area to buffer area (area of circular buffer).
Table 1. Description of the vector predictor variables in the LUR models of P1–P5.
No.

Predictor Variables

Abbreviations

Unit

Buffer Size

Land use
1
2
3
4
5
6

Cultivated land
ForestGrass land
Waterbody
Built-up area
Unused land

Cul_xx
For_xx
Gra_xx
Wat_xx
Bui_xx
Unu_xx

m2
m2
m2
m2
m2
m2

100, 300, 500, 1000, 3000, 5000
100, 300, 500, 1000, 3000, 5000
100, 300, 500, 1000, 3000, 5000
100, 300, 500, 1000, 3000, 5000
100, 300, 500, 1000, 3000, 5000
100, 300, 500, 1000, 3000, 5000

Traffic information
7
8
9
10

Trunk road length
Primary road length
Secondary road length
Railroad length

Tru_xx
Pri_xx
Sec_xx
Rai_xxmm
m
m

100, 300, 500, 1000, 3000, 5000
100, 300, 500, 1000, 3000, 5000
100, 300, 500, 1000, 3000, 5000
100, 300, 500, 1000, 3000, 5000

POI information
11
12
13
14
15
16
17
18

Bus station number
Gas station number
Polluted enterprise number
Chinese restaurant number
Distance to the nearest bus station
Distance to the nearest gas station
Distance to the nearest polluted enterprise
Distance to the nearest Chinese restaurant

POI1_xx
POI2_xx
POI3_xx
POI4_xx
D_bus
D_gas
D_pol
D_res

m
m
m
m

100, 300, 500, 1000, 3000, 5000, 7000
100, 300, 500, 1000, 3000, 5000, 7000
100, 300, 500, 1000, 3000, 5000, 7000
100, 300, 500, 1000, 3000, 5000, 7000
NA
NA
NA
NA

Road data were obtained from OpenStreetMap (https://www.openstreetmap.org,
accessed on 5 May 2021). The specific road factor indicator was the ratio of road length tobuffer area.
Points of interest (POI) data were derived from Amap, by applying API based on category and keyword semantics (http://lbs.amap.com/api/webservice/guide/api/search/,
accessed on 25 May 2021). Different POI categories in different buffer sizes represent different emission information with regard to PM2.5. In this study, we used four types of POI as

4

Int. J. Environ. Res. Public Health 2022, 19, 12614

pollutant emission sources, including bus stations, gas stations, polluted enterprises andChinese restaurants. To reflect the impact of local emission sources and possible regionaltransmission, the buffer sizes for POI in this study were set from 100 to 7000 m [38].
As shown in Table 2, population data, geographical information, vegetation index,
remote sensing data, meteorological data and aerosol optical depth data, the six majorraster predictor variables, are included in the LUR model.
Table 2. Description of the raster predictor variables in the LUR models of P1–P5.
No.

Predictor Variables

Abbreviations

Unit

Original Spatial Resolution

Population
1

Population densityPoppeople/m2

1 km

Geographic information
2

Elevation

DEM

m

30 m

Vegetation index
3

NDVI

NDVI

-

1 km

Remote sensing data
4

CHAP_PM2.5

CHAP

µg/m3

1 km

Boundary layer height2 m temperature
Total precipitation
Surface pressure
10 m u-component of wind
10 m v-component of wind

BLH
T2M
TP
SP
U10m
V10m

m
K
mm
106 Pam/s
m/s

0.125◦
0.125◦
0.125◦
0.125◦
0.125◦
0.125◦

Optical_Depth_047

AOD_047-

1 km

Meteorological data
5
6
7
8
9
10
Aerosol optical depth
11

The population density data used in this study were based on Gridded Populationof the World (GPW) from the Columbia University Socioeconomic Data and Application
Center (CU2020) as raster data, with a resolution of 1 × 1 km [39].
Elevation data were obtained from the Advanced Spaceborne Thermal Emission and
Reflection Radiometer Global Digital Elevation Model, version2 (ASTER GDEM v2) on the
USGS Earth Explorer site, with a spatial resolution of 30 m (https://yceo.yale.edu/astergdem-global-elevation-data, accessed on 8 May 2021).
Normalized difference vegetation index (NDVI) data were provided by the MOD13A2
Version 6 product distributed by NASA EOSDIS Land Processes DAAC, which was a
MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid with a spatial resolutionof 1 km (https://lpdaac.usgs.gov/products/mod13a2v006/, accessed on 12 May 2021).
Data were calculated into mean data for the corresponding period of P1–P5 [40].
Remote sensing PM2.5 data were provided by the MODIS/Terra + Aqua Level 3 (L3) Yearly0.01 degree gridded ground-level PM2.5 products in Eastern China (ECHAP_PM2.5_Y1K) from2019 to 2020 (https://zenodo.org/record/4660858#.YU2JKux0IdU, accessed on 14 May2021) [41,42].
Meteorological data were extracted from the ERA–Interim reanalysis dataset of European Centre for Medium-Range Weather Forecasts (ECMWF), with a spatial resolutionof 0.125◦ × 0.125◦ . The data downloaded were daily means of 2-meter temperature data,
U wind speed data, V wind speed data, boundary layer height data, surface pressure data
and total precipitation data, which needed to be summed into mean data of P1–P5 by
Python, prepared for later modeling [43].
Aerosol Optical Depth (AOD) data were obtained from the MCD19A2 Version 6 data
product distributed by NASA EOSDIS Land Processes DAAC, which was a Moderate
Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle
Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD)
gridded Level 2 product produced daily at 1 km pixel resolution. (https://lpdaac.usgs.
gov/products/mcd19a2v006/, accessed on 15 May 2021) [44].

5Int. J. Environ. Res. Public Health 2022, 19, 12614

2.3.2. Building the LUR Model
A five-step backward method was adopted for fitting the LUR model [45]. For all
available potential predictor variables, bivariate correlation analysis with average PM2.5
concentrations of P1–P5 were conducted first. Second, the predictor variables were sorted
by adjusted R2 . Third, other variables (Pearson correlation coefficient R ≥ 0.7) with highrelevance to the highest ranked variables in each subcategory were removed. Fourth, all
the remaining predictor variables and PM2.5 concentrations were entered into the stepwise
linear regression model to obtain the multiple linear regression equation. Finally, thesignificance level (p value < 5%) and variance inflation factor (VIF < 4) of each predictor
variable were checked to confirm the variables’ significance levels and to ensure no issuesof multicollinearity. The leave-one cross-validation (LOOCV) and 10-fold CV method werechosen to evaluate the predictive capacity of the model. From the cross-validation, the R2
and root mean squared error (RMSE) were used to evaluate and compare the predictivityof the model [46,47].
2.4.
Statistical Analysis
Int. J. Environ. Res. Public Health 2022, 19,12614

7 of 18

Figure 3 shows the overall theoretical framework used for assessing the effect of PM2.5
concentration on gym visits under COVID-19.

Figure3.3.Theoretical
Theoreticalframework
frameworkmodel
modelofofthis
thisstudy.
study.
Figure2.4.1. Building Ordinary Least Squares (OLS) Model
2.4.1. Building Ordinary Least Squares (OLS) ModelFirst, we used an OLS panel regression model to test how the count of the gym visitsFirst, we used an OLS panel regression model to test how the count of the gym visitsvaries as a function of air pollution and climate conditions.
varies as a function of air pollution and climate conditions.
NU Mit==𝛼α0++𝛼α1 PM2.5
PM2.5it ++ α𝛼2 W
it + α3 Xit + Tt + γi + ε it ,
𝑁𝑈𝑀
𝑖𝑡
0
1
𝑖𝑡
2 𝑊𝑖𝑡 + 𝛼3 𝑋𝑖𝑡 + 𝑇𝑡 + 𝛾𝑖 + 𝜀𝑖𝑡 ,

(1)
(1)

where 𝑁𝑈𝑀𝑖𝑡 and PM2.5𝑖𝑡 represent the comments, reviews, and PM2.5 concentrationsof gym i on COVID-19 period t. 𝑊𝑖𝑡 represents weather conditions, including gym mean
6
temperature and precipitation of P1–P5. 𝑋𝑖𝑡 is used as a control variable, including forresidents’ gym ratings and per capita consumptions. 𝑇𝑡 and 𝛾𝑖 are used to control forInt. J. Environ. Res. Public Health 2022, 19, 12614

where NU Mit and PM2.5it represent the comments, reviews, and PM2.5 concentrationsof gym i on COVID-19 period t. Wit represents weather conditions, including gym mean
temperature and precipitation of P1–P5. Xit is used as a control variable, including forresidents’ gym ratings and per capita consumptions. Tt and γi are used to control for
COVID-19 wave-fixed effects and gym area-fixed effects. ε it is the random error term. The
standard errors are clustered by individual. To avoid collinearity with the PM2.5 data
generated by the LUR model, the average temperature and precipitation data of P1–P5
used in Equation (1) are based on other meteorological data obtained from 18 standard
meteorological stations at the Beijing Meteorological Informational Center, including hourly
temperature and precipitation.
Second, we defined the variable COVID-19 to denote the number of confirmed cases
in P1–P5 waves into Equation (1) to generate Equation (2).
NU Mit = α0 + α1 COVID-19t + α2 PM2.5it + α3 Wit + α4 Xit + Tt + γi + ε it ,(2)

where COVID-19t represents the cumulative number of confirmed cases of COVID-19 in
P1–P5. Coefficient α2 reflects the effect of PM2.5 concentration on gym visits under the
background of the COVID-19 pandemic, which is expected to be consistent with coefficient
α1 in Equation (1). The meanings of other characters are as the same as the above model.
Third, a robustness check was used to replace the PM2.5 variable with PM2.5 dataobtained from the Kriging interpolation method of the MEP PM2.5 monitoring sites andthat from the United States Embassy and Consulates to verify the association of actual
PM2.5 concentration with gym visits in P1–P5 of the COVID-19 pandemic [48].
2.4.2. Building Spatial Econometric Models
To begin, we used Moran’s Index to examine whether there was a spatial autocorrelation relationship or not between gym visits in P1–P5 of the COVID-19 pandemic [49] asEquation (3).
∑ijN wij ( xi − x ) x j − xN0(3)Moran s I = N
2∑ij wij∑iN=1 ( xi − x )with i 6= j, where xi is the interest variable in gym i, x is the variable mean value, N
is the number of observations, and wij is the spatial weight. The Moran index varies
between −1 and 1. A value close to 1 indicates the presence of clusters, and a value
close to −1 indicates spatial dispersion in the data. If the value is 0, then there is no
spatial autocorrelation.
If the spatial factor was ignored or classified as a random disturbance term, the results
obtained by the OLS modeling would have errors. Therefore, when analyzing the impactof PM2.5 on gym visits, spatial econometric models need to be considered [50].
Then, spatial factors were taken into account in the spatial lag model (SLM), whichemphasized the spatial spillover effect of the local gym visits to the number of exercisers inother nearby gyms. The SLM model is expressed formally as Equation (4):n

NU Mit = α + ρ ∑ wij NU M jt + β 1 COVID-19t + β 2 PM2.5it + β 3 Wit + Tt + γi + ε it

(4)

j =1

where ρ is the influence degree of the explained variable with spatial lag on the explained
variable, wij represents the weight matrix, β is the degree of influence of each explanatory
variable (COVID-19, PM2.5 and weather situations) on gym visits, and the meanings ofother characters are the same as the above models.
Furthermore, the spatial error model (SEM) measured the error impact of local influential factors on the gym visits themselves and the impact of these influential factors onother gyms in Beijing. The SEM model is expressed formally as Equation (5):NU Mit = α + β 1 COVID-19t + β 2 PM2.5it + β 3 Wit + Tt + γi + ε it

7(5)

Int. J. Environ. Res. Public Health 2022, 19, 12614

hin
ε it = λ ∑ wij ε jt + µit , µit ∼ N 0, δ2 I
j =1

where λ represents the spatial error correlation coefficient, µit is the error term that satisfies
the OLS assumption, and the meanings of other characters are the same as the above models.
Lastly, the spatial Durbin model (SDM) reflects the degree of agglomeration of gym
visits in a single gym, the level of agglomeration of gym visits in adjacent gyms, and theimpact of the overall gym visits on the development of the gym industry in Beijing. The
SDM model is expressed formally as Equation (6)NUMit = α + ρ ∑nj=1 wij NU M jt + β 1 COVID-19t + β 2 PM2.5it + β 3 Wit +γ wij COVID-19t + γ wij PM2.5t + γ wij Wt + Tt + γi + ε it(6)

where wij COVID-19t , wij PM2.5t and wij Wt represent the spatial lag COVID-19, PM2.5 andweather situations (temperature and precipitation) of the average observation value of theadjacent gyms. γ is the spatial correlation coefficient, and the meanings of other characters
are the same as the above models.
3. Results
3.1. PM2.5 Concentration Estimates from the LUR Model
The results of the five LUR model simulations were strongly correlated with the independent in situ PM2.5 values based on the leave-one cross-validation (LOOCV) and 10-fold
CV results on the pandemic scale of P1–P5 (Table 3). After the final regression models
were obtained, regular 1 × 1 km grids were generated and predicted PM2.5 concentrations
were calculated at the grid points. Then, Kriging interpolation was conducted to generate
PM2.5 concentration distribution simulation maps of Beijing in P1–P5 (Figure 4). Finally,
weextracted the corresponding PM2.5 concentration values of all gyms in P1–P5 as10PM2.5
Int. J. Environ. Res. Public Health 2022, 19, 12614
of 18
exposure concentrations in P1–P5 of the COVID-19 pandemic, preparing for the follow-up
study of the effect of air pollution on gym visits under COVID-19.

FigureFigure 4.
4. Spatial
Spatial distribution
distribution ofof simulation
simulation results
results ofof PM2.5
PM2.5 concentrations
concentrations inin Beijing
Beijing in
in P1–P5.
P1–P5.

3.2. The Impact of PM2.5 Concentration on Gym Visits under COVID-19
As shown in Table 4, we used a series of OLS models to present specifications that
include progressively more controls
from Model 1 to 3. Model 1 only controls for time8
fixed effects of five different COVID-19 waves, adding in area-fixed effects in Model 2,
gym attributes control variables in Model 3, and finally the level and squared terms of

Int. J. Environ. Res. Public Health 2022, 19, 12614

Table 3. Summary of the final LUR models for PM2.5 in P1–P5.
Model
Pre-COVID
PM2.5 (µg/m3 )COVID-Lock
PM2.5 (µg/m3 )VariableIntercept

Coefficient

Std. Error

T Valuep (>|t|)VIF

Global Statistics
Adjusted R2 = 0.68; LOOCV R2 = 0.68
RMSE = 3.10 µg/m3 ;10-fold CV R2 = 0.68;
RMSE = 3.72 µg/m3 .

2.34
0.079

5.552
0.014

0.421
7.192

0.676
0.000

NA
1.039

Cul_3000

4.174 × 10−7

1.363 × 10−7

3.063

0.004

1.039

Intercept
CHAP

5.868
2.197

8.578
0.265

0.684
8.274

0.499
0.000

NA
1.002

Cul_300

4.23 × 10−4

9.5 × 10−5

3.063

0.000

1.002

7.039
4.297
3.468
3.577
−2.545

0.000
0.000
0.002
0.001
0.017

NA
1.048
1.053
1.017
1.033

Adjusted R2 = 0.64; LOOCV R2 = 0.64
RMSE = 1.95 µg/m3 ;10-fold CV R2 = 0.64; RMSE = 2.34 µg/m3 .

0.741
0.000
0.000
0.000
0.003
0.014

NA
1.674
2.854
1.017
3.94
1.101

Adjusted R2 = 0.73; LOOCV R2 = 0.73
RMSE = 3.36 µg/m3 ;10-fold CV R2 = 0.73; RMSE = 3.97 µg/m3 .

0.057
0.000
0.045

NA
1.044
1.044

Adjusted R2 = 0.54; LOOCV R2 = 0.54
RMSE = 2.89 µg/m3 ;10-fold CV R2 = 0.542; RMSE = 3.61 µg/m3

CHAP

COVID-RecoverI PM2.5(µg/m3 )Intercept
CHAP
Tru_500
Cul_300
Gra_100

Xinfadi-COVID
PM2.5 (µg/m3 )Intercept
AOD_047
POI3_7000
Pri_500
POI2_7000
Wat_100

COVID-RecoverII PM2.5(µg/m3 )Intercept
AOD_047
Cul_1000

20.9250.398
1198.099
1.17 × 10−4
−0.001
2.032
0.066
0.215
−2640.034
−0.19
−49.1

8.407
0.079
3.7 × 10−6

2.973
0.093
345.514
3.3 × 10−5
2.52 × 10−4

6.088
0.013
0.046
445.498
0.058
1.72 × 10−4
4.257
0.014
8.407

0.334
5.24
4.723
−5.926−3.274−2.615
1.975
5.505
4.257

Adjusted R2 = 0.73; LOOCV R2 = 0.73
RMSE = 5.12 µg/m3 ;10-fold CV R2 = 0.73; RMSE = 6.26 µg/m3 .

3.2. The Impact of PM2.5 Concentration on Gym Visits under COVID-19
As shown in Table 4, we used a series of OLS models to present specifications that
include progressively more controls from Model 1 to 3. Model 1 only controls for timefixed effects of five different COVID-19 waves, adding in area-fixed effects in Model 2,
gym attributes control variables in Model 3, and finally the level and squared terms of
PM2.5 in Model 4, which were used to explore the nonlinearity of the relationship between
PM2.5 concentration and gym visits. The standardized coefficients of PM2.5 from Model
1 to 3 steadily decreased (0.150, 0.148 and 0.119), showing that PM2.5 was significantly
positively associated with gym visits. Furthermore, precipitation was significantly negatively associated with gym visits with an effect that was 18.05 in Model 4 after controlling
for time-fixed effects, area-fixed effects and gym attribute variables. Model 4 exhibits anonlinear positive relationship between PM2.5 concentration and gym visits; thereby, ourstudy mainly focused on the linear effect of PM2.5 on gym visits under COVID-19.
Table 4. Estimated associations between PM2.5 and gym visits from the OLS models.
Dependent Variable:
Gym Comments

Model 1Model 2Model 3Model 40.150 ***
(0.000)4.509(0.153)
1.486 ***
(0.001)
11.16 *
(0.075)0.148 ***
(0.000)−20.01 ***(0.000)−0.0322(0.897)36.81 ***
(0.000)0.119 ***
(0.000)−18.05 ***(0.000)0.0259(0.916)0.327(0.939)0.366 ***
(0.008)−0.00165 *(0.065)−19.17 ***(0.000)-0.0561(0.823)30.61 ***
(0.000)COVID-19 Wave FEs
Gym FEs
Gym Controls

Yes
No
No

Yes
Yes
No

Yes
Yes
Yes

Yes
Yes
Yes

Observations
Participant number

12,2602452

12,2602452

12,2602452

12,2602452

PM2.5(PM2.5)2
Precipitation
Temperature
Constant

Note: We suppressed the coefficients on control variables to conserve space. The 95% confidence intervals are
based on heteroscedastic robust standard errors. *** p < 0.001; * p < 0.01.

9Int. J. Environ. Res. Public Health 2022, 19, 12614

Moreover, the positive impact of PM2.5 grew from 0.119 in Model 3 of Table 4 to 0.171 inModel 1 of Table 5, which meant that such a positive effect increased to a 0.051 standard
deviation when introducing the COVID-19 variable into Model 3. Notably, we found that
COVID-19 was negatively associated with gym visits (−0.0272 in Model 1 of Table 5), which
could be explained by Chinese pandemic control measures and people’s high awareness of
pandemic prevention under the COVID-19 pandemic.
Table 5. Estimated associations between PM2.5 and gym visits in P1–P5.
Dependent Variable:
Gym Comments

Model 1Model 2Model 3−0.0272 ***(0.000)0.171 ***
(0.000)−19.14 ***(0.000)0.116(0.626)−2.985(0.479)−0.0246 ***(0.000)0.00305(0.952)−17.14 ***(0.000)0.0712(0.771)4.568(0.307)−0.0246 ***(0.000)2.341 ***
(0.000)−17.04 ***(0.000)0.0707(0.773)−94.63 ***(0.000)Gym FEs
COVID-19 Wave FEs
Gym Controls

YES
YES
YES

YES
YES
YES

YES
YES
YES

Observations
Participant Number

12,2602452

12,2602452

12,2602452

COVID-19
PM2.5
PM2.5 Kriging
PM2.5 US Embassy
Precipitation
Temperature
Constant

Note: We included time-fixed effects of five different COVID-19 waves, area-fixed effects and gym attributes
control variables in all Models. In Model 2 and 3, we replaced the PM2.5 variable with PM2.5 data obtained
from the Kriging interpolation method of the MEP PM2.5 monitoring sites and that from the US Embassy andConsulates, as mentioned in Materials and Methods. The 95% confidence intervals are based on heteroscedastic
robust standard errors. *** p < 0.001.

In our robustness check, PM2.5 from the Kriging interpolation method in Model 2 ofTable 5 was not statistically significantly associated with gym visits. However, the positive
impact of PM2.5 from the US Embassy and Consulates grew to almost 14 times of that inModel 3 (2.341 versus 0.171). This might be due to the fact that there is only one PM2.5
monitoring site in the US Embassy and Consulates of Beijing, leading to an overestimationof the positive impact of pollution on gym visits. The correlation between any two of thethree PM2.5 indicators was as high as 0.96. We found that the coefficient of precipitation
was significant and negative.
When considering the spatial autocorrelation, the z-value and p value of the overall
Moran’s Index for the gym visits showed that at the 1% significant level (Table 6), the number of gym exercisers had a significantly positive spatial autocorrelation among different
gyms in Beijing, indicating that there was an obvious agglomeration phenomenon among
the gym crowds.
Because of the spatial autocorrelation, spatial econometric models needed to be taken
into account, which included the spatial lag model (SLM), spatial error model (SEM) andthe spatial Durbin model (SDM).
As shown in Table 7, PM2.5 concentrations were positively associated with gym visits
in the OLS model, SLM model and SEM model after controlling for both fixed effects, withcoefficients of 0.171, 0.205 and 0.197, respectively, which effectively illustrated that PM2.5
had a positive effect on gym visits under COVID-19. Notably, the standardized coefficientsof COVID-19 from the OLS model, SLM model, SEM model and SDM model were similar

10Int. J. Environ. Res. Public Health 2022, 19, 12614(−0.027, −0,027, −0.028 and −0.024), which indicated that the COVID-19 variable itself
inhibited people’s gym sports behavior.
Table 6. Moran’s Index of gym comments in P1–P5.
Dependent Variable:
Gym Comments under P1–P5Izp Value

Pre-COVID
COVID-Lock
COVID-Recover-I
Xinfadi-COVID
COVID-Recover-II

0.181 ***
0.062 ***
0.100 ***
0.102 ***
0.122 ***

6.137
2.159
3.394
3.490
4.229

0.000
0.015
0.000
0.000
0.000

Note: *** p < 0.001.

Table 7. Four regression model results in P1–P5.
Dependent Variable:
Gym Comments under P1–P5

OLS

SLM

SEM

SDM

−0.027 ***(0.000)0.171 ***
(0.000)−19.14 ***(0.000)0.116(0.626)-−0.027 ***(0.000)0.205 **
(0.017)−14.128(0.223)0.164(0.671)0.078 ***
(0.000)-−0.028 ***(0.000)0.197 **
(0.025)−13.897(0.253)0.196(0.614)0.078 ***
(0.000)−0.024 ***(0.008)0.110(0.233)5.397(0.757)0.347(0.376)
0.076 ***
(0.000)Gym FEs
COVID-19 Wave FEs

YES
YES

YES
YES

YES
YES

YES
YES

Observations
Participant Number

12,2602452

12,2602452

12,2602452

12,2602452

COVID-19
PM2.5
Precipitation
TemperatureρλNote: *** p < 0.001; ** p < 0.05.

In addition, the spatial autocorrelation coefficients ρ of both the SLM model andthe SDM model and the spatial autoregressive coefficient λ of the error term in the SEM
model were all significantly positive (0.078, 0.076 and 0.078), indicating that gyms withlarge gym visits could increase the number of exercisers of surrounding gyms and drive
the development of the gym industry of Beijing, which was consistent with the previous
analysis of the Moran’s Index.
Compared with the SLM model and SEM model, we adopted the SDM model to carryout the following analysis due to LR test rejecting the null hypothesis, which showed that
the SDM model was more reliable and stable. In order to analyze the spatial effect of each
influencing factor more concretely, we decomposed the SDM model to obtain the direct
effect (the influence of PM2.5 on local gyms), indirect effect (the impact of PM2.5 on otheradjacent gyms), and total effect (the impact of PM2.5 on all gyms of Beijing). The specificresults are listed in Table 8.
As shown in Table 8, the direct effect of PM2.5 was not significant, while the indirect
and total effects of PM2.5 were both significantly positive. It was proven that every 1%
increase in PM2.5 promoted an increase in gym visits at adjacent gyms by 0.394%, and anincrease in gym visits in all of Beijing by 0.512%. This phenomenon could be explained
because the air pollution had a spatial heterogeneity, that was to say, people with exposure
to higher pollution concentrations would refuse to go outside for exercise, while people
with relatively lower exposure to pollution nearby were more willing to travel to gyms.
This phenomenon needs to be discussed in future studies.

11Int. J. Environ. Res. Public Health 2022, 19, 12614

Table 8. Direct effect, indirect effect and total effect of the SDM model.
Dependent Variable:
Gym Comments under P1–P5

DirectIndirect

Total−0.024 ***(0.006)0.118(0.210)4.025(0.810)0.367(0.328)−0.003(0.788)0.394 ***
(0.003)−45.799 **(0.028)−1.412 *(0.065)−0.027 ***(0.001)0.512 ***
(0.000)−41.773 ***(0.007)-1.045(0.215)

Gym FEs
COVID-19 Wave FEs

YES
YES

YES
YES

YES
YES

Observations
Participant Number

12,2602452

12,2602452

12,2602452

COVID-19
PM2.5
Precipitation
Temperature

Note: *** p < 0.001; ** p < 0.05; * p < 0.01.

Furthermore, the direct effect and the total effect of COVID-19 on fitness were significantly negative, while the indirect effect was not significant. In reality, there was a negative
impact on gym fitness for all people when the pandemic was severe enough.
4. Discussion
4.1. Discussion of Main Findings
Given the high level of air pollution in Beijing, China, its air pollution dynamics
offered the opportunity to test how the demand for gym sports for this consumer city
was affected by pollution in the context of the intertwined impact of air pollution and the
COVID-19 pandemic.
Our study chose Beijing as the case to investigate how air pollution influenced people’s
demand for gym sports, focusing on the effect of PM2.5 on the frequency of traveling togyms during the COVID-19 pandemic. Our OLS model results showed that PM2.5 waspositively associated with gym visits, which was opposite of previous research studies
showing that air pollution was negatively associated with both outdoor physical activity [51,52] and indoor leisure places such as restaurants and shopping areas [9,10]. Sucha positive impact was larger when considering the influence of the COVID-19 pandemic.
One reason to explain this result was that people could not wait to exercise and participate
in recreational activities after the strict travel mandates of the COVID-19 pandemic [16,18].
Therefore, gym indoor physical activity gave them a chance to ignore the harmful effects
of both air pollution and COVID-19 virus contact when exercising. We also observed that
COVID-19 reduced the demand for gym sports, which was consistent with our expected
results [24]. Whether the accelerating effect of the COVID-19 pandemic on the positive
impact of air pollution on gym sports could trade off its own negative impact on gym
sports or not still needs more research and exploration. Future research should explore theinterweaving effects of the climate and social environment on people’s sports behavior.
Furthermore, in the section about measuring PM2.5 concentrations in the P1–P5
periods under COVID-19, five LUR models were constructed to accurately evaluate thelong-term exposure of gym crowds while traveling to the gym. Seasonality (i.e., spring,summer, autumn and winter) was not included as a variable in the analysis of this section
because collinearity existed between season and ambient temperature after we controlled
for seasonality in this study. In the following robust check, we replaced the PM2.5 datafrom the LUR models with that obtained through the Kriging interpolation method of the
MEP PM2.5 monitoring sites and that from the US Embassy and Consulates [48]. Whentaken together, the reported positive effects of air pollution on gym visits derived from OLS
models in our study may be pronounced and solid.
Lastly, our study employed spatial econometric models, including SLM, SEM andSDM to explore the spatial pattern of the gym visits in Beijing. We concluded that there
12

Int. J. Environ. Res. Public Health 2022, 19, 12614

was an obvious agglomeration phenomenon among the gym crowds, in other words, theincrease in gym visits at local gyms would lead to an increase in gym visits at other nearby
gyms, thereby making it possible to facilitate rapid development of the entire gym industryof Beijing. Furthermore, we used the SDM model to obtain the direct effect, indirect effectand total effect to verify the spatial spillover influence of PM2.5 on gym visits in Beijing
under COVID-19. The results showed that PM2.5 had a positive spatial spillover effecton gyms visits of nearby gyms and on the all gyms in Beijing. COVID-19 was negatively
associated with gyms visits of all gyms, which was consistent with the above OLS models.
4.2. Implications for Pollution Health and Gym Sports Research
To the best of our knowledge, the present study provides evidence of the relationshipbetween air pollution and gym sports under COVID-19. Gyms, as a combination of sports
and indoor leisure consumption activity, are becoming relatively safe and comfortable
places for people to exercise and avoid pollution exposure. Furthermore, gym exercise
played an important role in improving people’s fitness and enriching their daily life under
the rigorous pandemic prevention and control measures. This phenomenon offers us a newperspective that promoting gym use can produce considerable health benefits mainly fromless outdoor pollution and from COVID-19 virus exposure, as well as from increased levelsof physical activity.
Constructing more public indoor fitness facilities can be a good option to promote
the vigorous development of mass sports. However, the local government and companies
should consider the unintended impacts that constructing and operating new gyms may
have on local air pollution, for example emissions from construction activities [53], onsite
energy generators or increased demand for electricity from local power plants [54]. Furthermore, previous research has shown that people are more willing to pay for greenspace
under air pollution [55]. It is essential to ensure that enough greenspace is provided inindoor fitness facilities to prevent each individual from pollution exposure and to create
a comfortable and fresh exercise environment, which are beneficial for human mental
health [56].
Taking into account the situation of the COVID-19 pandemic and its prevention, thegovernment also needs to guarantee enough personal exercise space and avoid dangerous
problems caused by big occupancy on the basis of increasing indoor gym infrastructure.
For the issue of charging for private or public gyms, the government should improve thefree or low-cost opening subsidy policy for public sports facilities and promote the openingof facilities to all groups of people.
Therefore, the local government should encourage gym sports and promote the benefits of indoor fitness after the comprehensive consideration of the impact of various
influencing factors on people’s indoor fitness, especially under the COVID-19 pandemic.
As a result, more and more citizens are aware of the importance of physical activity under
the intertwined influences of both the pandemic and air pollution, which will continuously
improve the participation rate of national fitness in the future.
4.3. Research Limitations and Future Research Agenda
Our study has important limitations. First, given the high level of air pollution in
Beijing, China, our data come from 2452 gyms in Beijing, which may lead to possible
selection bias, and we need to be careful in generalizing this bias to other contexts. Further
research in other cities and countries is needed to cross-validate the external validity of
our quantitative findings. Second, we consider PM2.5 as the main source of pollutants inthis study, ignoring other urban pollutants, such as PM10 , NO2 and SO2 [57]. Third, weused PM2.5 generated from LUR models under five COVID-19 periods to represent thepollution exposure concentration of gym crowds. However, this lacks measurements ofthe specific time of their gym visits and their personalized pollution exposure risk fromdifferent commutes to the gym, which are expected to be resolved in follow-up research.
Finally, it is unclear whether exercise in a gym produce similar health benefits as outdoor

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sports. We need to consider multifaceted environmental attributes to possibly exert thehealth effects at different spatial scales of gyms, which requires further research.
5. Conclusions
Using big gym datasets of Beijing in 2019–2020, we documented that air pollution had
a positive impact on residents’ gym visits (proxied by gym reviews on “Meituan.com”)
under COVID-19. Such a positive impact was larger when considering the influence of the
COVID-19 pandemic. From the perspective of space analysis, PM2.5 had a positive spatial
spillover effect on the development of the whole gym industry in Beijing under COVID-19.
Overall, this study offers us a new perspective that gym sports can be developed
into an essential activity for the public due to the avoidance of COVID-19 virus contact
and pollution exposure. Furthermore, this study may provide useful information for anumber of relevant stakeholders and policymakers and may be informative for city andgym infrastructure planning that aims to promote better public health outcomes.
In the future, we need to consider the health hazards caused by large gatherings of
gym crowds and make some reasonable suggestions about gym management, for example,
making sure there are enough greenhouses in gyms and increasing gym facilities on thebasis of causing as little air pollution as possible and ensuring enough personal exercise
space under a pandemic prevention environment. In addition, due to the lack of direct
evidence on people’s movement, we had to use online consumption data to replace theoriginal activity track data. In follow-up research, we need to measure personalized
exposure pollution, such as different levels of air pollution exposure by different commutes
to gyms and real-time indoor pollution concentration of indoor gyms.
Author Contributions: Conceptualization, X.D. and C.Z.; methodology, X.D.; software, X.D.; validation, X.D., S.Y. and C.Z.; formal analysis, X.D.; investigation, X.D.; resources, S.Y. and C.Z.; data
curation, X.D. and S.Y.; writing—original draft preparation, X.D.; writing—review and editing, X.D.,
S.Y. and C.Z.; visualization, X.D.; supervision, S.Y. and C.Z.; project administration, C.Z.; funding
acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.
Funding: This study was funded by National Natural Science Foundation of China (grant no. 42075044).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data that support the findings of this study are available from thecorresponding author upon reasonable request.
Acknowledgments: We thank Beijing Meteorological Informational Center for help with meteorological data from Beijing Meteorological Monitoring Stations.
Conflicts of Interest: The authors declare no conflict of interest.

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16International Journal ofEnvironmental Researchand Public Health
ArticleVariation in Public Trust, Perceived Societal Fairness, andWell-Being before and after COVID-19 Onset—Evidence fromthe China Family Panel Studies
Chunli Wei 1,2 , Qingqing Li 3 , Ziyi Lian 4 , Yijun Luo 4 , Shiqing Song 5 and Hong Chen 4,6,7, *
1
2
3
4
5
6
7

*

Citation: Wei, C.; Li, Q.; Lian, Z.; Luo,Y.; Song, S.; Chen, H. Variation in
Public Trust, Perceived Societal
Fairness, and Well-Being before andafter COVID-19 Onset—Evidence
from the China Family Panel Studies.

Center for Studies of Education and Psychology of Ethnic Minorities in Southwest China, Southwest University,
Chongqing 400715, China
Psychological Development Guidance Center, Guangxi University, Nanning 530004, China
School of Psychology, Central China Normal University, Wuhan 430079, China
Faculty of Psychology, Southwest University, Chongqing 400715, China
School of Psychology, Shaanxi Normal University, Xi’an 710062, China
Key Laboratory of Cognition and Personality of Ministry of Education, Southwest University,
Chongqing 400715, China
Chongqing Key Research Bases in Humanities and Social Sciences, Chongqing 400715, China
Correspondence: chenhg@swu.edu.cn; Tel.: +86-23-6836-7975

Abstract: The sudden onset of the COVID-19 pandemic had a significant impact on all aspects of people’s lives, including their attitudes toward society and psychological well-being. This study aimed
to analyze the variation in public trust, perceived societal fairness, and well-being before and after
the outbreak of the coronavirus disease 2019 (COVID-19). This study used two-wave longitudinal
data of 15,487 residents (2018, T1; 2020, T2) derived from the Chinese Family Panel Studies (CFPS).
A repeated measures analysis of variance showed that (a) public trust, perceived societal fairness,and subjective well-being significantly improved and (b) depression significantly increased. Linear
regression analysis showed that education and socioeconomic status had a significant predictive effecton public trust, perceived societal fairness, and depression; socioeconomic status had a significant
predictive effect on subjective well-being. This study provides evidence and direction for current
social governance, namely, policy implementation and pandemic response.

Int. J. Environ. Res. Public Health 2022,
19, 12365. https://doi.org/10.3390/

Keywords: COVID-19; public trust; perceived societal fairness; mental healthijerph191912365
Academic Editors: Sachiko Kodera
and Essam A. Rashed
Received: 20 July 2022
Accepted: 20 September 2022
Published: 28 September 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
The sudden onset of the COVID-19 pandemic has had a major impact on economic
activities and social functioning. Several pandemic prevention and control measures havebeen implemented such as halting certain work operations, enforcing home quarantine, as
well as travel restrictions. However, these measures have had a significant impact on all
aspects of people’s lives, including their attitudes toward society and psychological wellbeing [1–3]. Public trust and perceived societal fairness form the foundation of attitudes
toward society and play a pivotal role in mental health, human survival, and development [4,5]. Depression and subjective well-being are a direct reflection of an individual’s
mental health, especially during the COVID-19 crisis. Therefore, public trust, perceived
societal fairness, depression, and subjective well-being have been described to function asa social “barometer”, which provides tools and indicators for analyzing and measuring thetrend of social development, contributing significantly to policy implementation, exploringsocial operation mechanisms, grasping social thought dynamics, and promoting long-termsocial governance [6].
To assess the variation in public trust, perceived societal fairness, and well-being
before and after the COVID-19 onset, this study used two-wave longitudinal data of
15,487 residents (2018, T1; 2020, T2) derived from the Chinese Family Panel Studies (CFPS).

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Comparing the responses of those who completed the CFPS from June 2018 to March 2019,(prior to the pandemic) to those who completed the CFPS from July 2020 to December
2020 offered the setting for a natural experiment to compare how participants’ perceptionsof the country, the government, and their lives changed after COVID-19 began. Most
relevant studies conducted in China have been cross-sectional, and thus do not highlightthe changes in public trust, perceived societal fairness, and well-being [3,7–10]. This study
aimed to explore the changes in public trust, perceived societal fairness, and well-being,
and verify the predictive role of education and socioeconomic status during the COVID-19
crisis. Furthermore, this study provides a basis and direction for current social governance,such as the implementation of policies and laws, as well as pandemic response.
1.1. Public Trust and Perceived Societal Fairness
Trust is a psychological state of willingness to take risks based on positive expectationsof external intentions or behaviors [11]. The COVID-19 pandemic has highlighted thevital role that trust plays in producing and maintaining compliance with public healthinstructions during times of crisis [12]. It has emphasized the several varieties of trustthat influence behavior when public health is at risk. People are expected to trust publichealth professionals to identify hazards to the public’s health with accuracy and to suggest
appropriate mitigation measures. They are expected to trust officials to faithfully andrapidly implement those policies. Specifically, citizens and communities need to trust
and adhere to advice from government officials and doctors. Recent studies indicate thattrust in government and public health guidelines have a close relationship [13–16]. Local
government officials may communicate information to ensure that citizens are aware of
new public health regulations [12]. Furthermore, during the pandemic, because the effort
to increase public awareness has the effect of reducing negative emotions and engaging in
rescue activities, interpersonal trust is an invisible force that reduces social dangers [17].
In order to deal with COVID-19, many people are required to work with strangers. These
acts come at a cost to the individual (e.g., social isolation), but are beneficial to the group asa whole (e.g., safeguarding vulnerable groups; i.e., a societal conundrum) [18]. A recent
study revealed that educating individuals about the dangers the pandemic poses to theirhealth significantly increased their trust in strangers [19].
The pandemic can influence individual trust in the government, doctors, and strangers [20].
A study found that trust in the government among Chinese residents was at an overall highlevel during the COVID-19 pandemic [21], as individuals were required to trust and comply
with the recommendations provided by government officials and doctors to mitigate thepandemic. Trust in institutions may be enhanced instinctively among individuals in thecontext of a common external threat owing to limitations imposed on choices [22]. In
accordance with the source model of group threat, perceptions of intergroup threat will
strengthen (in)group identification processes and relations because the perceived source ofa threat is crucial in forecasting its repercussions [23]. According to the source model ofgroup threat, groups such as nations confronting external threats react by strengthening
their intragroup connections [23]. Particularly, the emotional connections of individuals
are strengthened through both location and national identity, developing a strong sense ofcommunity [23,24]. Thus, public trust increases after natural disasters, possibly as a resultof the need for everyone to work collaboratively to overcome the tragedy [25]. Based on
research conducted in the United States during the H1N1 epidemic, individuals exhibited
substantial trust in public health officials [26]. Furthermore, longitudinal data analyzed
in Switzerland showed that individuals initially had high levels of trust in government
and public health officials. However, that trust gradually declined as the H1N1 pandemic
progressed [27].
In the present study, several indices of public trust relevant to the COVID-19 pandemic
were included: trust in local government officials, doctors, and strangers [12,18,19]. Local
government officials and doctors are cooperating to initiate response and communicate
the corresponding reasons to the public [12]. Trust and cooperation among strangers may

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be the key to understanding how societies are dealing with the COVID-19 pandemic [18].
These indicators cannot cover all aspects of public trust, but are representative, and thecontent and orientations involved are the focal issues deeply felt by the general public in asudden pandemic [6]. Most research demonstrates that being exposed to natural disasters
like earthquakes, typhoons, and volcanic eruptions strengthens relationships of trust andcollaboration [25,28–31]. We, therefore, assumed that trust in local government officials,
doctors, and strangers would increase after the outbreak.
Perceived societal fairness was also considered. Perceived societal fairness is theability of an individual to perceive and judge the extent of social fairness and includes three
types, namely, distributive, procedural, and interactional fairness [32,33]. An imbalancein perceived societal fairness can lead to the accentuation of social conflicts and contradictions, affecting social harmony and stability [34–36]. Maintaining societal cohesion bydealing with injustices would be critical in improving well-being post-pandemic [37]. The
Chinese government adopted three measures, including increasing the construction of the
public health system, improving the capacity of large-scale nucleic acid testing, and fully
covering the cost of treating patients with coronary pneumonia, in order to maintain social
harmony and stability [38]. Therefore, it was assumed that residents would experience
more perceived societal fairness. Coordinated efforts among individuals, communities,
and governments to combat a pandemic indicate a strong signal of cooperation, facilitating
the reorganization of external and internal groups into communities of purpose. Moreover,
the renewed social identity can help to coordinate and manage responses to threats while
promoting adherence to commitments and norms among group members, thus facilitating
national leadership behaviors [20]. An individual sense of common fate, which involves
identification and positive feelings about the nation, may have also increased with the
COVID-19 outbreak [23,24]. Similarly, patriotism and identification with fellow citizens
increased among Americans following the 9/11 incident [39]. Thus, social identity during the COVID-19 pandemic can enhance group relations and enhance perceived societal
fairness [37].
1.2. Depression and Subjective Well-Being
People’s mental health is likely to be impacted by the lived experience of witnessing
the pandemic unfold, in addition to the social isolation and financial uncertainty brought onby the pandemic [22]. Relevant studies demonstrate that experiencing widespread calamities (e.g., natural disasters and war) instantly puts people’s mental health at risk [40,41].
First, anxiety, panic, depression, and other negative emotions may be exacerbated amongindividuals during the pandemic, as well as profound changes to their mental health [42,43].
Furthermore, studies found that people experienced relatively high levels of anxiety anddepression after the outbreak [7,8], which is consistent with the findings underscoringhigh levels of fear and anxiety following the global SARS outbreak in 2003 [44,45]. Second,
pandemics are considered a social threat as they reduce offline interpersonal and social
contact, which undermines the subjective well-being of individuals [46], causing a specificdecline in life satisfaction, future confidence, and interpersonal relationships. However,social threats also provide opportunities to enhance social cohesion and bonding [40,47],
which can mitigate the negative effects of the pandemic.
To assess pre- and post-pandemic mental health, we took depression and subjective
well-being as the mental health index, which reflect people’s psychological state fromdifferent aspects [48]. Subjective well-being refers to the overall judgment of an individual’s life state based on personal criteria. Furthermore, it provides a subjective, holistic,
and relatively stable perspective, serving as a comprehensive psychological indicator
outlining the quality of an individual’s life [49,50]. Moreover, subjective well-being emphasizes a comprehensive subjective evaluation that includes pleasurable experiencesof the individual’s body and mind, emotional feelings, and satisfaction. Furthermore,
two indicators of subjective well-being were examined [22,51,52]: satisfaction with lifeand personal well-being (future security, personal relationships, and impact of happy

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Int. J. Environ. Res. Public Health 2022, 19, 12365

event). Diener et al. [51] proposed that subjective well-being includes an indicator of life
satisfaction. Moreover, Cummins et al. [52] proposed a Theory of Subjective Wellbeing
Homeostasis, which proposes that subjective well-being includes indicators of personal
well-being, involving satisfaction with standard of living, future security, personal relationships, impact of happy/sad event, and health. Therefore, this study used life satisfaction,
future confidence, interpersonal relationships, and experience of happiness to measuresubjective well-being. The outbreak of the COVID-19 pandemic is likely to lead to changes
in the mental health of residents. Therefore, we assumed that increased depression would
be associated with decreased subjective well-being after the outbreak of COVID-19.
1.3. Education and Socioeconomic Status
Education is associated with public trust, perceived societal fairness, and depression.
A recent study found that a higher level of education is associated with higher government
trust during COVID-19 [53]. Furthermore, education has a profound impact on shaping
the public’s perceived societal fairness [54]. A follow-up study found that education
had a protective effect against an increase in depressive symptoms [55]. Socioeconomic
status is a social classification that reflects an individual’s position in the social hierarchy,
which comprises subjectively perceived social status and objective material resources
measured by income, education level, and occupational status [56,57]. Socioeconomic status
has an impact on all parts of an individual’s life [58], including public trust, perceived
societal fairness, and well-being. The positive predictive effect of socioeconomic status
on interpersonal trust has been confirmed by many cross-cultural studies [59]. Moreover,socioeconomic status affects individuals’ perceived societal fairness [60]. Some research
indicated that individuals with a higher socio-economic status had greater advantages
in personal health [61,62]. Evidence-based studies have shown that those with a lower
socioeconomic status experienced higher rates of depressive symptoms [63], and had lower
satisfaction with life [64]. In response to the COVID-19 crisis, we envision to verify thepredictive role of education and socioeconomic status based on these studies.
2. Materials and Methods
2.1. Data and Study Population
The data were derived from the China Family Panel Studies (CFPS), which is a project
financed by Peking University and the National Natural Science Foundation of China. The
CFPS was first conducted in 2010 by its affiliate, the Institute of Social Science Survey, witha goal sample size of 16,000 homes. Furthermore, the CFPS is a multidisciplinary, national,
large-scale social tracking survey including 25 autonomous regions [65]. This study linked
a sample of 37,354 data points from Wave 4 of the survey, which was from June 2018 toMarch 2019 (hereafter referred to as T1 or 2018) with 28,590 data points from Wave 5 of thesurvey, which was from July 2020 to December 2020 (hereafter referred to as T2 or 2020), and15,487 valid questionnaires were finally collected. The 15,487 participants were the same inboth waves. Data were screened according to the following conditions: (1) complete data
were available for the fourth and fifth waves on the measures of public trust, perceived
societal fairness, depression, and subjective well-being; and (2) samples with missing
values for the variables in this study were excluded. Listwise deletion was used in all
analyses owing to low levels of missing data (5% on any variable) [19]. Consequently, a
total of 15,487 valid data points were obtained, which consisted of 7801 men, 7686 women,
8158 living in urban areas, and 7329 living in rural areas. Their ages ranged from 16 to
90 years old (M = 45.49 years, SD = 15.22) in the fourth wave of the survey and from 18 to
92 years old (M = 47.48 years, SD = 15.22) in the fifth wave of the survey. Education levels
ranged from illiterate and semi-literate (2739), elementary (2882), middle school (4786),high school (2632), college (1310), bachelor’s degree (1037), master’s degree (96), to doctoral
degree (5), with values from 1 to 8.

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Int. J. Environ. Res. Public Health 2022, 19, 12365

2.2. Measures
2.2.1. Public Trust and Perceived Societal Fairness
Participants were asked about their trust in local government officials, doctors, andstrangers [12,18,19], which were scored between 0 and 10. The composite reliability coefficients were 0.77 at T1 and 0.76 at T2, which indicated acceptable reliability [66]. The
factor analysis revealed that the inequality scale included two factors, one of which is fivepositive scoring questions and the other is three reverse scoring questions [67]. In thisstudy, the five positively scored items, related to life during the pandemic, were used tomeasure individual perceptions of social justice, which were shown to be valid and reliablein previous studies [67,68]. Respondents indicate the extent of their agreement with theitems using a five-point scale (strongly disagree, disagree, neither agree nor disagree, agree,
and strongly agree). An example item is “There is equality in harmonious interpersonal
relationships”. Higher scores indicate a stronger sense of social justice [68,69]. In this study,
the composite reliability coefficients were 0.76 at T1 and 0.76 at T2.
2.2.2. Depression and Subjective Well-Being
Measures included the eight-item Center for Epidemiologic Studies Depression Scale(CES-D) used to assess depressive symptoms. Participants were asked to rate the frequencyof each symptom experienced throughout the previous week on a scale from 1 (“rarely ornone of the time (<1 day)”) to 4 (“most or all of the time (5–7 days)”). Furthermore, previous
studies showed that the eight-item CES-D scale was a valid and reliable screening measureof depressive symptoms [48,70]. In this study, the Cronbach’s alpha coefficients at T1 andT2 were 0.76 and 0.77, respectively; the composite reliability coefficients at T1 and T2 were0.79 and 0.79, respectively. Participants’ subjective well-being was also assessed, which
included life satisfaction, future confidence, interpersonal relationships, and experienceof happiness [22,51,52]. Consequently, four items were selected: “satisfaction with yourlife” (scored between 1 and 5), “confidence in your future” (scored between 1 and 5), “thequality of your relationships” (scored between 0 and 10), and “how happy are you” (scoredbetween 0 and 10). In order to better reflect the meaning of subjective well-being, wetransformed 0 points into 1 point for “the quality of your relationships” and “how happy
are you”, and performed a two-fold weighting for the points of “satisfaction with your life”
and “confidence in your future”. The composite reliability coefficients at T1 and T2 were0.82 and 0.83, respectively.
2.2.3. Socioeconomic Status
Socioeconomic status was measured by two items “What is your personal income
here?” and “What is your social status here?” [58]. Participants were asked about theircurrent situation on a scale from 1 to 5 (1 = very low, 5 = very high). The two indicators
were converted into standard scores according to Tan and Kraus [71] before being added. A
higher level of socioeconomic status was reflected by higher scores. Socioeconomic statusranged from −1.96 to 1.89 (M = 0.00, SD = 0.88).
2.3. Data Analysis
SPSS software version 26 (IBM, Armonk, NY, USA) was used to manage and analyze
the data. First, the existence of a common method bias was examined using Harman’s
single-factor test. Thereafter, a one-way repeated measures ANOVA was conducted to
explore the variation in public trust, perceived societal fairness, and well-being before andafter the COVID-19 outbreak. Finally, linear regression analysis was used to examine thepredictive effects of education and socioeconomic status on public trust, perceived societal
fairness, depression, and subjective well-being.
3. Results
Harman’s single-factor test was used to assess the common method bias based on
the self-reported data used. According to the exploratory factor analysis, the first factor

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Int. J. Environ. Res. Public Health 2022, 19, 12365

accounted for 14.17%, which was 40% below the critical value [72], indicating that there
was no significant common method bias in this study.
3.1. The One-Way Repeated Measures ANOVA of Public Trust and Perceived Societal Fairness
A one-way repeated measures ANOVA was conducted on public trust and perceived
societal fairness, with gender, urban/rural, and age as covariates. The one-way repeatedmeasures ANOVA results for public trust and perceived societal fairness are shown in
Table 1. We chose η2 p as the effect size for the ANOVA, which can respond to the size ofthe difference. Notably, the value of η2 p is less than 0.04, which indicated a very small
effect [73–75].
Table 1. Repeated measures ANOVA of public trust, perceived societal fairness, depression, andsubjective well-being (the main effect of time).
Variables
Public trust
Perceived societal fairness
Depression
Subjective well-being

T1 (M ± SD)
14.03 ± 5.27
19.07 ± 2.64
13.46 ± 3.85
30.95 ± 5.63

T2 (M ± SD)
15.17 ± 5.08
19.15 ± 2.56
13.55 ± 4.06
30.98 ± 5.65

F

p

η2 p

90% CI [Lower, Upper]

111.66
4.50
4.27
14.87

0.000
0.034
0.039
0.000

0.007
0.000
0.000
0.001

[0.005, 0.01][0.000, 0.001][0.000, 0.001][0.000, 0.002]

For public trust, the main effect of time was significant, F(1, 15,483) = 111.66, p = 0.000,
η2 p = 0.007, 90% confidence interval; CI [76]: [0.005, 0.01], and T2 was significantly
higher than T1. In terms of societal fairness, the main effect of time was significant,F(1, 15,483) = 4.50, p = 0.034, η2 p = 0.000, 90% CI: [0.000, 0.001], and T2 was significantly
higher than T1.
3.2. The One-Way Repeated Measures ANOVA of Depression and Subjective Well-Being
A one-way repeated measures ANOVA was conducted on depression and subjective well-being, with gender, urban/rural, and age as covariates. The one-way repeatedmeasures ANOVA results for depression and subjective well-being are shown in Table 1.
For depression, the main effect of time was significant, F(1, 15,483) = 4.27, p = 0.039,
η2 p = 0.000, 90% CI: [0.000, 0.001], and T2 was significantly higher than T1. With regard to
subjective well-being, the main effect of time was significant, F(1, 15,483) = 14.87, p = 0.000,
η2 p = 0.001, 90% CI: [0.000, 0.002], and T2 was significantly higher than T1.
3.3. Linear Regression Analysis with Public Trust, Perceived Societal Fairness, Depression, andSubjective Well-Being as the Dependent Variables
Hierarchical multiple regression analysis was used to examine the predictive effects
of education level and socioeconomic status on T2 public trust, with T1 public trust,gender, urban/rural, and age as control variables. We use the same approach to analyze
perceived societal fairness, depression, and subjective well-being, with the dependent
variable at T1, and gender, urban/rural, and age as control variables. As indicated inTable 2, results showed that education (β = 0.17, p < 0.001) and socioeconomic status(β = 0.91, p < 0.001) had a significant predictive effect on T2 public trust. As indicatedin Table 3, the results showed that education (β = −0.08, p < 0.001) and socioeconomic
status (β = 0.48, p < 0.001) had a significant predictive effect on T2 perceived societal
fairness. As indicated in Table 4, the results showed that education (β = −0.22, p < 0.001)
and socioeconomic status (β = −0.45, p < 0.001) had a significant predictive effect on T2
depression. As indicated in Table 5, the results showed that socioeconomic status (β = 1.99,
p < 0.001) had a significant predictive effect on T2 subjective well-being.

22Int. J. Environ. Res. Public Health 2022, 19, 12365

Table 2. Public trust as the dependent variable.
First Level

Variables
Constant
T1 Public trust
GenderUrban/rural
Age
Education
Socioeconomic status
R2
∆R2
F

Second Level

β

t

β

t

9.30 ***
0.45 ***
0.27 ***
0.01−0.01 ***

66.43
3.79
0.18
−5.69

9.47 ***
0.43 ***
0.24 **
0.02−0.02 ***
0.17 ***
0.91 ***

61.92
3.30
0.20
−6.99
5.74
21.58

0.22
0.22 ***
1118.42 ***0.25
0.02 ***
849.82 ***Note: ** p < 0.01, *** p < 0.001.

Table 3. Perceived societal fairness as the dependent variable.
First Level

Variables
Constant
T1 Perceived societal fairness
GenderUrban/rural
Age
Education
Socioeconomic status
R2
∆R2
F

Second Levelβt13.11 ***
0.31 ***
0.22 ***−0.31 ***
0.00

42.43
5.59
−8.00
1.95

β

t

14.25 ***
0.29 ***
0.25 ***−0.17 ***
−0.01 ***
−0.08 ***
0.48 ***

39.00
6.61
−4.16
−5.29
−5.29
21.42

0.12
0.12 ***
509.15 ***0.14
0.03 ***
432.81 ***First Level

Second Level

Note: *** p < 0.001.

Table 4. Depression as the dependent variable.
Variables
Constant
T1 Depression
GenderUrban/rural
Age
Education
Socioeconomic status
R2
∆R2
F

β

t

7.26 ***
0.50 ***−0.37 ***
−0.42 ***
−0.00

66.85
−6.43
−7.32
−0.32

0.24
0.24 ***
1216.65 ***βt8.27 ***
0.48 ***−0.32 ***
−0.33 ***
−0.00
−0.22 ***
−0.45 ***

63.66
−5.64
−5.40
−1.69
−9.28
−13.370.25
0.01 ***
865.99 ***Note: *** p < 0.001.

Table 5. Subjective well-being as the dependent variable.
First Level

Variables
Constant
T1 Subjective well-being
GenderUrban/rural
Age

23

Second Level

β

t

β

t

13.54 ***
0.52 ***
0.11−0.32 ***
0.03 ***

76.00
1.48
−4.18
11.95

17.25 ***
0.43 ***
0.12
0.02
0.01 *

63.74
1.58
0.21
2.17

Int. J. Environ. Res. Public Health 2022, 19, 12365

Table 5. Cont.
First Level

Variablestβ

Education
Socioeconomic status
R2
∆R2
F

Second Level

0.28
0.28 ***
1532.81 ***

β

t

0.02
1.99 ***

0.61
44.71

0.37
0.08 ***
1487.42 ***Note: * p < 0.05, *** p < 0.001.

4. Discussion
Globally, countries are taking action against COVID-19. The present study analyzed
the variation in public trust, perceived societal fairness, and well-being before and after theoutbreak of the COVID-19 and verified the predictive role of education and socioeconomic
status. In addition to theoretical and scientific implications, these findings provide valuable
information to governments, which must rapidly develop and alter COVID-19 management
plans, as well as to the global population, who are confronted with this dilemma [22].
4.1. Variation in Public Trust and Perceived Societal Fairness
The present study found that public trust and perceived societal fairness significantly
improved, which was supported by previous studies [21,25,26]. Firstly, consistent withgroup threat theory that perceptions of intergroup threat will strengthen (in)group identification processes and relations, a significant increase in public trust was observed in thisstudy because external threats motivate people to unite and trust each other [23]. Secondly,
the bold and decisive actions of the Chinese government, including the shutdown of theeconomy, have the potential to unite people in the fight against the pandemic. A recent
study indicated that individuals’ trust is a critical factor at various stages of a complicated causal chain of pandemic response, such as diagnosis, regulation, promulgation,and enforcement [12]. Furthermore, public trust facilitates the implementation of pandemic prevention and control measures [20]. Studies have found that the implementationof official recommendations for preventive behaviors occurs most likely when trust in
the government is high and negative feelings are low [77]. Several findings suggest thattrust in local governments contributes to lower infection rates [78]. Similarly, a Dutch
follow-up study found that pandemic lockdown measures increased residents’ trust in
the government and science [79]. Perceived societal fairness is related to the residents’social identities [37]. The COVID-19 pandemic outbreak has facilitated the internationalrecognition and validation of China’s rapid response, efficient strategy, and successful
experience in mitigating the pandemic. Moreover, China’s response to the outbreak hasenhanced the pride of the nation. The Chinese government has created new norms within
existing groups to address societal inequalities by prioritizing the safety and health of its
people, thereby underscoring the concept of “life first, people first” [38]. Therefore, thismay result in a very stable perceived societal fairness for residents.
4.2. Variation in Depression and Subjective Well-Being
The present study found that depression significantly increased among residents,
which is supported by previous empirical studies [7,8]. Another surprising finding wasthat subjective well-being has also increased. Notably, both depression and subjective
well-being had low effect sizes. First, this result may be related to the time period. In
February 2020, the development trend of the pandemic was very alarming, with the mostserious period in the Chinese mainland. During this stage, the negative impact of thepandemic was the most representative and intense. By April 2020, the Chinese government
had curbed the development trend of the pandemic through a series of measures, withpeople in most areas gradually returning to work and school. As a result, the negative
effects may have decreased. Data from the survey were collected from July to December
24Int. J. Environ. Res. Public Health 2022, 19, 12365

2020; therefore, individuals’ depression and subjective well-being during this period did
not change significantly [42]. Second, public trust may have contributed to the stability
in mood (i.e., level of depressive symptoms) and subjective well-being. A large survey
conducted across several countries, including China, found that social trust was positively
associated with life satisfaction [80]. Furthermore, our study found that people’s publictrust and perceived societal fairness significantly improved, which may contribute to thestability in their mental health. Moreover, the improvement in subjective well-being may
be impacted significantly by the positive effects of the increased public trust and perceived
societal fairness. However, the adverse short-term effects of the pandemic on depression
and subjective well-being were limited, which may partly be due to an increase in publictrust and perceived societal fairness. Nevertheless, depressive symptoms increased after
the pandemic, which is consistent with previous research showing that social threats havea negative effect on mental health [81,82]. Overall, the individuals showed resilience inmental health.
4.3. Predictive Role of Education and Socioeconomic Status
The present study verified the predictive role of education and socioeconomic status.
Education and socio-economic status are important factors in determining public trust,perceived societal fairness, and well-being [83]. The subjective evaluation of the COVID-19
crisis responsiveness varies depending on the individual. To establish an opinion, one musthave the ability and motivations to look for and analyze relevant information. Education
is an essential source of information [53]. Socioeconomic status reflects an individual’s
objective social resources and subjective perceptions of social status [56,57]. It is thus
crucial to develop measures that correspond to the educational and socioeconomic status
of individuals. The predictive role of education and socioeconomic status contributes tounderstanding the factors of public trust, perceived societal fairness, and well-being incrisis management. Furthermore, offering insightful information about how individuals
respond to government action may be helpful in future instances.
4.4. Limitations and Future Research Directions
This study used public trust, perceived societal fairness, and well-being data of
15,487 residents from 2018 (T1) to 2020 (T2) from the CFPS. Overall, the residents in thisstudy showed resilience, which is consistent with previous findings. For example, Zhou [84]underscored that the improvement in psychosocial resilience is the future direction for thesocial mindset of Chinese people. However, this study has several limitations. First, thedata from the CFPS conducted in 2018 and 2020 were secondary. As a result, it is possiblethat the variables chosen for measurement did not accurately capture the concept’s meaning. Future research should employ specific subjective well-being and public trust researchmethods. Second, although we found a significant effect of public trust, perceived societal
fairness, and well-being, the effect size was small. Future studies should concentrate onother variables that affect public trust and subjective well-being. The effects on public trust,perceived societal fairness, depression, and subjective well-being are ongoing as a result of
economic, political, and social development, as well as the repeated adverse effects of thepandemic. Thus, future in-depth studies regarding this topic still need to be conducted.
First, the proportion of depressed people may increase with the recurrence of thepandemic imposed by the impact of various management policies on interpersonal socialization, such as pandemic prevention and control, and constraints on economic development [22]. Simultaneously, an immediate increase in public trust may decline over time. A
follow-up study of the pandemic in Italy found that people’s sense of social responsibility
increased. However, trust in the government remained largely unchanged over time owing
to the impact on economic development, while trust in science and health participation
decreased [85].
Second, there should be an increased focus on the differences in the ability of various
groups to cope with the pandemic, focusing on assessing protective and risk factors for25

Int. J. Environ. Res. Public Health 2022, 19, 12365

different groups to cope with the pandemic. For example, groups lacking social support
and pre-existing underlying problems that pose a greater risk during a pandemic should
be included in future research [82,86]. Furthermore, combined stressors such as unemployment or relationship instability can exacerbate the impact of a pandemic [22]. Longitudinalstudies conducted in the United States after 9/11 found that most people were resilient,despite a significant proportion who developed post-traumatic stress disorder [86]. Thus,
future studies should prioritize exploring approaches to help different groups cope with apandemic [20].
Finally, the results of this study highlight the need for a unified response to thepandemic amidst adversity and the importance of gaining social acceptance for pandemic
control initiatives [20,87]. This study shows that the strong response to the pandemic in
China could contribute significantly to the increased public trust among the population.
Furthermore, China has always adhered to the concept of building a community of humanpurpose. Moreover, China has been responsible for life safety and physical health within
its population, which has implications for global public health and has gained widespread
acceptance, most likely increasing trust in the government among residents [38].
5. Conclusions
The current study analyzed the variation in public trust, perceived societal fairness,
and well-being before and after the outbreak of COVID-19 based on two-wave longitudinal
data of 15,487 residents (2018, T1; 2020, T2) derived from the CFPS. The results showed thatpublic trust, perceived societal fairness, and subjective well-being significantly improved
and depression significantly increased. Linear regression analysis showed that educationand socioeconomic status had a significant predictive effect on public trust, perceived
societal fairness, and depression; socioeconomic status had a significant predictive effecton subjective well-being. Thus, this research provides evidence and direction for current
social governance, with implications for policy implementation and effective pandemic
response methods.
Author Contributions: Conceptualization, C.W., Q.L. and S.S.; methodology, C.W. and Y.L.; data
curation, C.W. and Z.L.; writing—original draft preparation, C.W.; writing—review and editing, C.W.
and Q.L.; supervision, H.C.; project administration, H.C. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was funded by the National Nature Science Foundation of China (NO.32271087),
Collaborative Innovation Team Project for Philosophy and Social Sciences in Chongqing Universities(NO.7110200530), and Key research topics on the theory and practice of ideological and politicaleducation for college students in Guangxi higher education institutions (NO.2020MSZ038).
Institutional Review Board Statement: Ethical review and approval were waived for this study due
to publicly open data from the China Family Panel Survey.
Informed Consent Statement: All the participants in CFPS survey were required to sign an informed consent form (http://www.isss.pku.edu.cn/cfps/docs/20200615141215123435.pdf, accessedon 11 May 2021) before interview, and all the obtained information from participants was handled
voluntarily, confidentially, and anonymously.
Data Availability Statement: Original data in this study are obtained from the Institute of Social
Science Survey of Peking University and are available at http://www.isss.pku.edu.cn/cfps/index.
htm (registration and approval needed, accessed on 8 January 2022).
Acknowledgments: We gratefully acknowledge Peking University for giving us the permission to
use the data of the China Family Panel Studies conducted in 2018 and 2020.
Conflicts of Interest: The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported in this paper.

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International Journal ofEnvironmental Researchand Public Health
Review

A Review of COVID-19-Related Literature on Freight Transport:
Impacts, Mitigation Strategies, Recovery Measures, and Future
Research Directions
Ahmed Karam 1,2, *, Abdelrahman E. E. Eltoukhy 3 , Ibrahim Abdelfadeel Shaban 4
1
2
3
4
5

*

Citation: Karam, A.; Eltoukhy, A.E.E.;
Shaban, I.A.; Attia, E.-A. A Review of
COVID-19-Related Literature on
Freight Transport: Impacts,
Mitigation Strategies, Recovery

and El-Awady Attia 5

Department of the Built Environment, Aalborg University, 9220 Aalborg, Denmark
Department of Mechanical Engineering (Shoubra), Benha University, Benha 11672, Egypt
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University,
Hong Kong SAR, China
Mechanical and Aerospace Engineering Department, UAE University,
Al-Ain P.O. Box 111, United Arab Emirates
Department of Industrial Engineering, College of Engineering, Prince Sattam bin Abdulaziz University,
Al Kharj 11942, Saudi Arabia
Correspondence: akam@build.aau.dk

Abstract: The COVID-19 pandemic has caused significant disruptions in the freight transport sector.
The number of studies on the impact of COVID-19 on freight transport and possible mitigation
strategies are growing. However, a systematic and comprehensive review highlighting the research
themes, main findings, research methods, and future research directions of these studies remains
scarce. Therefore, this study presents a mixed review comprising scientometric and systematic
reviews to cover these research gaps. Results show that 68 studies have been published on this topic
since the beginning of 2020 and that they cover three main themes: the impacts of COVID-19 on
freight transport, mitigation strategies, and recovery during and after COVID-19. In addition, we
describe the research methods, main findings, and possible research directions in each of them. Thus,
the findings of our work present both theoretical and practical analyses of COVID-19-related researchon freight transport and provide important future research directions in this domain.

Measures, and Future Research
Directions. Int. J. Environ. Res. Public
Health 2022, 19, 12287. https://

Keywords: freight transport; COVID-19; air freight; sea shipping; road transport; recovery measures;
strategies

doi.org/10.3390/ijerph191912287
Academic Editors: Sachiko Kodera
and Essam A. Rashed
Received: 4 August 2022
Accepted: 20 September 2022
Published: 27 September 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
In December 2019, China announced the first case of the novel coronavirus disease,called “SARS-CoV2”. Since then, the virus has spread over the world, infecting andkilling people. By March 2020, the World Health Organization (WHO) had declared it apandemic and officially named it “COVID-19”. By the end of 2020, the virus had infected
more than 82 million people and killed over 1.8 million. Therefore, the pandemic may
be referred to as the most disastrous black swan event in 2020. To curb the spread of
COVID-19, travel restrictions were firmly implemented by governments worldwide. As
a result, these restrictions have significantly affected all modes of freight transport andcaused severe disruptions. As of April 2020, global demand for air freight has dramaticallydeclined by approximately 27.7% compared to 2019 [1], while air freight capacity shrankby 42% compared to 2019 [2]. This dire situation has also been observed in global roadfreight, with an estimated loss of EUR 550 billion due to movement restrictions imposed bygovernments [3]. Similarly, sea freight transport was not spared, as container ship mobility
reduced by approximately 14% in June 2020 [4].
Freight transport has always been an important aspect of the supply chain. Therefore,
since 2020, many studies have studied the impact of COVID-19 on freight transport logistics
and possible mitigation strategies. Examples of these impacts include shortage of transport

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Int. J. Environ. Res. Public Health 2022, 19, 12287

resources [5,6], higher operating costs [7,8], and fluctuations in demand for transport
services [6,9]. The identification of appropriate strategies for managing such impacts has
been addressed in some studies [10,11]. Given the significance of this topic, it is valuable
to report the up-to-date status of research regarding freight transport during COVID-19
and outline some future research directions. It is worth noting that some previous studies
have addressed the impact of other types of pandemics, e.g., influenza [12], cholera [13],Ebola [14], and malaria [15]. In addition, some scholars have published review papers on
the impacts and response strategies to these disease outbreaks [16,17]. However, thesereview papers are focused on the pre-COVID 19 pandemics with emphasis on logistics [16],supply chains [17], and buying behaviors of customers due to pandemic outbreaks [18].
Compared to the previous pandemics, COVID-19 stands out with extraordinary impacts on
virtually all human activities. Unlike the previous pandemics, that disrupt activities in somelocales, COVID-19 affects businesses around the globe. Furthermore, to curb the spreadof the disease, many countries implement lockdowns that halt all industrial and social
activities with attendant disruptions to most global and local supply chains [19]. Although
the COVID-related literature on freight transport is constantly growing, the findings fromthese studies are still scattered across several sources and rather unsystematized. Morespecifically, the literature reporting COVID-19-pandemic-related freight transport studies
in a systematic approach remains scarce. In this regard, there are only two relevant review
papers [19,20]. Chowdhury, Paul, Kaisar and Moktadir [19] conduct a review study on
the impacts of the COVID-19 pandemic on eight supply chain-related areas includingtransportation and logistics. Despite the significance of this review study, it considers
only a small set of the COVID-19-related freight transport studies, i.e., 12 studies. Borca,
Putz and Hofbauer [20] review the literature on the different crises that hit Europe in theprevious 20 years and their effects on freight transport modes. Although the authors list
COVID-19 as one of the crises, only three studies related to the impact of COVID-19 on
freight transport are covered.
Unlike the previous studies, this current work analyzes 57 COVID-19-related studies
on freight transport. In particular, this work seeks to answer the following three main
research questions (RQ):
RQ1: What are the research themes and main findings of the literature on freighttransport during COVID-19?
RQ2: What are the main research methods used in the COVID-19-related literature on
freight transport?
RQ3: What are the future research directions in the freight transport sectors withrespect to the COVID-19 pandemic?
To answer these three research questions, the present study uses a mixed review
method that combines scientometric and systematic review methods. A scientometricanalysis is conducted to identify the major research scopes, linkage of researchers, countries,
and others on research articles related to COVID-19 and freight transport. Next, a systematic
review of these articles is undertaken to summarize the main research themes in this domain.
The findings of this work will be of immense benefits to researchers and forestall duplicationof research efforts at this stage of the pandemic. Additionally, researchers can take cues
from the identified future research avenues to enrich the existing body of knowledge inthe domain of freight transport. Furthermore, practitioners will gain deep insight into themain themes of freight transport and how scholars have addressed them. This will help
bring more innovation and performance improvement in this field.
The remainder of this paper is organized as follows. Section 2 presents the review
methodology for scientometric analysis and systematic review. In Section 3, results of thescientometric analysis are presented, whereas Section 4 discusses the systematic literature
review. Lastly, Section 5 concludes the study.

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Int. J. Environ. Res. Public Health 2022, 19, 12287

2. Review Methodology
Figure 1 summarizes the methodology of the review process, which combined bibliometric analysis, scientometric analysis, and systematic review. Note that the bibliometricanalysis was conducted before the scientometric analysis and systematic review, to ensurethat the collected bibliographic data were representative of the proposed review topic. The
following sub-sections expatiate on the details of each process.

Figure 1. Summary of the review methodology.

2.1. Bibliometric Analysis
The bibliometric analysis started with the definition of the scope, which was freighttransport amid the outbreak of COVID-19. Next, based on this scope, combinations of
three keywords and their related synonyms were used to search for the related literature
in a scientific database. These keywords, “COVID-19, coronavirus, and SARS-CoV-2”,“transport”, and “freight, cargo, and goods”, were combined by using Boolean logic, as
shown in Figure 2. After generating the combination of keywords, the SCOPUS database
was selected due to its updated and wide coverage of existing literature on the proposed
topic [21]. The search results gave 101 research documents, which further dropped to
57 articles when only English language documents were extracted. Note, the search results
were downloaded in a CSV file, which included each article’s title, abstract, author list,
funding details, etc. The bibliometric analysis ended with the filtering process, which
comprised screening the articles’ titles and reading their abstracts to eliminate irrelevant
research papers.
2.2. Scientometric Analysis
Scientometric analysis is a data and text mining technique that uses the filtered datafrom the bibliometric study to visualize the relationships between keywords, citations,authors, and others. This analysis reduces the subjectivity associated with the findingsof narrative reviews [22]. Thus, this analysis is adopted in several studies in different
research fields, including transportation [23], construction management [21], and healthcare
management [24]. As shown in Figure 1, the first step in conducting the scientometricanalysis was to revise the results of the bibliometric analysis and then select a suitablesoftware to perform the scientometric analysis. In this study, VOSviewer [25] was utilized33

Int. J. Environ. Res. Public Health 2022, 19, 12287

for the scientometric analysis, including analysis of keyword co-occurrence, co-citation,and co-authorship.

Figure 2. Keywords combination through Boolean logic gates.

2.3. Systematic Review
Despite the advantages of the scientometric analysis, it is still insufficient to provide acomprehensive understanding of the bibliometric results. Therefore, a systematic review
can be deployed to organize the extracted articles based on a number of research themes.
As shown in Figure 3, we identified three broad research themes in the existing studies.
These included the impact of COVID-19 on freight transport, the mitigation strategies thatwere adopted, and the ability to recover during and after COVID-19. At the early stageof the pandemic, countries of the world proposed several measures to fight the spread of
infection, which in turn influenced freight transport systems in several ways, as reported
by researchers and governmental reports. We identified these studies and categorized
them under five main impacts. During the pandemic or what is now called the new norm,
maintaining food and medical supplies was dependent on creating robust freight transport systems. To this end, practitioners paid great attention to risk mitigation programs,
researchers proposed novel mitigation strategies based on state-of-the-art technologies,
and additionally, some regulations on freight transport were relaxed in many countries.
Some of these mitigation strategies are expected to lead to rethinking the future of freighttransport in the post-pandemic era. We categorized these mitigation strategies into five,which included (1) usage of autonomous delivery vehicles (ADVs), (2) drone delivery,34

Int. J. Environ. Res. Public Health 2022, 19, 12287(3) deployment of mobile warehouses, (4) engagement of large ships, and (5) application ofquantity discounts. In addition to adopting risk mitigation strategies, the evaluation of therecoverability of freight transport systems was tackled through several measures, such assystem recovery rates, quality, demand recovery, and efficiency.

Figure 3. Schematic diagram illustrating the 3-D matrix research themes, including impact, mitigation,and recoverability.

3. Results and Discussion of the Scientometric Analysis
Table 1 summarizes the selected scientometric analysis performed in this study, including keyword co-occurrence, co-authorship, and citation analysis. For each analysis, its
related criteria are fixed, as shown in Table 1. Based on the search output from the SCOPUS
database, the 57 documents that cover the research scope contain 604 keywords. To study
the most co-used keywords in these documents, a threshold criterion of three is applied(i.e., the minimum occurrence of a keyword is set to three). Forty-two keywords meet thethreshold criterion, and all of them are connected. In analyzing co-authorship between
40 countries of the authors who have published on freight transport during COVID-19, twothresholds are applied: one for the minimum number of documents (i.e., 2) per country
and another for the minimum citations (i.e., 0) of countries. Eighteen countries meet thethreshold criteria, and only fourteen of them are connected. In the following sub-sections,the results of these analyses are discussed. Similarly, in the citation analysis of the sources,
36 sources meet the threshold criteria, but only 13 are connected.
Table 1. Summary of results of some selected scientometric analyses.
Analysis
Co-occurrence of(keywords)
Co-authorship between(countries)
Citation of (sources)

Total Numberof (---)

Threshold Criteria Minimum Number ofOccurrence
Documents
Citation

Number of (---) Meetthe Threshold

Number ofConnected (---)

604

3

–

–

42

42

40

–

2

0

18

14

36

–

1

0

36

13

3.1. Keyword Analysis
Keyword analysis is used to generate the keywords mapping network shown in
Figure 4. The mapping network consists of nodes connected by lines. Keywords withbigger nodes mean they are more frequently used than others with smaller nodes. Similarly, a thicker connecting line between two keywords indicates the frequency of theiroccurrence in multiple articles. Therefore, the keyword “COVID-19” is the most frequently
used. Meanwhile, it co-occurs most with the keywords “Airline industry”, “Maritime
trade”, “Transportation policy”, etc. This implies that most of the research in this domain
focuses on policies aimed at mitigating the impact of the COVID-19 pandemic on freighttransportation.
35

Int. J. Environ. Res. Public Health 2022, 19, 12287

Figure 4. Keyword mapping network.

Table 2 shows the top 15 keywords used in freight transport studies amid COVID-19,
while others are shown in the keyword mapping network in Figure 4. For instance, “drones”
can be regarded as one of the most important mitigation strategies, though further investigations into their use are required. This is because of the legal differences to fly them in
different countries [26]. Additionally, COVID-19 can be deemed as one of the long-term
disruptions. For example, flight schedules are interrupted, borders are closed, and work
modes have changed. However, the word “optimization” is not used sufficiently to copewith the disruption in the three modes of freight transport.
Table 2. Top used keywords.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

KeywordLinks

Occurrences

COVID-19
Freight transport
Supply chains
China
Epidemic
Viral disease
Sustainability
Air transportation
Logistics
Ships
Transportation policy
Airline industry
Cargo
Human
Transportation system

38
20
8
25
19
26
10
15
14
9
15
9
12
15
9

52
14
10
9
8
8
8
6
5
5
5
4
4
4
4

36

Int. J. Environ. Res. Public Health 2022, 19, 12287

3.2. Co-Authorship Analysis
As COVID-19 has spread worldwide, it is interesting to see how different countries
cooperate in the research on freight transport. For this purpose, a co-authorship analysis isconducted, as shown in Figure 5. Figure 5a shows that the highest co-authorship in termsof the number of published research works is found in the cluster comprising China, Hong
Kong, and India. However, in Figure 5b, the cluster of the most cited authors includes
Canada and the United States. This means that there is no direct correspondence between
citation and number of publications.

Figure 5. Density visualization of the co-authorship between countries based on (a) total publisheddocuments, and (b) number of citations.

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Int. J. Environ. Res. Public Health 2022, 19, 12287

3.3. Source Citation Analysis
The number of citations of an article does not only reflect its impact but also affects
the impact of the publishing source. Thus, the analysis of citation is necessary to identify
the most impactful research works, sources, and authors [22]. However, Table 3 shows thatthe journal sources with a high number of research articles do not necessarily publish highimpact research articles with high citation counts. For more detail, until the time of ourresearch (the last update of these scientometric data was on 11 December 2021), Sustainability(Switzerland) and Science of Total Environment (SOTE) have received 39 and 37 citations,
respectively. However, these citation counts do not reflect the exact impact of these twojournals, because the number of documents published by Sustainability and SOTE are 10
and 1, respectively. This means that the average citation for Sustainability does not exceed
4 compared to 37 in SOTE. Moreover, normalization of citation counts is a metric thatcan be used to eliminate the effect of time on source citation (i.e., the propensity that anolder document is more likely to get higher citations than a newer one). For example,
the average number of citations for Transportation Research Interdisciplinary Perspectives(TRIP) is 30, while it is 79 for the International Journal of Advanced Manufacturing Technology(IJAMT). However, the normalized citation of TRIP is 3.7687, which is almost three times
the normalized citation of IJAMT. To conclude, the most impactful journal within the period
under review is the Canadian Journal of Agricultural Economics, because it has the highest
average citations and the highest normalized citation.
Table 3. Top cited sources.
Source
1
2
3
4
5
6
7
8
9

Canadian Journal of Agricultural Economics
Transportation ResearchInterdisciplinary Perspectives
International Journal of Advanced
Manufacturing Technology
Transport Policy
Sustainability (Switzerland)
Science of the Total Environment
IEEE Engineering Management Review
International Journal of Logistics Researchand Applications
Ocean and Coastal Management

Documents

Citations

Normalized Citations

Avg. Citations

2367

9.1039

183.5

3

90

3.7687

30

1

79

1

79

7
10
1
1

49
39
37
30

8.74275.42236.60170.7442

7
3.9
37
30

1

20

0.496120

110

1.7842

10

4. Results and Discussion of the Systematic Review
4.1. COVID-19-Related Impact
Our review shows that 36 out of the identified studies investigate different impactsof COVID-19 on the freight transport industry. These 36 studies are carefully analyzed
to identify and group the different impacts of COVID-19 on freight transport. There are
five categories of these impacts, including impact on: (1) transport demand, (2) transportcapacity, (3) operational transport cost, (4) delivery performance, and (5) carbon emissions,as shown in Table 4. It should be noted that a study may report more than one impact;
therefore, the sum of all entries under ‘studies per impact’ is not necessarily 36. In thefollowing subsections, these 36 articles are classified and thoroughly analyzed based on thereported impacts, transport mode, and research methodology.
Table 4. Categorization of different impacts of COVID-19 on freight transport.
Impacts of COVID-19

Studies per Impact

References

Demand for freight transport services
Capacity shortage
Operating transport cost
Delivery performance
CO2 emissions

24
10
13
9
4

[5–9,11,27–43][5,6,32,34,35,44–48][5–8,20,27,31,32,34,35,49–52][8,9,32,47,48,53–55][5,28,38,56]

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Int. J. Environ. Res. Public Health 2022, 19, 12287

4.1.1. Impacts of COVID-19•

Demand for freight transport services:

Twenty-four studies report the impact of the COVID pandemic on the demand for
different freight transport modes, i.e., air, maritime, rail, and road. Six studies examine theimpact of COVID-19 on the demand for seaports and waterways transport [6,7,9,37,39–43].
Tianming, Erokhin, Arskiy and Khudzhatov [9] show that the pandemic breaks the connectivity among seaports in China, Norway, Iceland, Russia, South Korea, and Sweden.
In Australia, Munawar, Khan, Qadir, Kouzani and Mahmud [6] report that during theinitial stage of the pandemic, the demand for water freight transport declines by 9.5%.
Michail and Melas [7] indicate that the daily number of calls of dry bulk and clean tankers
in ports is highly affected by the pandemic, while the pandemic has no impact on the calls
for crude oil vessels. Narasimha, Jena and Majhi [37] confirm that the overall demand
for major Indian seaports declines by 4.59% for 2020 compared to 2019. A case study ofa maritime transport service provider shows that the transshipments of cargo decreases
by approximately 32%, whereas the urgent supplies of medical equipment increase [39].
Further, five studies examine the impact of the COVID-19 pandemic on the demand forairfreight transport [5,6,34,36,38]. For example, in Australia, the demand for airfreighttransport decreases significantly from 84.8 million kilograms in January 2020 to 54.5 million
kilograms in April 2020 [6].
Nine studies examine the impact of the COVID-19 pandemic on the demand for roadfreight transport [6,8,11,20,27–30,33]. For example, Arellana, Márquez and Cantillo [28]show that freight trips in Colombia reduce by around 38% due to a low demand for nonessential products. Due to the lockdown and travel restrictions, people shift from in-store
shopping to online shopping and demands for last-mile deliveries increase [6,8,11,27].
From January 2020 to June 2020, global retail platforms witness a 26.9% increase in onlineshopping orders [57]. In Europe, online shopping sales in April 2020 increase by 30%
compared to April 2019 [58]. Due to the panic buying behavior of people during the
pandemic and the sharp fall in oil prices, Ho, Xing, Wu and Lee [33] report a positive
correlation between road freight transport turnover and the number of confirmed casesof COVID-19 in China. In Portugal, Cruz and Sarmento [30] state that during the peakof COVID-19 in April 2020, the traffic volume of light commercial vehicles reduces by anaverage of 44.5%, while the traffic of heavy trucks reduces by 24%. Bartuska and Masek [29]conclude that the traffic volumes of trucks and trailers are not significantly affected on the
major roads of South Bohemia (Czech Republic). Borca, Putz and Hofbauer [20] investigate
road transportation in the USA at the beginning of the pandemic. They report that despite
an unprecedented 46% reduction in the traffic volume of private cars, freight traffic volume
reduces by a mere 13%. These values of commercial traffic volumes indicate that the
supply of essential products is not significantly disrupted, especially to supermarkets andpharmacies during the pandemic. Additionally, this also implies that many companies,
even if closed, increase their inventories to be safe against future uncertainties and to start
production again once the lockdown ends. Although the previous studies focus on a single
freight mode, some studies investigate multiple freight modes. For example, Cui, He,
Liu, Zheng, Wei, Yang and Zhou [31] study the impacts of the COVID-19 pandemic on
China’s transport sectors. Their results show a reduction in the outputs of households andproduction sectors; thus, there is a reduction in the demand for transportation in these
sectors. For example, aviation freight transportation reduces by 2.81%, while road freighttransportation, railway freight transportation, and waterway freight transportation are
down by 2.20%, 1.84%, and 1.04%, respectively. Gray [32] reports an increasing demandin the agriculture sector for different transportation services, e.g., bulk ocean freight, rail
movement, and road trucks. Li, Bai, Hu, Yu and Yan [42] present equations to evaluate
the traffic variations of overall provincewide traffic and tested the hypotheses for traffic
recovery analysis. The results show that truck traffic dropped by 68.19% during theoutbreak and found that the higher the GDP per capita the region has, the more the traffic

39Int. J. Environ. Res. Public Health 2022, 19, 12287

in the region was affected by the pandemic. It is also found that truck traffic had a rapid
recovery period compared to that of passenger traffic.

•

Capacity shortage•

Operating cost and prices of transport services

Ten studies report the impact of the COVID pandemic on the transport capacity ofthe different freight transport modes. The imposed epidemic control requirements leadto a shortage of transport capacity, e.g., driver shortage, belly-hold shortage in airfreighttransport, and shortage of empty containers. In April 2020, passenger flights decrease byapproximately 95% compared to the previous year [45]. Similarly, belly capacity for international air cargo drops by 75% in April 2020, compared to the previous year. Meanwhile,at the peak of COVID-19, the demand for medical and personal protective equipment
significantly increases worldwide. On the other hand, air freight capacity is severely limited due to the shutdown of passenger flights [5,6,34,48] and the available cargo aircraft
cannot resolve the shortage in airfreight capacity. To resolve this, passenger aircraft are
granted exemptions from July 2020 to July 2021 to carry freight in the main cabin when no
passengers are being carried [59].
Workforce capacity of the transport sector has also been shown to be vulnerable toshortage because of mobility restrictions and sickness [45]. For instance, truck drivers
cannot access many toilets at rest areas since they are closed. Therefore, mobile toilets
are provided for truck drivers in Canada [32]. In China, the beginning of the pandemic
coincides with the holiday season, when most logistics laborers have travelled to theirhometowns. Subsequent restrictions on people’s mobility result in a severe workforce
shortage in the logistics industry [35]. On the bright side, quarantine obligations lead tothe high availability of some categories of the workforce, e.g., truck drivers, in freight transportation [46]. For example, most truck drivers working in Western European countries,e.g., France, Denmark, and Germany, are from Eastern European countries, e.g., Poland
and Romania. To avoid workforce shortages during the holiday periods, logistic companies
request their drivers to postpone their holidays to avoid the mandatory 14-day quarantine.
Containerized trade represents 90% of the global trade volume. Therefore, containers
are essential resources to transport different products between countries. In the container
supply chain, containers are frequently returned empty to the exporting country. The
COVID-19-related shutdown of many production facilities worldwide and the subsequent
reduction in demand result in a shortage of empty containers [47]. Some food products,
transported in containers, are delayed due to lack of empty containers in North America [32].
In addition, shortage of truck drivers, stringent inspection protocols, and requirement forquarantine delay the delivery of maritime freight [48]. Compared to sea and road transport,
rail transport is less labor-intensive since the rail transport sector relies on a small sizeof very specialized crews to operate large trains. However, rail transport may have been
impacted by COVID-19 if a significant proportion of the very specialized workforce is
infected [32].
Thirteen studies report the impact of the COVID-19 pandemic on the operating costsof freight vehicles. The reviewed literature identifies three reasons responsible for theincrease in operating costs of the logistics industry in China. Firstly, restriction on people’s
mobility cause a shortage in workforce capacity, leading to higher labor costs and freightrates [5,32,35,49]. Secondly, long-term infection prevention, e.g., disinfecting parcels andcontactless delivery facilities, attract extra cost [31,34,35]. Thirdly, uncertainties in labor
availability, traffic restrictions, and status of pandemic outbreaks incur extra costs inplanning transportation routes [7,27,35]. Gray [32] reports that COVID-19 prevention andprotective measures (e.g., multiple drivers are prevented from using the same truck) have
led to increased transport costs of agricultural products in Canada. To support companies
in applying such measures, Canadian transport authorities increase the maximum hours ofservice for drivers [60]. Some logistics enterprises adopt new modes of logistics services
to fight the COVID-19 pandemic through the use of contactless delivery. This has also40

Int. J. Environ. Res. Public Health 2022, 19, 12287

increased their operating costs, since contactless delivery needs extra investment cost incontactless facilities [8]. Michail and Melas [7] show that an increase of 1% in the global
COVID-19 infection cases decrease the freight rates of Baltic dry and dirty Tankers by
0.03% and 0.046%, respectively. Bartle, Lutte and Leuenberger [5] report that the airfreightrates increase during the pandemic due to limited air cargo capacity. For example, inJune 2020, the airfreight rates from China to Europe are 254% higher than those of June2019 [50]. By using a multi-sectoral computable general equilibrium model of China, Cui,
He, Liu, Zheng, Wei, Yang and Zhou [31] estimate that the cost of protective measures (e.g.,disinfection, quarantine, and parking inspection) lead to a 1.5% reduction in the production
efficiency of the freight transport sector. Grzelakowski [52] found that during the firstmonths of 2020, the global maritime shipping sector, and specifically container transport,
was affected by the lockdown and the resulting congestion in port terminals because of
the inability of carriers to pick up freight from seaports and, thus, cargo ships became
floating warehouses. In the middle of the third quarter of 2020, the supply chains become
more stabilized, the demand on freight transport increased and, consequently, the spot
freight rates also increased significantly. This was apparent in container shipping at the Far
East–USA trade lane, as well as the routes towards South America or West Africa and the
Middle East–Western Europe trade lanes.
On the bright side, COVID-19-related restrictions have led to a reduction in fuelprices. On 20 April 20 2020, the price of West Texas Intermediate crude oil dropped by
42.5% [61]. Fuel price accounts for approximately 15 to 20% of the total operating cost of
the transport industry. The IATA reports that in July 2020, fuel prices are 45% lower thanthe price during the same period in 2019, resulting in a saving of nearly USD 70 billion for
the airline industry [51]. Regardless of the drop in oil prices, travel restrictions and low
demand for transport services result in a marginal profit for freight transport providers [6].
Some countries adopt policies, e.g., tax reduction, to reduce the operating costs of logistics
companies. For example, China exempted logistics enterprises from vehicle tolls andvalue-added taxes on income due to delivering essential living supplies [8].

•Delivery performance

Nine studies report the impact of the COVID pandemic on the delivery performanceof different freight transport modes. COVID-related restrictions exert the most impact on
the delivery performance of maritime and air transport sectors. This is because these two
sectors apply some quarantine virus containment measures to shipments, vessels, and crew.
Several importing countries impose a 14-day quarantine for containers upon arrival at theport, resulting in a lengthy cycle times of containers [32]. In addition, COVID-19-related
shortage of transport capacities, e.g., workforce, vehicle capacity, and empty containers,
increase the delay in freight delivery [47,48]. Koyuncu, Tavacioğlu, Gökmen and Arican [54] suggest that the COVID-related restrictions on production and trade may break theinterconnected supply chain; thereby, serious losses might be faced in some container lines.
Due to restricting people’s mobility, several cities worldwide witness reduced roadtraffic. Therefore, some studies report positive effects on road freight transport, such as
fewer traffic jams, increased efficiency, and fewer emissions [20]. For example, until theso-called “new normal” (11/05/2020), road traffic drops, on average, down to 76.28% in
Madrid [27], 73% in UK [62], Sanpaolo (−55%), New York (−74%), Milan (−74%), Barcelona(−73%), Stockholm (−48%) [40]. The reduced road traffic causes delivery times to be much
shorter [6]. Since the pandemic reduces demand for all nonessential products, access ofessential products to seaports, rails, and trucks has generally improved [32]. However,finding truck parking is more difficult, especially for a large fleet [53]. This is because mostemployees are working from home and their private cars are occupying the parking slots
in the streets. During the pandemic, many shops, e.g., pharmacies, grocery stores, andbakeries, start pickup and home delivery options in an attempt to stay in business. Thus,
the efficiency and quality of last-mile logistics services are impaired [8,55].

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Int. J. Environ. Res. Public Health 2022, 19, 12287•

Emissions

Four studies report the impact of COVID-19 on the emissions from different freighttransport modes. The COVID-19 pandemic creates an opportunity for achieving some
temporary sustainability gains in the transport sector. This is mainly due to the reduction
in carbon emissions, mainly because of mobility restrictions. However, this might be offset
by increasing demand for cargo transport [5]. Few studies report the reduction in CO2emissions from freight transport during the pandemic [5,28,38,56]. In China, COVID-19
significantly reduces industrial and transport activities, resulting in lower CO2 emissions.
During the first quarter of 2020, freight transport has a 22.3% reduction in CO2 emissions, while passenger transport is 59.1% compared with the same period of 2019 [56]. In
Columbia, mobility restrictions have created positive side effects regarding externalitiesof transport, such as less traffic safety issues, noise, and pollution [28]. In Croatia, some
airports have an average reduction of 96% in flights in April 2020 compared to April 2019.
This directly affects the reduction in CO2 emissions by a factor of 1.81 for the commercial
airport and 3.49 for the seasonal airport [38].
4.1.2. Transport Mode
Table 5 classifies the 32 studies based on transport mode. Table 5 shows the studies
that investigate the COVID-related impacts on only one individual transport mode, i.e.,road (10 studies), air (7 studies), and seaport (9 studies). In addition, three studies address
the impacts on two or more transport modes, while six studies discuss the impacts on thefour modes or from a general perspective without focusing on a specific transport mode.
It can be noted from Table 5 that road freight transport is the most studied mode, while
rail freight transport is rarely studied. This may be because road freight transport is thedominant transport mode in most countries compared to other freight transport modes.
Table 5. Classification of COVID-19-related impact studies based on transport mode.
Transport Mode (s)Number of Studies per
Transport Mode (s)References

Road
Air
SeaportAir and road
Road and rail
Road, sea, and rail
All modes (General)

10
5
8
1
1
1
6

[11,27,29,30,35,41–43,46,56][5,6,34,36,38][7,9,33,37,39,40,52,54]
[28]
[45]
[20]
[31,32,44,48,49,55]

4.1.3. Research Methods
Regarding the research methods, the identified literature employs different methods tostudy the COVID-related impacts. These methods are grouped into four method categories:(1) empirical, (2) quantitative, (3) literature review, and (4) mixed methods. In addition,
each method category includes a number of specific methods, as shown in Table 6. The mostfrequently used method category is the quantitative method (secondary data analysis andregression, forecasting, and mathematical models), followed by empirical methods (casestudies, questionnaires, and interviews). Only one study conducts a systematic literature
review on the types of crises that affect Europe in the previous 20 years [20]. Some studies
employ mixed methods by merging two different methods to improve and validate theoverall results [37,48]. For example, Narasimha, Jena and Majhi [37] demonstrate thenegative impact of COVID-19 on four Indian seaports using sets of secondary data, i.e.,cargo transshipment data of the year 2020. To obtain more insights into the results, theycollect the opinions of some experts on the results.

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Int. J. Environ. Res. Public Health 2022, 19, 12287

Table 6. Classification of studies on the impact of COVID-19 based on research method.
Main Method Category

Number of Studies per
Main Method Category

Specific Method

Number of Studies
per Specific Method

Empirical methods

8

Case studies
Questionnaires
and interviews

3[30,34,44]

5[32,35,44,49,53]

Secondary data analysis

15

Regression models
Forecasting models
Analytical models

43
4[5,6,27–
29,38,40,41,43,45,47,50–
52,56][7–9,33][36,39,54][11,31,42,55]

References

Quantitative methods

21

Literature review methods

1

Systematic review

1

[20]

2

Questionnaires and second
data analysis
Review and second
data analysis

1

[37]

1

[48]

Mixed methods

4.1.4. Research Gaps and Trends for Future Research
Reviewing the 36 studies shows a variety of interesting paths for future researchas follows:

•

•

•

•

•

Since most reviewed studies focus on the peak period of the COVID-19 pandemic(January–June 2020), more research is needed to analyze the impacts of COVID-19
on each transport mode, starting from the beginning of the so-called “new norm”(July 2020). This will identify the strengths and weaknesses of each transport mode
under the different “new norm” policies adopted to reopen society. It is also of great
importance to qualify the analysis with some statistics showing how different “newnorm” policies affect the KPIs of the individual transport modes. This will support
policymakers and practitioners in learning from past events and in formulating better
policies for unexpected future pandemics.
There is a lack of studies investigating the impacts of COVID-19 on freight transport
in developing countries. Therefore, further research may evaluate the impacts of
COVID-19 on freight transport in developing countries and compare them with thosein developed countries. Additionally, it may be relevant to compare different control
measures implemented by these countries and to assess the impacts of these measureson the performances of freight transport.
Many studies report severe pressure exerted by the pandemic on city logistics [6,8,11,27].
Therefore, future research may examine the resilience of specific city logistics initiatives,e.g., smart lockers, collection points, etc., during the early and “new norm” periodsof the pandemic, where new buying habits of consumers emerge and will need more
resilient and efficient urban logistics. This will help policymakers and practitioners
in defining better initiatives and relaxing resisting regulations for possibly occurring
waves of the pandemic.
Some studies propose prediction models for evaluating the impacts of COVID-19 on
freight transport performances during 2020 [36,39,54]. Future research may extend
these studies by comparing the predicted results with the actual results. Furthermore,
it is relevant to develop a better understanding of the main factors that may lead to any
deviations between both results. This is expected to guide researchers in developing
more robust prediction models for future unexpected pandemics.
Most studies evaluate the economic impact of the pandemic on freight transport whilevery few studies study the social and environmental impacts [5,28,38,56]. Therefore,
future research may be directed at measuring the social and environmental impactsof the pandemic on individual freight transport modes in general, and urban freightdeliveries in particular. Furthermore, existing studies use only traffic data in estimating
the carbon emissions from freight transport during the pandemic. Hence, future
research may give a complete picture by calculating the reduction in CO2 emissions
concerned with the utilization of different transport facilities and infrastructure.

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Int. J. Environ. Res. Public Health 2022, 19, 12287•

Most studies provide pieces of evidence of the impacts of COVID-19 based on data
analysis from a macro perspective [5,6,27–29,38,45,56]. Therefore, future research
may adopt various methods based on empirical data analysis from a micro perspective to develop more managerial insights by answering several questions of interest,for example:
1.
2.
3.
4.

•

What are the short- and long-term impacts of the pandemic on different KPIs of
logistics companies?
What are the causal relationships among the various impacts of the COVID-19 pandemic?
How do the impacts of the COVID-19 pandemic vary among logistics companies
handling different freight or serving different industrial sectors?
How can logistics companies deal with the pandemic and to what extent do support
policies from the governments help them to alleviate the impact of COVID-19?

Further avenues for future research may address developing simulation models toevaluate the long-term effects of different pandemic control policies on freight transport modes. This can have a significant value for policymakers, since the pandemic is
still ongoing and will probably have multiple waves caused by new virus variants. In
developing models, researchers can greatly benefit from the findings of the literatureon the early stage of the pandemic.

4.2. COVID-19-Related Mitigation Strategies
In response to the current pandemic, some studies propose various mitigation strategies to minimize the impact of COVID-19 on the different modes of freight transport.
By accurately investigating existing literature, we notice a limited number of studies,around 20 studies, discussing mitigation strategies. These strategies include (1) usageof autonomous delivery vehicles (ADVs), (2) deployment of drone delivery, (3) relaxing existing regulations, (4) utilization of mobile warehouses, (5) engagement of large
ships, (6) applying quantity discounts, (7) capacity augmentation, and (8) mixed strategies.
Table 7 summarizes the mitigation strategies discussed in the literature, and the impacts tobe mitigated. As in the previous section, the studies identified here are classified according to the mitigation strategy, transport mode, and the research methodology. In the last
subsection, the research gaps, along with future research directions, are elaborated.
Table 7. Mitigation strategies for minimizing the impact of COVID-19.
COVID-19-Related Mitigation
Strategies
Usage of autonomous delivery vehicles
Deployment of drone delivery
Relaxing existing regulations
Utilization of mobile warehouses
Engagement of large ships
Application of quantity discounts
Capacity augmentation
Mixed strategies

Impact to Be MitigatedStudies per Strategy

References

Driver capacity shortage, emissions
Driver capacity shortage, delivery
performance, emissions
Capacity shortage and operating cost
Delivery performance
Container capacity shortage
Demand for freight transport service
Driver capacity shortage,
delivery performance
Driver capacity shortage, delivery
performance, emissions

6[10,63–67]

5[68–72]3
1
1
1[8,59,60]
[11]
[73]
[74]

2

[75,76]1[77]

4.2.1. Mitigation Strategies•

Usage of autonomous delivery vehicles (ADVs):

ADVs are self-driving electric ground vehicles that travel at a restricted speed, around
5–10 km/h on roadways or sidewalks. Using ADVs as a mitigation strategy can reducethe impact of driver capacity shortage, as ADVs are self-driving vehicles, and enhance
the positive impact of emission reduction, as ADVs move at a low speed releasing a44

Int. J. Environ. Res. Public Health 2022, 19, 12287

small amount of CO2 . There are six studies that focus on ADVs as a mitigation strategy
on road freight transport. Kapser, Abdelrahman and Bernecker [63] investigate user
acceptance of ADVs during COVID-19 through the acceptance unified theory. This study
is conducted based on real data collected from Germany using online questionnaires.
Findings reveal that the user acceptance rate is slightly low, due to a high shipment cost
resulting from the expensive technology used in ADVs. Similar to Kapser, Abdelrahman
and Bernecker [63], Pani, Mishra, Golias and Figliozzi [67] study the public acceptance of
ADVs, but in Portland, USA. Based on a sample of 483 consumers, results indicate that the
majority of US consumers, around 61.28%, are willing to pay extra to receive their goods
using ADVs. Chen, Demir, Huang and Qiu [10] discuss the idea of food delivery using a
traditional vehicle along with robots to serve customers in the last miles. This study showsthe efficiency of using these robots in minimizing the contact between the customers anddrivers. Besides delivering food products, as shown in the previous studies, Ozkan andAtli [66] present the usage of ADVs in transporting PCR testing samples between hospitals
and laboratories. To this end, a mathematical model is presented as a mixed-integer linear
programming model (MILP), considering the payload of the ADVs. Using a real casein Istanbul, including 219 hospitals and 23 laboratories, the proposed model shows its
efficiency in transporting around 25,000 samples.
Although some studies focus on the usage of ADVs to mitigate the impact of driver
shortage capacity, other studies evaluated the usage of ADVs to mitigate the impact ofgas emission of traditional vehicles. Li, He, Keoleian, Kim, De Kleine, Wallington andKemp [64] study the greenhouse gas emission of automated vehicles (last mile) assistedwith robots (last 50 feet) used for last-mile delivery. Their results show the outperformanceof the automated vehicles with robots over the traditional option (traditional vehicle withhuman for final delivery) in minimizing the gas emissions.
ADVs are also technically discussed, especially the hardware design. Liu, Liao, Gan,
Ma, Cheng, Xie, Wang, Chen, Zhu, Zhang, Chen, Liu, Xie, Yu, Guo, Li, Yuan, Han, Chen,
Ye, Jiao, Yun, Xu, Wang, Huang, Wang, Cai, Sun, Liu, Wang and Liu [65] assess the designof the hardware of ADVs, particularly the chassis and autonomous driving-related devices.
Based on a real case study containing 25 ADVs, these ADVs are successful to run a total
distance of 2500 km, satisfying 676 delivery tasks.

•

Deployment of drone delivery

Technically, drones are teleoperated flying machines that do not need continual operator control. In real practice, drones often fly using batteries, which help in carrying goods.
These features have attracted the attention of some companies such as UPS and AMAZON
to use drones for delivery [78]. This inspires the usage of drones as a mitigation strategy
to reduce the impact of driver capacity shortage, improve the impact of delivery performance, as drones are fast transport means, and enhance the positive impact of emission
reduction, as drones do not emit CO2 . Four studies in the literature report the usage ofdrone delivery as an alternative to road freight transport. Kunovjanek and Wankmüller [69]study the viability of using existing drone infrastructure to deliver COVID-19 viral test to
infected people in Australia. This study reveals the viability of using the drone in termsof time and financial measures. However, transport policy needs to be relaxed to permit
widespread usage of this service. It is somewhat challenging to establish a legal framework
for this service. Alsamhi, Lee, Guizani, Kumar, Qiao and Liu [68] introduce a conceptual
framework of integrating blockchain with drone delivery. With the help of a consensus
algorithm, the blockchain is fruitful in easing the collaboration among multi-drones by
sharing the delivery information among them. Thus, the delivery time is reduced, resulting
in an improvement in delivery service. Yakushiji, Fujita, Murata, Hiroi, Hamabe andYakushiji [71] assess the usage of drones to transport medical supplies in Japan during
traffic blockages. Similarly, Quintanilla García, Vera Vélez, Alcaraz Martínez, Vidal Ulland Fernández Gallo [70] investigate the efficiency of using drones as extra support for
healthcare logistics in Valencia, Spain. These studies indicate a successful application ofdrones in transporting medical goods without needing a dedicated infrastructure. Bathke,45

Int. J. Environ. Res. Public Health 2022, 19, 12287

C, x00Fc, nch, Gracht and Hartmann [72] assess some resilience strategies in the domain ofmaritime container shipping with the help of 51 maritime experts. The results indicate thatpicking the low-hanging fruit requires the deployment of drones to improve resilience inthe maritime container shipping domain.

•

Relaxing existing regulations•

Utilization of mobile warehouse•

Usage of large ships•

Application of quantity discount•Capacity augmentation

COVID-19 prevention and protective measures (e.g., multiple drivers are prevented
from using the same truck) increased transport costs. Some countries, such as Canada,
increased the maximum hours of service for drivers [60]. In response to air freight capacity
shortages, passenger aircraft are granted exemptions from July 2020 to July 2021 to carryfreight in the main cabin when no passengers are being carried [59]. To reduce the operating
costs of logistics companies, some companies, e.g., in China, exempt logistics enterprises
from vehicle tolls and value-added taxes on income due to delivering essential living
supplies [8]. The European Union relaxed some rules of the competition law to allow
competing companies to cooperate, which might help them overcome capacity shortages
or better respond to disrupted supply channels [79].
The mobile warehouse is simply a truck that covers a specific geographical location,
carrying an inventory of some products to be sold to the customers according to the demandof these products. Utilization of the mobile warehouse as a mitigation strategy mitigates
the impact of delivery performance as the mobile warehouse reaches near the customers’
locations. This strategy has not received much attention from researchers, with a single
study reported in road freight transport. Srivatsa Srinivas and Marathe [11] are among thefirst to propose the utilization of mobile warehouses during COVID-19. Using such means
can aid B2C e-commerce, which can minimize store congestion during COVID-19.
The main rationale of using this strategy is to satisfy the high demand for goods witha fewer number of ship trips, limiting human movement, and, therefore, control the spreadof COVID-19. In addition, using large ships as a mitigation strategy can reduce the impact
of container capacity shortage, as using large ships can fix the problem of returning empty
containers to the importing companies. This strategy is discussed in the study by Pasha,
Dulebenets, Fathollahi-Fard, Tian, Lau, Singh and Liang [73], who develop a mathematicalmodel to determine the service frequency and ship sailing speed and schedules. By using
10 real cases, the proposed strategy demonstrates its efficiency in satisfying food demand
while improving the total turnaround profit by approximately 22.94%.
Due to COVID-19, cargo demand in some routes is disrupted (e.g., demand in someroutes exceeds the capacity of the aircraft (hot selling), whereas the demand in otherroutes is not sufficient to fill the aircraft capacity (underutilized)). This disruption results
in a problem, called demand imbalance, or demand shortage for air freight transport.
Shaban, Chan and Chung [74] propose a game-theoretic model to make a balance between
the hot-selling and underutilized routes. The balance can be achieved by applying aquantity discount in the underutilized routes to attract some demand from hot-sellingroutes, ultimately leading to fixing the imbalance. To summarize, using this strategy canhelp to mitigate the impact of demand shortage for air freight transport.
The main concept of this strategy is that, instead of reserving the normal capacityof the product, the logistics facilities temporarily increase their reserved capacity. This
extra reserved capacity will help face disruptions that happen during COVID-19. Aloui,
Hamani and Delahoche [75] study this strategy by proposing a two-stage stochastic mixedinteger programming model that helps make some decisions related to location-allocation,
inventory, and routing planning. Schofer, Mahmassani and Ng [76] conduct in-depth
46Int. J. Environ. Res. Public Health 2022, 19, 12287

interviews with Rail Intermodal Freight leaders to figure out how to efficiently enhance theresilience of the U.S. rail industry. The results of these interviews reveal the importance of
capacity augmentation in improving the overall freight system performance.

•

Mixed strategies

In the previous sub-sections, all the studies focus on applying a single strategy. However, some studies focus on studying more than one strategy. Simić, Lazarević and Dobrodolac [77] apply a fuzzy method to select the best transportation means among drones,
ADVs, cargo bicycles, and Postmates for the last-mile delivery during COVID-19. By presenting a real case in Belgrade, results indicate that “postmates” is the best mode, followedby drones and ADVs.
4.2.2. Transport Mode and Research Methods
In this subsection, we classify the studies on mitigation strategies based on transportmodes. Table 8 indicates that “road” has been extensively investigated by scholars in
14 studies. On the contrary, “air” and “seaport” modes are barely discussed in the literature,as they only appear in four studies. The road is the most commonly used mode of transport
worldwide. Therefore, it is frequently investigated, which is also observed in the COVID-19
impact studies.
Table 8. Classification of COVID-19-related strategies studies based on transport mode.
Transport Mode (s)Number of Studies per
Transport Mode (s)References

Road
Air
Seaport and waterways

142
2[8,10,11,60,63–71,75–77][59,74][72,73]

The research methods used in the studies of mitigation strategies are also reviewed.
For this purpose, these studies are divided into two categories: empirical and quantitative,
such that each category is further sub-categorized to other methods, as shown in Table 9.
Table 9. Classification of studies on COVID-19-related strategies based on research method.
Main Method Category

Number of Studies per
Main Method Category

Specific Method

Number of Studies per
Specific Method

References

Empirical methods

10

Case studies
Questionnaires and interviews

4
6[59,64,70,71][8,60,63,67,72,76]

10

Optimization
Simulation optimization
Analytical model
Fuzzy method
Game theory

5
2
1
1
1

[10,65,66,68,73][69,75]
[11]
[77]
[74]

Quantitative methods

Table 9 reveals that both the quantitative method and empirical methods are equallyused research methods, each one appearing in ten of the 20 studies. Indeed, for quantitative
methods, five articles focus on optimization methods using different approaches such as
two-stage meta-heuristics algorithm [10], CPLEX [66], deep-learning-based algorithms [65],and recursive route re-composition heuristic [73]. The remaining quantitative methods,
including simulation optimization, analytical model, fuzzy method, and game theory, areexplored in the rest of studies.
Empirical methods are discussed in 10 out of 17 articles. In particular, four studies
use the case studies method. For instance, Li, He, Keoleian, Kim, De Kleine, Wallington
and Kemp [64] present a case study to assess greenhouse gas emissions of automatedvehicles. In addition, Yakushiji et al. [10] assess a real case study on flying drones totransport medical supplies. The other six studies rely on questionnaires and interviews toinvestigate user acceptance of ADVs [63,67] and assess the technologies that help increase47

Int. J. Environ. Res. Public Health 2022, 19, 12287

resilience efficiency at maritime container shipping companies [72]. The limited numberof empirical studies indicates that scholars are still faced with the challenges of acquiringand investigating real data. However, the existing empirical studies help to understandthe attitudes of people towards the usage of new transportation tools such as ADVs anddrones [63,67,71].
4.2.3. Research Gaps and Trends for Future Research Directions
After investigating the 20 articles, which focus on the mitigation strategies, someresearch gaps along with future research directions are identified as follows:

•

•

•

•

•

•
•

•

There is a low user acceptance of ADVs in Germany [63]. Indeed, ADVs are still
immature; therefore, the results presented in these studies are largely premature. Accordingly, more research is required to check the user acceptance of ADVs after thematuration phase. In addition, limited factors, such as age, gender, and citizen income,are considered in evaluating the user acceptance of ADVs [63]. To generalize these findings, more factors, such as delivery distance and time, need to be investigated. Besides
Germany, user acceptance of ADVs has only been evaluated in Portland, USA [67].
The investigation is limited to a metropolitan area with small data size, restricting theknowledge of public acceptance of ADVs. In this connection, considering multiple
US states with larger data set is imperative to test the public acceptance of ADVs. In
addition, making the comparison between ADVs and drones is suggested to see theadvantages and disadvantages of each transportation means.
Robots have been shown to help in minimizing the contact between customers anddrivers of traditional vehicles [10]. Here, the only drawback may include overlooking
some features of the robots, such as energy consumption and operational cost, which
are likely to limit a large-scale deployment of robots. Accordingly, these features
should be incorporated in future research to arrive at a better assessment of theapplicability of robots.
Utilization of ADVs with robots is also asserted to minimize greenhouse gas emissions [64]. However, some considerations which are known to affect gas emissions,such as traffic and weather conditions, should be incorporated into future studies toaccurately determine greenhouse gas emissions.
The viability of using existing drone infrastructure, based on time and cost measures,has been verified in Australia [69]. However, many factors, such as legal visibility andopinion of policymakers, have not been considered. For a better verification process,these factors should be investigated in future research.
Although drones have been used to transport medical supplies in Japan during traffic
blockages, it has only been during favorable weather conditions, such as low windspeed and no rain [71]. Therefore, it is imperative to investigate the usage of drones indifferent weather conditions to further evaluate their applicability.
Similarly, drones can successfully transport medical goods without the need of specialized infrastructure in Spain [70]. However, this application is accomplished only
on a small scale. To enable a large-scale deployment, more investigation is needed.
The deployment of mobile warehouses has been evaluated by Srivatsa Srinivas andMarathe [11]. Indeed, this study lacks an accurate estimation of product demand. For
this purpose, we suggest using data analytic techniques to accurately estimate theproduct demand. In addition, this study overlooks the dynamic routing and parking optimization of the mobile warehouse. Therefore, investigating this overlooked
optimization problem during COVID-19 could be another research direction.
The deployment of large ships is efficient in satisfying food demand [73]. The main
pitfalls of the study include ignoring fluctuations in demand, sea weather conditions,and sailing time of ships, that are typically experienced in real practice. In this connection, it is suggested to develop a stochastic model while considering all uncertain
factors to accurately capture this reality.

48

Int. J. Environ. Res. Public Health 2022, 19, 12287••

Applying quantity discounts appears in the study by Shaban, Chan and Chung [74]while considering deterministic demand. Since demand usually fluctuates, it will be
more reliable to consider the stochastic demand while investigating quantity discounts.
The planning horizon of the study is quite limited, around one month, which limits
the generalization of the gained results. To avoid this situation, it is recommended to
study a longer planning horizon (i.e., one year).
Most studies have presented mitigation strategies for “road” mode, overlooking air
and sea modes. Accordingly, more research is required to develop different mitigation
strategies for air and sea modes.

4.3. Recoverability Measures Related to COVID-19
As stated earlier, the COVID-19 outbreak has had a great impact on freight transport.
Therefore, a research stream has studied and evaluated this impact. Then, another searchstream has studied measures aimed at counteracting the impact of COVID-19. So, several
strategies and policies have been developed to mitigate these impacts. In addition, it
is necessary to study the ability of freight transport to recover from the disruption of
COVID-19. In this regard, this section summarizes the literature which have studied themeasures of recovery of freight transport, during or after COVID-19. Although this stream
is still limited and needs further work, there are 15 articles in the existing literature thathave assessed various recovery measures using several research methodologies.
4.3.1. Recovery Measures
Few studies tackle the ability to recover in freight transport. However, they adopt
different measures of recovery for freight transport, as shown in Table 10. These measures
include quality, efficiency, sustainability, capacity, and rate of recovery. The quality as arecovery measure is investigated by Gnap, et al. [80], who consider the quality of transportation infrastructure as fundamental to strengthening the recovery of road traffic andhigh-speed trains after COVID-19. Likewise, Tardivo, et al. [81] conclude that the qualityof the management system is the most important measure for recovery. However, bothstudies have not given numerical recovery solutions which can be used in the future tojudge the resilience of freight transport. van Tatenhove [82] and Munawar, Khan, Qadir,Kouzani and Mahmud [6] adopt the efficiency measure to study the recovery of freighttransport in Europe and Australia. Moreover, sustainability measures are used to study therecoverability of freight transport during COVID-19. Guo, et al. [83] adopt performancemeasurement techniques to study the recoverability of the aviation industry. Four studies
have used at least one sustainability aspect. For example, the socioeconomic aspects areused to study the long-term impact and government role to contribute to the recovery
from COVID-19 [84]. Additionally, the economic and environmental aspects are adopted
to evaluate the recovery of road transport after the usage of plug-in electric vehicles [85].
Bartle, Lutte and Leuenberger [5] and Akyurek and Bolat [86] add the social measure to
economic and environmental aspects to study the recovery in air transport. However, very
limited research has tackled the capacity and the recovery rates as measures of recovery.
Notteboom, et al. [87] study the adaptive capacity of container shipping lines. The authors
compare the capacity of the sea shipping transportation to recover in the era of COVID-19
with the capacity used to recover during the economic crisis of 2008 to 2009. They claim thatsea shipping is stable during COVID-19 because the shipping liners adjust their fleets when
demand drops. Gudmundsson, et al. [88] and Cheong, et al. [89] evaluate the recovery ratesof the air transportation profits and demand, respectively. However, using only one single
measure to study the recovery is not enough to address the recoverability of any industry.
A generic study used an integrated grey decision-making trial to rank the recovery-basedsustainability measures for the freight industry regardless of transportation modes

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Int. J. Environ. Res. Public Health 2022, 19, 12287

Table 10. Summary of recovery measures.
Recovery Measure

Study per Measure

References

Quality
Efficiency
Performance
Sustainability
Capacity
Recovery rate

2
3
1
5
2
3

[80,81][6,82,90][83][5,84–86,91][87,90,92][88–90]4.3.2. Research Methods and Transport Modes
This subsection discusses the research methods which have been used to study therecoverability of different transportation modes amid COVID-19. As shown in Table 11,
11 studies have adopted several research methods to explore the recoverability of different
transport modes for freight transport. Although the number of articles is very limited on
this topic, several methods, including empirical, quantitative, and mixed methods, havebeen used. For example, three empirical methods are adopted: one to create a framework
for the future of a freight transport mode [5], one used a scenario-based approach [82],and another one used index-based evaluation techniques to measure the resilience oftransportation mode(s) [90]. As for the quantitative methods, comparative analysis [86],forecasting the recovery of demand [88,92] and profit [89], have also been evaluated. The
rest of the studies use mixed methods, such as simple data acquisition and experts’ opinions,to assess the current status and comment on the possibility of recovery [84,87]. Nevertheless,data acquisition is not the only mixed method used; the analytical hierarchy process has
also been used to integrate the economic and environmental criteria to evaluate public
transportation systems [5,85].
Table 11. Classification of research methods based on the transportation mode.
Transportation Mode

Main Method Category

Number of Studies
per Main
Method Category

Specific Method

Empirical methods

3

Framework study
Scenario-based research
Index-based evaluation

X

Quantitative methods

5

Forecasting
Comparative analysis

X
X

Mixed methods

8

AHP multi-criteria
decision making
Correlation analysis
Data acquisition

X
X
X

Road
and Railways

Seaports
and Waterways

X
X
X

X

Air

References

X

[5]
[82]
[90]

X
X

[88,89,92][86,91]

X[83,85]X[80][5,6,81,84,87]

On the focus of transport modes, Table 11 shows that all the transport modes arealmost equally studied, as each transport mode is studied by around three to four studies.
This observation is contrary to the observation noticed in the impact and mitigation parts.
4.3.3. Research Gaps and Trends for Future Research
Although the COVID-19 pandemic has caused severe disruptions to the freight transport sector, the number of articles does not reflect the criticality of these disruptions.
Additionally, the 11 studies examined here can be considered as an initial step towards
the recoverability of freight transport, and some future directions can be recommended
as follows:
Although there is no numerical evidence that the air transport mode is the mostaffected amongst the other modes, lockdown of international borders has significantly
affected air transport. Most of the studies have focused on the amount of impact, but not
many studies tackled the ways to recover after the lockdown. Therefore, more future work
is still needed to enhance the recoverability of the air freight transport mode, not only forthe post-COVID era but also for any unexpected long-term disruptions.
50Int. J. Environ. Res. Public Health 2022, 19, 12287

The continuous mutation of COVID-19 causes huge disruption in freight transport
domestically and internationally. Therefore, further studies are suggested to study therecoverability of freight transportation and the way to improve the resilience of each mode
is a great challenge to be considered.
Despite the fact that the existing studies have suggested different measures for recovery, a recovery index that uses multiple measures is required to evaluate the recoverabilityof freight transport. The recovery index may be developed either by freight transportmodes in total or for each mode separately.
5. Conclusions
The existing review studies lacked a detailed analysis regarding freight transport
during COVID-19. Therefore, this study contributed by conducting a comprehensive
review in the domain of freight transport to identify some research gaps and propose future
research directions. To do so, 68 studies were collected and analyzed using scientometricand systematic review approaches.
The scientometric analysis revealed that: (1) the combination of China, Hong Kong,and India published the highest number of papers on freight transport during COVID-19,
whereas authors from Canada and the United States had the highest citations worldwide;(2) Sustainability (Switzerland) and Science of Total Environment were the top-cited journals,
while the Canadian Journal of Agricultural Economics was the most influential journal inthis research area. Lastly, the analysis of the keywords showed that “COVID-19”, and“Freight transport” appeared frequently, reflecting the focus of researchers on these areas.
Meanwhile, some keywords such as “Airline industry” and “Cargo” were rarely used,indicating the need for future research in these areas. The systematic review was carriedout by categorizing the studies into three main themes, including impact-related studies,
mitigation strategy-related studies, recoverability-related studies. The results reveal thatthe most studied theme was related to the impacts of COVID-19, followed by mitigation
strategies, and then recoverability. Each theme was discussed alongside types and different
transportation modes (i.e., road, air, and sea) and solution methods adopted.
Our review showed that 36 studies reported several impacts of the COVID-19 pandemic on freight transport. These impacts were categorized into five main groups, which
included demand for freight transport services, transport capacity, operating cost, delivery
performances, and carbon emissions. Surprisingly, not all reported impacts were negative.
Negative impacts included, for example, reduced demand for transport services, higher
operating costs, transport capacity shortages, delayed delivery, and difficulties in finding
parking areas. On the contrary, some studies reported some positive impacts, such asincreased availability of truck drivers and lower operating costs, increased demand fortransport services, tax reductions on the service of logistics companies, fewer traffic jams, increased efficiency, and fewer emissions. Most of these studies discussed the demand-related
impact, followed by impacts on operating transport, transport capacity, delivery performance, and carbon emissions. Regarding the transport mode, road freight transport andseaports were the most evaluated modes, followed by airfreight transport, while rail freighttransport was rarely addressed. Regarding the employed research methodologies, themost frequently used methods were secondary data analysis, followed by questionnaires
and interviews.
After reviewing 20 studies focusing on mitigation strategies, we observed eight typesof mitigation strategies, including (1) usage of autonomous delivery vehicles (ADVs),(2) deployment of drone delivery, (3) relaxing existing regulations, (4) utilization of mobile
warehouses, (5) engagement of large ships, (6) applying quantity discounts, (7) capacity
augmentation, and (8) mixed strategies. It was noticed that the most studied strategy was
the usage of ADVs with six studies, whereas the least studied strategies were strategies 4 to
8, with each strategy appearing in one or two studies. After that, the studies were classified
based on the transport mode, with the most frequent transport mode in 14 studies being
the road. The least frequent modes were air and seaport, each mode occurred in one or two

51

Int. J. Environ. Res. Public Health 2022, 19, 12287

studies. The last classification was based on the research method, such as empirical methods
and quantitative methods. This classification indicated that both quantitative methods andempirical methods were equally used, such that each one appeared in ten studies.
In light of the undesirable impacts of pandemics and the mitigation strategies toovercome these impacts, it was necessary to study the recoverability of freight transport inits three modes. Nevertheless, only 15 studies covered the recoverability topic, discussing
six recovery measures, such as quality, efficiency, sustainability, performance, capacity, andrate of recovery. Most of these studies, 5 out of 15 studies, focused on sustainability as
a recovery measure, while little attention was paid to the rest of the recovery measures.
In addition, the recoverability studies discussed all the transport modes with the sameattention, as each transport mode was studied by an average of six studies. The recoverability studies were discussed using different research methods, such as empirical methods,
quantitative methods, and mixed methods. It was observed that the most studied method
was the mixed method (i.e., correlation analysis and data acquisition), which comprised
approximately 8 out of 15 studies, while the rest of the methods received almost the sameattention from scholars, from two to four studies for each method.
After reviewing the studies on the different themes, some future research directions
were highlighted. Regarding the impact theme, studying the impacts of COVID-19 indeveloping countries was suggested. Focusing on the mitigation theme, more mitigation
strategies were needed for air and seaport transport modes, as their related strategies werelimited. Additionally, investigating dynamic routing and parking of mobile warehouses
were suggested. Based on the recoverability theme, it was recommended to develop robust
optimization models to deal with schedule disruptions and profit reduction. More detailed
research gaps and future directions are represented in Figure 6.
Although the novelty of this study has been highlighted, it is, nevertheless, importantto highlight some of its limitations. For instance, some scientometric analyses, such as themost prolific/active authors, were not provided to avoid making the study too long. Similarly, in the systematic review, we overlooked studies on freight transport before COVID-19.
It might be interesting research to study freight transport before and during COVID-19.

52

Int. J. Environ. Res. Public Health 2022, 19, 12287

Figure 6. Research gaps and future directions matrix.

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Int. J. Environ. Res. Public Health 2022, 19, 12287

Author Contributions: Idea for the article: A.K.; Literature search and bibliometric data filtration:
A.K. and I.A.S.; Scientometric analysis: I.A.S. and A.E.E.E.; Writing and drafting: A.K., A.E.E.E.,
E.-A.A. and I.A.S.; Critical revision of the work: A.K., A.E.E.E. and E.-A.A.; Funding acquisition: A.K.
and E.-A.A. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The search queries used in Scopus during the study are available fromthe corresponding authors on reasonable requests.
Acknowledgments: This study was supported by the Danish Research Center for Freight Transport,
Aalborg University.
Conflicts of Interest: The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported in this paper.

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International Journal ofEnvironmental Researchand Public Health
ArticleAssessing the Nationwide COVID-19 Risk in Mexico throughthe Lens of Comorbidity by an XGBoost-Based Logistic
Regression Model
Sonia Venancio-Guzmán 1 , Alejandro Ivan Aguirre-Salado 1, *, Carlos Soubervielle-Montalvo 2
and José del Carmen Jiménez-Hernández 1
1
2*

Citation: Venancio-Guzmán, S.;
Aguirre-Salado, A.I.;
Soubervielle-Montalvo, C.;
Jiménez-Hernández, J.d.C. Assessing
the Nationwide COVID-19 Risk in
Mexico through the Lens ofComorbidity by an XGBoost-Based
Logistic Regression Model. Int. J.
Environ. Res. Public Health 2022, 19,
11992. https://doi.org/10.3390/
ijerph191911992
Academic Editors: Sachiko Kodera

Institute of Physics and Mathematics, Universidad Tecnológica de la Mixteca,
Huajuapan de León C.P. 69000, Mexico
Faculty of Engineering, Universidad Autónoma de San Luis Potosí, San Luis Potosí C.P. 78280, Mexico
Correspondence: aleaguirre@mixteco.utm.mx; Tel.: +55-953-532-0214

Abstract: The outbreak of the new COVID-19 disease is a serious health problem that has affected alarge part of the world population, especially older adults and people who suffer from a previous
comorbidity. In this work, we proposed a classifier model that allows for deciding whether or nota patient might suffer from the COVID-19 disease, considering spatio-temporal variables, physicalcharacteristics of the patients and the presence of previous diseases. We used XGBoost to maximize
the likelihood function of the multivariate logistic regression model. The estimated and observed
values of percentage occurrence of cases were very similar, and indicated that the proposed model
was suitable to predict new cases (AUC = 0.75). The main results revealed that patients without
comorbidities are less likely to be COVID-19 positive, unlike people with diabetes, obesity andpneumonia. The distribution function by age group showed that, during the first and second wave of
COVID-19, young people aged ≤ 20 were the least affected by the pandemic, while the most affected
were people between 20 and 40 years, followed by adults older than 40 years. In the case of the thirdand fourth wave, there was an increased risk for young individuals (under 20 years), while older
adults over 40 years decreased their chances of infection. Estimates of positive COVID cases withboth the XGBoost-LR model and the multivariate logistic regression model were used to create maps
to visualize the spatial distribution of positive cases across the country. Spatial analysis was carriedout to determine, through the data, the main geographical areas where a greater number of positive
cases occurred. The results showed that the areas most affected by COVID-19 were in the central andnorthern regions of Mexico.

and Essam A. Rashed
Received: 12 July 2022

Keywords: coronavirus; comorbidity; spatial analysis; logistic regression; ROC curve

Accepted: 17 September 2022
Published: 22 September 2022
Publisher’s Note: MDPI stays neutral

1. Introduction

with regard to jurisdictional claims in

The COVID-19 pandemic has affected many countries worldwide. Among these, theUnited States, India, Brazil, the United Kingdom and Russia lead the list of countries withthe highest number of infected [1]. The outbreak of the SARS-CoV-2 virus was declareda pandemic in March 2020, due to its rapid dissemination and its negative effects acrossvarious countries [2]. After 18 months, the United States has reported 82,613,620 confirmed
cases and 999,842 deaths; India has reported 43,125,370 confirmed cases and 524,260 deaths;
Brazil has reported 30,701,900 confirmed cases and 665,216 deaths, among others. In thecase of Mexico (5,752,441 confirmed cases and 324,617 deaths), it is in 15th and 4th place regarding the countries with the larger number of positive cases and deaths, respectively [1].
Coronaviruses are a group of viruses that cause respiratory illnesses [3]. The mostcommon symptoms produced by the virus include fever, dry cough, tiredness and shortnessof breath, though, in more severe cases, it causes pneumonia or severe acute respiratory
syndrome (SARS) that may lead to death. In other cases, some people infected by the virus

published maps and institutional affiliations.

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

59

Int. J. Environ. Res. Public Health 2022, 19, 11992

do not develop any symptom, but they may still infect the rest of the population. According
to the World Health Organization (WHO), the coronavirus can be transmitted from aninfected person to others through droplets (also called aerosols) that are expelled whencoughing, sneezing or speaking, when shaking a sick person’s hand, or by touching objects
or surfaces contaminated by the virus [4].
The vulnerability of a person to COVID-19 is determined by various factors; among
these, the educational level, age, gender, social conditions, sociodemographic conditions,
housing conditions, and even psychological and emotional factors. In their recent study,
Ref. [5] analyzed the effects of living conditions on morbidity and mortality in the state of
Oaxaca. In this study, it was determined that the health of the population was seriously
affected by poor living conditions, and the lack of services such as electricity, gas, water, andhealth educational services. Therefore, several factors have contributed to the exacerbationof the coronavirus disease. Ref. [6] tested the hypothesis that social vulnerability in Mexico
contributes considerably to the probability of hospitalization of people sick from COVID-19.
To prove this, a cross-sectional study was carried out with public data from the General
Directorate of Epidemiology of the Ministry of Health of Mexico using 5-month period
data from individuals with coronavirus. The results show that patients with diabetes have
38.4% probability of being hospitalized, and for those patients whose suffer from someother comorbidity, the risk increases to 42.9% (42.2–43.7%).
Several studies have carried out for the evaluation of comorbidities associated withsevere and fatal cases of COVID-19. Ref. [7] reviewed 33 systematic studies and 22 metaanalysis, concluding that, of the total cases, 40% had comorbidities, while fatal cases had
about 74% had comorbidities. Hypertension, diabetes, and respiratory diseases were themost frequent comorbidities in severe and fatal cases. Ref. [8] founded that about 66.6%of the deaths caused by from COVID-19 were to men, with a mean age of 69.9 years.
In addition, a high incidence of hypertension, diabetes, low platelet levels, and chronic
cerebrovascular and cardiovascular diseases were observed among the deceased. Ref. [2]carried out a descriptive analysis of COVID-19 cases in Mexico with data obtained throughthe official website of the Ministry of Health in Mexico. The study included epidemiological,
demographic, and clinical characteristics of the patients who were confirmed positive by areal-time RT-PCR test. The results determined that most of the positive cases of COVID-19
occurred in Mexico City, the average age of the patients confirmed with the disease was
46 years old, most of these cases occurred in people between 30 to 59 years, and 58% of
all positive cases occurred in men. Regarding deceased patients, it was determined thatthey had one or more comorbidities, mainly hypertension, diabetes and obesity. Similarly,
Ref. [9] determined that the most influential factor in men and women was obesity, followedby diabetes and hypertension, as well as chronic kidney failure only in the case of women.
These findings indicate that these comorbidities were associated with the severity of thedisease and predispose to more serious complications of COVID-19.
Multivariate logistic regression has been widely used to identify variables associatedwith risks of coronavirus disease. Ref. [10] adjusted a multivariable logistic regression
models with 450 patients from the Massachusetts General Hospital (MGH). They foundthat, among patients with diabetes, 42.1% was admitted to the intensive care unit, or ICU,
37.1% required mechanical ventilation, and 15.9% died; while, among patients without
diabetes, 29.8% was admitted to ICU, 23.2% required mechanical ventilation, and 7.9% died.
In their results, they also found that diabetes and obesity were associated with greater odds
of ICU admission and mechanical ventilation. Ref. [11] conducted a study with 220 adultpatients with confirmed and suspected COVID-19 using multivariate logistic regression
and found that older age was an independent risk factor for mortality. In a study with
648 COVID-19-positive patients with a median age of 34 years, using multivariate logistic
regression, Ref. [12] found that independent risk factors for critical outcomes among
COVID-19 cases include old age, males, cardiac patients, chronic respiratory diseases,
and the presence of two or more comorbidities; all had significant p-values < 0.05. Inorder of predicting COVID-19 severity, Ref. [13] conducted a retrospective study with60

Int. J. Environ. Res. Public Health 2022, 19, 11992

287 patients of which 36.6% were classified as severe cases and 63.4% as non-severe cases.
They used 23 covariates on blood chemistry and obtained an accuracy of 85.2% and an
AUC of 0.928. Ref. [14] analyzed the survival probability of patients with COVID-19
in Mexico considering their characteristics and comorbidities. They carried out logistic
regression analyses and fitted nonparametric survival curves using the Kaplan–Meierestimator. A greater risk of death was associated with diabetes, obesity, hypertension, age
and gender, and at a lower degree to, obstructive pulmonary disease, kidney disease andimmunosuppressive diseases. Ref. [15] implemented two logistic regression models toinvestigate the rate of hospitalization and mortality against other variables. In the analysis,
10,544 records published by the Epidemiological Surveillance System of Respiratory Viral
Diseases of the Ministry of Health of Mexico (Ministry of Health, SSA) were used. The
results showed that the majority of the positive cases were men, being 54% times more
likely to be hospitalized than women. People older than 50 years and with two or more
simultaneous chronic diseases were prone to a higher risk of hospitalization and death.
Currently, there is a wide variety of statistical models and machine learning techniquesthat allow predictions to be made given a set of characteristics. Ensemble learning uses
simple models to form a more precise and efficient algorithm. Boosting is an ensemble
technique where simpler algorithms are used in a sequential way to take advantage of eachoriginal model in order to improve the precision of the subsequent model (Freund andShapire, 1996). Some assembly models that are based on the Boosting principle include the
AdaBoost [16], CatBoost [17], LightGBM [18] and Extreme Gradient Boosting (XGBoost).
XGBoost is an algorithm developed as a research project at the University of Washington
by [19] at the SIGKDD conference. This model was built by adding simpler models basedon regression trees. The optimization algorithm consisted on the gradient descent on an
objective function composed of the sum of individual loss functions of each observation.
These loss functions measure the distance between an observation and its prediction basedon the sum of its estimate in the previous iteration plus a new function that is added
sequentially in each iteration. These characteristics allow the global estimation of the model
to be scalable.
The XGBoost algorithm has proven its effectiveness and scalability over other methods
in recent studies. For example, Ref. [20] implemented XGBoost to predict PM2.5 concentration per hour. The method was compared to several algorithms including random
forest, multiple linear regression, decision tree, and support vector machines. Their results
showed that this algorithm superpassed all of these methods. Ref. [21] designed a real
driving task to extract data to model driving stress dynamics. They built a driver stressmanagement model based on driving behavior, environment and road familiarity, andperformed a cluster analysis with K-means to grouping observations of psychological anddriver stress data. The XGBoost was used to monitor stress within each group. Performancecomparisons revealed that the XGBoost model significantly transcended support vector
machines, random forest and gradient boosting decision tree.
In accordance with the current health emergency around the world, it is necessary tobuild models that can predict the characteristics of people at higher risk of being infectedwith COVID-19. Therefore, in this research work, we build an XGBoost model to predict
when the patient may or may not present the disease, considering risk factors such as
having previous diseases. For this, the proposed methodology to address this problem
is initially described. Subsequently, the description of the data is made, as well as themain results obtained from the descriptive statistics and the results predicted by the model.
Finally, a brief discussion and conclusion is made about the main results obtained, as wellas the scope and limitations of the work carried out.
2. Materials and Methods
The population of Mexico is approximately 126 million people, with a proportion of
51.2% and 48.8% of women and men (Population and Housing Census 2020, [22]), witha median of 29 years old. The country has 32 states, of which the state of Mexico has the61Int. J. Environ. Res. Public Health 2022, 19, 11992

largest number of inhabitants, while the state of Colima is the least populated. The data
used to carry out this study were collected for the entire country.
2.1. Available Data
The data for this study were obtained from the official website of the General Directorate of Epidemiology of the Ministry of Health of Mexico https://www.gob.mx/salud/
documentos/datos-abiertos-152127 (accessed on 10 May 2022). The records obtained fromthe database range from February 2020 to the end of April 2022, a total of 15,119,419 registered cases, and 40 explanatory variables. The set of variables used is shown in Table 1.
The only available geographic data were the name of the municipality. To obtain the geographic coordinates of the municipalities, a shapefile was used. In order to merge the data,
7,347,705 were eliminated from the database, this due to the presence of missing values
on the records and the presence of variables that represented individuals with duplicate
characteristics, thus leaving a total of 7,771,714 individuals to be considered for analysis; ofthese, 3,119,851 (40.14%) was confirmed as positive cases and the rest as negative cases.
Table 1. Explanatory variables used in the study.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

Variable

Description

Units

Input Data Source 1,2

Date
GenderAge
AsthmaDiabetes
Cardiovascular disease
COPD
Hypertension
Obesity
Chronic kidney disease
Smoking
Pneumonia
Other disease
Longitude
Latitude−0: Male, 1: Female−
0: No, 1: Yes
0: No, 1: Yes
0: No, 1: Yes
0: No, 1: Yes
0: No, 1: Yes
0: No, 1: Yes
0: No, 1: Yes
0: No, 1: Yes
0: No, 1: Yes
0: No, 1: Yes
X UTM coordinate
Y UTM coordinate

Days
−
Years
−
−
−
−
−
−
−
−
−
−
m
m

DGE
DGE
GDE
DGE
DGE
DGE
DGE
DGE
DGE
DGE
DGE
DGE
DGE
CONABIO
CONABIO

1 https://www.gob.mx/salud/documentos/datos-abiertos-152127 accessed on 10 May 2022; 2 http://www.

conabio.gob.mx/informacion/gis/maps/geo/cabmun2kgw.zip accessed on 10 May 2022.

2.2. Logistic Regression
A generalized linear model is a function that relates a set of response variables withsome distribution belonging to the exponential family and a set of independent variables
that are obtained through a measurement or observation. This type of model allows
the generalization of other existing models by varying the distribution of the response
variable or the function that relates E(Yi ) = µi with the set of covariates [23]. This last
function, commonly called the link function, is a monotonic and differentiable function,
which satisfies:g(µi ) = X > β,(1)where X is an nxp matrix of explanatory covariates and β ∈ R p is a px1 vector of coefficientsof the model. In the case where the response variable follow a Bernoulli distribution, theparameter µi = pi represents the probability that a success will occur.
 The link functionp
1− p. Consequently,

e β0 + β1 x1 + β2 x2 +...+ β p x p,
1 + e β0 + β1 x1 + β2 x2 +...+ β p x p(2)

commonly used in this case is the logit function g(µ) = g( p) = log
the conditional probability of Y given X can be expressed as:p (Y = y | X ) =
62

>

eX β
1+e

X> β

=

Int. J. Environ. Res. Public Health 2022, 19, 11992

which satisfies (1). This model is known as the logistic regression model.
2.3. XGBoost
In this research, we used an XGBoost-based logistic regression model for the classification problem, which may be denoted XGBoost-LR, where the explanatory variables are themain risk factors described in [7,8], and the response variable is the presence or absence of
COVID-19 disease.
Let be a data set: {( xi , yi ) : i = 1, . . . , n, xi ∈ R p , yi ∈ R}, where xi is the vector ofcharacteristics, and yi is the response variable. An assembly model based on regression
trees makes use of K simpler models, to predict yi additively:Kybi = ∑ f k ( xi ),f k ∈ F,k =1

ybi is the prediction of yi and F is the class of functions of all possible regression trees;noF = f ( x ) = wq( x) | q : R p → I, w ∈ RT ,(3)

T is the number of leaf, I = {1, 2, . . . , T }, q( x ) represents the index q( x )-th in vector w,
wq( x) represents the q( x )-th component of w, f k represents an independent regression treethat corresponds to a tree structure q with leaf weights w.
Regression trees have a score on each of their leaves, wi denotes the score on the i-thleaf of a tree. The data used in the training of the model are grouped in the leaf nodes. Thus,in the prediction of an example, the decision rules generated by these trees are considered
to classify it in the corresponding leaves and calculate the final prediction adding each of
the scores in the leaves given by the vector w of each tree [19].
The regularized function to minimize, and to learn each f i is:n

L = ∑ l (yi , ybi ) + ∑Ω( f k ),
i =1

(4)

k

l represents the loss function between the observed value yi and the predicted value ybi .
In addition,1Ω( f ) = γT + λkwk2 .
2Ω is the regularized term that penalizes the complexity of the model to avoid overfitting of
the data, γ penalizes the number of leaves or equivalently the complexity of the tree, T is
the number of leaves in the tree, λ is the regularization parameter, and w is the score vector
in the leaves.
The loss function is considered as the mean square error if the problem is regression,
whereas in a binary classification problem, the loss function is the likelihood function
obtained from Equation (2): no(t)

Let ybi1= ∑tk=1 f k ( xi ) = ∑tk−
=1 f k ( xi ) + f t ( xi ), and Equation (4) can be written as:n( t −1)

L(t) = ∑ l (yi , ybi
i =1+ f t ( xi )) + Ω( f t ).

(5)

Using the second order Taylor expansion of the loss function and denoting by
Ij = {i | q( xi ) = j} for some fixed structure q( x ) to the index set whose examples
correspond to that leaf, we can rewrite (5) as:

L(t)

T

=



1



∑ ( ∑ gi )w j + 2 ( ∑ hi + λ)w2j  + γT.

j =1

63

i∈ Ij

i∈ Ij

(6)

Int. J. Environ. Res. Public Health 2022, 19, 11992

The internal sums of the Equation (6) over the index set Ij of the leaf j is due to the factthat the examples present in that leaf have the same score. Subsequently, to find the optimalvalue of the parameters w j in each of the leaves, fixing q( x ) and deriving Equation (6), it
was determined that such a value is:w∗j = −∑ i ∈ I j gi

∑i ∈ Ij hi + λ.

(7)Substituting (7) in (6), we found that the minimum value is reached in:L(t) ( q ) = −2
1 T ( ∑ i ∈ I j gi )+ γT.
∑
2 j =1 ∑ i ∈ I j h i + λIn practice, the goal is to optimize one level of the tree at a time, so that branches areadded iteratively to a tree. Thus, if a leaf is divided into two leaves and the index set of theexamples that are on the left leaf and on the right leaf are I I and ID , respectively, then the
reduction in loss after division is given by:"#( ∑ i ∈ I D gi ) 2( ∑ i ∈ I I ∪ I D gi ) 2
1 ( ∑ i ∈ I I gi ) 2
Lsplit =
+
−
− γ.
(8)
2 ∑ i ∈ I I h i + λ ∑ i ∈ ID h i + λ ∑ i ∈ I I ∪ ID h i + λThis last expression consists of the scores of the original leaf, the left leaf, the rightleaf, and the regularization term. In addition, this formula is the gain in the loss reduction
equation; therefore, it is used to evaluate the division with the candidates.
3. Results
3.1. Spatiotemporal Distribution of Daily Cases
The temporal distribution function of daily cases of COVID-19 in Mexico from February 2020 to the end of April 2022 is described in Figure 1. When analyzing the Figure 1,
it can be seen that, after the arrival of the SARS-CoV-2 virus in Mexico, the daily cases ofinfected increased rapidly beginning in the month of May, reaching the peak of the firstwave of positive cases in summer 2020 (mid-July and early August). In this first wave, thetemporal distribution function considering the gender of the patient indicates that there
is a greater presence of masculine cases than feminine. Subsequently, the second wave ofpositive cases is confirmed during the winter of 2021 (January–February). In this phase, theoccurrence of positive cases was higher than in the first wave, the infection had propagatedto every state in Mexico. In addition, notice from Figure 2 that the cases in women and men
are quite similar, and the trend of male cases is very similar to the trend of female cases. On
the other hand, the third and fourth wave of cases occurred during the months of July 2021
and January 2022. Furthermore, the last wave was the largest, with a significantly greaternumber of cases in men than in women. The distribution function by age group showsthat, during the first and second wave, young people aged ≤ 20 were the least affected by
the pandemic, while the most affected were people between 20 and 40 years, followed byadults older than 40 years. In the case of the third and fourth wave, there was an increased
risk for young individuals (under 20 years), while older adults over 40 years decreased
their chances of infection; see Figure 3.

64Int. J. Environ. Res. Public Health 2022, 19, 11992

Figure 1. Temporal distribution function in terms of probability for daily COVID-19 cases fromFebruary 2022 to April 2021. The dotted line represents the date when a maximum was reached in
COVID cases.

Figure 2. Temporal distribution function in terms of probability for daily COVID-19 cases by gender.

The results obtained from the statistical analysis by geographic location determinedthat the states with the highest occurrence of COVID-19 cases were the following: in thecentral zone of Mexico, the predominant states were Mexico City and the State of Mexico;in the west, Jalisco and Guanajuato; in the north of Mexico, these were Tamaulipas, Nuevo
León, Sonora, and, in the south, these were Puebla, Veracruz, and Tabasco, see Figure 4.
Most of the states least affected by the disease, with the exception of Chiapas, were the least
populated, as is the case of Colima, Nayarit and Campeche; an exception is the state of
Chiapas. On the other hand, the municipalities in Mexico with the most cases of COVID-19
were Puebla, Querétaro, León, Monterrey and Guadalajara. In the case of Mexico City, thelargest number of cases occurred in the most populated municipalities, such as Iztapalapa,
Gustavo A. Madero, Álvaro Obregón, Tlalpan and Benito Juarez, see Figure 5.

65

Int. J. Environ. Res. Public Health 2022, 19, 11992

Figure 3. Temporal distribution function in terms of probability for daily COVID-19 cases by age.

Figure 4. Mexican states with most COVID-19 cases from February 2020 to April 2022.

Figure 5. Municipalities with most COVID-19 cases from February 2020 to April 2022.

66

Int. J. Environ. Res. Public Health 2022, 19, 11992

Additionally, the maps in Figure 6a,b show the actual frequency for each municipalityof positive cases of COVID-19. Figure 6a shows that many entities in Mexico have a highnumber of incidents, while Figure 6b shows the center of Mexico, it can be seen that thefederal district is one of the most affected entities.

Figure 6. Spatial distribution of COVID-19 positive cases: (a) national context; (b) zoom-in toCentral Mexico.

3.2. Spatiotemporal Distribution of Individuals with Comorbidities
In Mexico, a large part of the population suffers from a disease that exposes them
to a higher risk of developing COVID-19 or of having serious complications once they
are sick. Therefore, it is important to explore the probability of cases against the main
risk factors. To do this, four groups that were diagnosed positive for COVID-19 wereanalyzed: (1) individuals who did not present any risk factor, (2) individuals with diabetes,(3) individuals who suffered from obesity and (4) those who presented pneumonia, Figure 7.
For the group of individuals without comorbidity, it was determined that the highest
number of incidences occurred in patients between the ages of 25 and 35 years, with anaverage of 37.75 years. In the case of individuals with diabetes, the mean was presented at67

Int. J. Environ. Res. Public Health 2022, 19, 11992

57.5 years, while, for obesity, the cases were positively skewed with a mean of 45.62 years,
unlike pneumonia, where the distribution of cases was negatively skewed with a mean of
56.8 years.

Figure 7. Probability distribution functions for positive COVID-19 cases by comorbidity against age.

The proportion of patients who presented some risk factor of the total set of records is
described in Table 2. Figure 8 shows that the main risk factors present in a large part of
the Mexican population are hypertension, pneumonia, obesity, diabetes and smoking. On
the other hand, the spatial distribution of each of the comorbidities under study associatedwith the cases that tested positive for COVID-19 can be seen in Figure 8.
Table 3 shows the patients diagnosed as positive for COVID-19, who had some previous comorbidity. The main risk factors in the Mexican population included hypertension,
pneumonia, obesity, and diabetes. In addition, only of the positive cases (3,119,851), 55.59%of the patients suffered at least one of the 10 diseases that were considered in the study.
When analyzing the gender, it was found that 50.31% and 49.69% of the total positive cases
are men and women, respectively. In the group of men, 56.16% had at least one disease,
while in the case of women this was 55%.
Table 2. Percentage of patients with any comorbidity, out of a total of 7,771,714 observations.
Variable
Hypertension
Pneumonia
Obesity
Diabetes
Smoking
AsthmaChronic kidney failure
Cardiovascular disease
Other disease
COPD

68

Yes

No

19.00%
9.00%
15.50%
14.17%
10.02%
3.77%
1.88%
1.94%
2.6%
1.32%

81.00%
91.00%
84.50%
85.83%
89.98%
96.23%
98.12%
98.06%
97.40%
98.68%

Int. J. Environ. Res. Public Health 2022, 19, 11992

Figure 8. Spatial distribution of comorbidities associated with COVID-19 occurrence from February2020 to April 2022: (a–j) all the comorbidities and (k) captions for states and countries.
Table 3. Percentage of COVID-19 patients with any comorbidity, out of a total of 3,119,851 observations.
Variable
Hypertension
Pneumonia
Obesity
Diabetes
Smoking
AsthmaChronic kidney failure
Cardiovascular disease
Other disease
COPD

Yes

No

21.62%
15.62%
17.67%
16.53%
8.98%
3.37%
1.93%
1.95%
2.78%
1.37%

78.38%
84.38%
82.33%
83.47%
91.02%
96.63%
98.07%
98.05%
97.22%
98.63%

3.3. Classification Analysis with XGBoost-LR and Classic LR
This study is focused on determining a model capable of predicting whether or not thepatient will suffer from the COVID-19 disease, considering a set of explanatory variables.
Additionally, it sought to identify the importance of each of these variables in different
groups of patients. The study was performed with the XBGoost-LR model described in
Section 2.3 using the likelihood of a multivariate logistic regression model as a loss function.
The measure of selection that determined the order of importance of variables in the context
69

Int. J. Environ. Res. Public Health 2022, 19, 11992

of decision trees was the gain, defined in (8). This measure allowed for deciding which
attribute should go to a decision node, selecting the one whose gain is greater.
The XGBoost-LR model was built considering 70% of the data as the training set
and 30% as the testing set. To measure model quality among a set of candidate models,evaluation metrics were calculated with only the test set. For the final model, we obtained ascore known as sensitivity (66.11%) (i.e., recall rate or fraction of true positives), which is the
proportion of positive cases that the model correctly identified. The specificity was 70.11%(true negative rate), which represented the proportion of negative cases that were correctlyclassified by the algorithm. The probability of how close the result of a measurement
is to the true value, that is, the accuracy of the model, was 68.5%. In addition, it wasdetermined that the most influential variables in the model (in terms of decision trees,
which generate the greatest gain) for the classification were: date, pneumonia, location withlatitude, longitude and age, while diseases such as asthma, hypertension, disease Chronic
obstructive pulmonary disease and cardiovascular diseases were the least influential in themodel classification. The importance of the variables is shown in Figure 9. This type ofimportance represents the average gain across all splits where feature was used. Therefore,
the table shows that the variables age, latitude, longitude, pneumonia and date are thevariables with the greatest importance, while that the variables associated with asthma,
hypertension, COPD, cardiovascular disease, another disease, diabetes, chronic, kidney,disease, smoking obesity and gender have lower values of importance. Although themeasure of importance obtained by these variables is low, this does not mean that thevariables do not contribute to the adjustment; rather, it means that the number of cases inwhich the variable was decisive to form a new branch is lower.

Figure 9. Importance of explanatory variables in XGBoost.

The predictive capacity of the built model was analyzed by comparing it with otherclassification models. To do this, it was compared with the classic multivariate logistic
regression model, which allows for predicting a binary response variable [23]. For training
the classical logistic regression model, the same training and testing set used with the
XGBoost-LR model was employed. The results obtained for the new model were as
follows: the sensitivity was 43.63%, the specificity was 70.49%, and the accuracy was
59.69%. Therefore, it can be showed that the XGBoost-LR model outperformed the classic
logistic regression model in almost all the assessment metrics, except for specificity, see
Table 4.
Alternatively, the predictive ability of the final model can also be viewed graphicallyusing maps; see Figure 10a,b. The prediction of cases by municipality obtained with the
XGBoost-LR model considering the entire Mexican territory can be seen in Figure 10a, while
the zoom-in to Central Mexico is found in Figure 10b. Figure 10a shows the level of risk

70Int. J. Environ. Res. Public Health 2022, 19, 11992

present in each of the municipalities, even more so the areas with the highest probability ofrisk include the north, coast and center of the country. Note that the map is quite similar
to the map in Figure 6, so it can be assumed that the model has good predictive capacity
on the data. We also elaborate a map with the predictions using the multivariate logistic
regression model to contrast the results of the proposed model. The results are shownin Figure 11, in which we observe a significant difference with respect to the map of theobserved values shown in Figure 6.

Figure 10. Predicted spatial distribution of COVID-19 occurrence from February 2020 to April 2022:(a) average actual occurrence with XGBoost-based logistic regression; (b) zoom-in to Central México.

71

Int. J. Environ. Res. Public Health 2022, 19, 11992

Figure 11. Predicted spatial distribution of COVID-19 occurrence from February 2020 to April 2022:(a) average actual occurrence with multivariate logistic regression; (b) zoom-in to Central México.

The XGBoost-LR model was analytically evaluated by calculating the area under the
ROC curve called AUC. The AUC value obtained was 0.75, which indicates that, if an
individual is randomly selected, then there is a probability of 0.75 that the diagnosis madeby the model is correct. Thus, the constructed model is considered appropriate to predictthe risk of acquiring the COVID-19 disease. Figure 12a, shows the ROC curve with theperformance of the XGBoost model. Specificity evaluates the proportion of real negatives
that are correctly identified, while sensitivity describes the proportion of real positives
that were correctly identified. In this way, the ROC curve also provides a perspective on

72

Int. J. Environ. Res. Public Health 2022, 19, 11992

the behavior of the predictive potential of the proposed model. In addition, the optimal
cut-off point was also determined, which maximizes the difference between sensitivity
and 1-specificity (Figure 12b). Figure 12c shows the ROC curve and AUC obtained forthe multivariate logistic regression model. The value of the AUC obtained for this modelshows that the proposed model is better than the multivariate logistic regression model. In
this model, the cut-off was 0.37, which is slightly lower than the cut-off obtained by the
XGBoost model.
Table 4. Evaluation metrics of the XGBoost-LR and classic multivariate logistic regression models.

Model
XGBoostMultivariate logistic regression

Sensitivity

Specificity

Accuracy

66.11%
43.63%

70.11%
70.49%

68.50%
59.69%(a) ROC curve and AUC of the XGBoost-LR model.

(b) Global optimal cut-off in 0.4 of the of the XGBoostLR model.

(c) ROC curve and AUC of the logistic regression
model.

(d) Global optimal cut-off in 0.37 of the logistic regression model.

Figure 12. ROC curve (a,c), area under the AUC curve (b,d) and optimal cut-off value of the models:
XGBoost-LR and logistic regression, respectively, to estimate the risk of suffering from COVID-19.

73

Int. J. Environ. Res. Public Health 2022, 19, 11992

3.4. Predicting New Cases
Figure 9 shows that pneumonia is the most important variable of the variables associated with previous comorbidities. In addition, the rest of the variables associated withprevious comorbidities also contribute to the construction of the model. The reason is
because the decision tree considers spatial and temporal variables, and consequently, incertain geographical places, these variables play an important role for prediction. However,
because these places are less numerous within the study region, the importance assigned to
the variable is considerably lower. In the case of patients with no history, it was observedthat the estimated probability of risk is low, so the aforementioned probability does notexceed the optimal threshold of 0.4, for which the model classifies these observations as
negative cases for COVID-19. In the case of patients suffering from obesity and diabetes, it
was determined that the probability of risk increases slightly compared to individuals who
do not present comorbidity; however, this probability is also below the optimal threshold;thus, they are considered as cases negative for the disease. Finally, in the case of pneumonia,
the probability of risk increases beyond the optimal threshold after 10 years.
4. Discussion
Our findings based on descriptive statistics determined that the most prevalent comorbidities in the studied population were obesity, diabetes, hypertension and pneumonia.
When analyzed variables of location, the data reflected a notable spatial differentiation in
the number of incidents of positive and non-positive COVID-19 cases. That is, urban areasin the central, northern and coastal region of the country, presented a high risk of infection
compared to less populated places, such as Chiapas, Campeche and Colima, as can be seenin the maps.
Another result of the descriptive analysis was the ability to determine the time periods
with a high rate of contagion in Mexico, which occurred shortly after the holiday seasonsof each year. This is reasonable given the enormous mobility on those dates. Additionally,
when analyzing the distribution function of positive cases of COVID-19 grouped by comorbidity, it was observed that older adults were more likely at least one of the comorbidities
considered in the study. Depending on gender, there was almost the same proportion of
positive cases in men and women, which suggests that the risk is independent of gender.
On the other hand, after using the XGBoost-LR model, we were able to confirm that, indeed,a large part of the Mexican population was affected by the COVID-19 disease.
Our results also revealed that some vulnerable groups have a greater risk, which
may be characterized by their health status and geographic location. This fact is of vital
importance not only for the study of the new coronavirus disease, but to show the healthstatus of a large part of the Mexican population. In fact, the Mexican Ministry of Healthindicated that, after the United States, Mexico ranks second in terms of obesity rate, a disease
that is highly related to diabetes, being one of the main causes of death in the country.
On the other hand, the analysis of the geographical locations makes it possible to analyze
the existing risk in densely populated places, and encourages the relevant authorities totake better measures, for example, to strengthen security protocols, in touristic areas of
the country.
Treviño [24] found the presence of the same comorbidities, finding that men sufferfrom COVID-19 disease more often than women. In addition, he indicated that admission to
the Intensive Care Unit was related to the gender patients, as verified by the Chi-square test.
Our result probably differs in that, during the first year of the pandemic, there were morepositive cases in men than in women; however, during the second year, this could change.
On the other hand Ref. [15] performed a logistic regression analysis and determined thatmen were 1.54 times more likely to be hospitalized than women. Furthermore, both [25]and [9] found that, in addition to diabetes and obesity, high blood pressure and chronic
kidney damage can increase mortality in COVID-19 patients. Regarding the analysis bygeographic location of each state of Mexico, Refs. [2,26] analyzed the cases of COVID-19 in
Mexico. In both studies, the results agreed with our analysis, since the same spatial pattern74

Int. J. Environ. Res. Public Health 2022, 19, 11992

was detected throughout the country: State of Mexico, Nuevo León, among others, werethe most affected by the pandemic.
Having a predictive model that allows for identifying individuals with particularcharacteristics is important for taking preventive measures to avoid further deaths. On
the other hand, the constant appearance of new variants of the virus worries stakeholders
around the world and makes it difficult to pinpoint an end date for the pandemic. Therefore,
the authorities must strengthen surveillance and adopt systematic approaches to provide
health indications to the entire population, based on each local context.
5. Conclusions
In the present study, positive and non-positive cases of COVID-19 in Mexico were analyzed at the municipal level using data from the Mexican Ministry of Health. A descriptive
analysis was performed to explore the spatial and temporal distribution of positive cases,
and subsequently an XGBoost-LR model was trained to estimate the risk of infection using
the main comorbidities in patients as covariates. According to the results, we confirm thatpneumonia, obesity and diabetes were variables of utmost importance to predict new cases.
The estimated and observed values of percentage occurrence of cases were very similar,
and indicated that the proposed model was suitable to predict new cases (AUC = 0.75). The
optimal global cutoff to identify when a case is positive for COVID-19 was 0.41. Our results
revealed that patients without comorbidities are less likely to be COVID-19 positive, unlike
people with diabetes, obesity and pneumonia with ages close to 56, 46 and 59 years old,
respectively. The current worldwide situation regarding COVID-19 is still serious, especially with the emergence of new SARS-CoV-2 virus variants. Therefore, we recommend
reviewing and adapting government policies concerning the establishment of preventative
measures to avoid the spread of virus and mitigate the pandemic effects.
Author Contributions: Conceptualization, S.V.-G., A.I.A.-S., C.S.-M. and J.d.C.J.-H.; Data curation,
S.V.-G., A.I.A.-S., C.S.-M. and J.d.C.J.-H.; Formal analysis, S.V.-G., A.I.A.-S., C.S.-M. and J.d.C.J.-H.;
Funding acquisition, S.V.-G., A.I.A.-S., C.S.-M. and J.d.C.J.-H.; Investigation, S.V.-G., A.I.A.-S., C.S.-M.
and J.d.C.J.-H.; Methodology, S.V.-G., A.I.A.-S., C.S.-M. and J.d.C.J.-H.; Project administration, S.V.-G.,
A.I.A.-S., C.S.-M. and J.d.C.J.-H.; Resources, S.V.-G., A.I.A.-S., C.S.-M. and J.d.C.J.-H.; Software, S.V.-G.,
A.I.A.-S., C.S.-M. and J.d.C.J.-H.; Supervision, S.V.-G., A.I.A.-S., C.S.-M. and J.d.C.J.-H.; Validation,
S.V.-G. and A.I.A.-S.; Visualization, S.V.-G. and A.I.A.-S.; Writing—original draft, S.V.-G. and A.I.A.-S.;
Writing—review & editing, S.V.-G. and A.I.A.-S. All authors have read and agreed to the publishedversion of the manuscript.
Funding: This work was supported by a CONACYT Grant scholarship: 2021–2023, for the doctoral
studies of the first author. This research was funded by CONACYT through the grant “Convocatoria
de Ciencia Básica y/o Ciencia de Frontera 2022”, project ID 320036.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The analyzed data sets can be consulted at the following links: https://www.gob.mx/salud/documentos/datos-abiertos-152127 accessed on 10 May 2022; http://www.
conabio.gob.mx/informacion/gis/maps/geo/cabmun2kgw.zip accessed on 10 May 2022.
Acknowledgments: The authors thank the Secretary of Health of Mexico (https://www.gob.mx/
salud/documentos/datos-abiertos-152127, accessed on 10 May 2022) for providing the data usedin this research. Special thanks are also given to two anonymous reviewers who shared insightful
observations that deeply improved our work.
Conflicts of Interest: The authors declare no conflict of interest.

75

Int. J. Environ. Res. Public Health 2022, 19, 11992

Abbreviations
The following abbreviations are used in this manuscript:
COVID-19
CONABIO
COPD
DGE
ROC
SARS-CoV-2
WHO‘CO’ stands for corona, ‘VI’ for virus, ‘D’ for disease, and ‘19’ for 2019
National Commission for the Knowledge and Use of Biodiversity
Chronic obstructive pulmonary disease
Directorate General for Epidemiology
Receiver Operating Characteristic
Severe acute respiratory syndrome coronavirus 2
World Health Organization

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77International Journal ofEnvironmental Researchand Public Health
Review

The Atmospheric Environment Effects of the COVID-19
Pandemic: A Metrological Study
Zhong Chen 1

and Dongping Shi 1,2, *1
2*College of Environment and Resources, Xiangtan University, Xiangtan 411105, China
Key Laboratory of Large Structure Health Monitoring and Control, Shijiazhuang 050043, China
Correspondence: shidongping@xtu.edu.cn

Abstract: Since the COVID-19 outbreak, the scientific community has been trying to clarify various
problems, such as the mechanism of virus transmission, environmental impact, and socio-economic
impact. The spread of COVID-19 in the atmospheric environment is variable and uncertain, potentially resulting in differences in air pollution. Many scholars are striving to explore the relationshipbetween air quality, meteorological indicators, and COVID-19 to understand the interaction between
COVID-19 and the atmospheric environment. In this study, we try to summarize COVID-19 studies
related to the atmospheric environment by reviewing publications since January 2020. We usedmetrological methods to analyze many publications in Web of Science Core Collection. To clarify thecurrent situation, hotspots, and development trends in the field. According to the study, COVID-19
research based on the atmospheric environment has attracted global attention. COVID-19 and air
quality, meteorological factors affecting the spread of COVID-19, air pollution, and human healthare the main topics. Environmental variables have a certain impact on the spread of SARS-CoV-2,
and the prevalence of COVID-19 has improved the atmospheric environment to some extent. The
findings of this study will aid scholars to understand the current situation in this field and provide
guidance for future research.
Citation: Chen, Z.; Shi, D. The
Atmospheric Environment Effects of

Keywords: COVID-19; metrology; data analysis; atmospheric environment; pollution

the COVID-19 Pandemic: A
Metrological Study. Int. J. Environ.
Res. Public Health 2022, 19, 11111.
https://doi.org/10.3390/
ijerph191711111
Academic Editors: Sachiko Kodera
and Essam A. Rashed
Received: 21 July 2022
Accepted: 29 August 2022
Published: 5 September 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
The COVID-19 pandemic caused by SARS-CoV-2 has been recognized as a global public health emergency by The World Health Organization [1]. The outbreak and continued
prevalence of COVID-19 has had a severe impact on all aspects of human life worldwide. In
response to the strong infectivity and spread of COVID-19, many countries have adopted
policies of lockdown and restriction of activities to strictly control the occurrence of theinfection. Lockdown is the most direct way to stop COVID-19, but it cannot be sustained in
the context of a global pandemic. Research into specific drugs to prevent or treat COVID-19
have yet to yield results [2]. Vaccination is the most effective and cost-effective intervention to control the spread of COVID-19, but the continuous emergence of new variants of
SARS-CoV-2 undoubtedly poses another great challenge to vaccine development [3].
Under the current circumstances, COVID-19 is likely to coexist with humans for a longtime. Its normalization will lead to a significant decrease in the mobility of the population,
making a big change in the way people live and travel, and having a great impact on thefunctioning of society and the policies of the state. Both natural and human behavioral
changes resulting from COVID-19 have direct or indirect effects on the atmospheric environment. These influences are multifaceted, with many positive and negative influences. Also,
the atmospheric environment has an important relationship to the spread of COVID-19.
Understanding the interrelationship between air quality, meteorological indicators, and
COVID-19 is significant for saving human lives.
We need to comprehensively understand the relationship between COVID-19 and theatmospheric environment, which has attracted the attention of many authors. But there is79

COVID-19. Understanding the interrelationship between air quality, meteorological indicators, and COVID-19 is significant for saving human lives.
We need to comprehensively understand the relationship between COVID-19 andInt. J. Environ. Res. Public Health 2022, 19, 11111
the atmospheric environment, which has attracted the attention of many authors. But
there is no metrological analysis type of review study in this field. Metrological analysis
overcomes
the subjective
factors
in thestudy
traditional
review, completely
covers allnometrologicalanalysis typeof reviewin thisliteraturefield. Metrologicalanalysis overcomes
literaturein theselected
period,avoidsliterature
the loss of
key literature,andcan quantitatively
thesubjective
factors
in thetraditional
review,
completely
coversall literature
explorethe knowledge
structure,researchandthelatestinsights in explore
some scienin
the selectedperiod, avoids
the lossof keyhotspots,
literature,andcanquantitatively
thetific fields [4].
The metrologicalstudy andof COVID-19
and theinatmosphericenvironment
knowledge
structure,research hotspots,
the latest insights
some scientificfields [4].
The
metrological aiming
study oftoCOVID-19
the atmosphericenvironment
simultaneously,
simultaneously,present theandoverallpicture of thisfield by analyzing
and exaimingtopresent
theoverallpicture
of
this
field
by
analyzingandexploringthecurrent
ploring the current situation and research hotspots in this field [5].
situation and research hotspots in this field [5].
2. Research Methodology
2. Research Methodology
This section details the process of research design (Section 2.1), data collection (Secdetails
the processof research
design2.1), data collection (Section 2.2),
tion This2.2),section
and data
processing(Section
2.3) usedin (Section
this research.
and data processing (Section 2.3) used in this research.
2.1. Research Design
2.1. Research Design
The bibliometric approach is good at uncovering the underlying knowledge strucThe bibliometric approach is good at uncovering the underlying knowledge structure
ture in literature and integrating the visualization results for further analysis [4]. In thisin literature and integrating the visualization results for further analysis [4]. In this paper,
paper, we use bibliometric analysis methods and data visualization analysis tools to synwe use bibliometric analysis methods and data visualization analysis tools to synthesize andthesize and comb the literature related to COVID-19 and the atmospheric environment,
comb the literature related to COVID-19 and the atmospheric environment, quantitatively
quantitatively analyze the data, and generate visualization and content analysis results.
analyze the data, and generate visualization and content analysis results. Drawing with
Drawing with AutoCAD as shown in Figure 1. To understand the complex relationship
AutoCAD as shown in Figure 1. To understand the complex relationship between COVIDbetween
andenvironment
the atmosphericenvironment
as wellthe ofdegreeand trendof19
and theCOVID-19
atmospheric
as wellas the degree
and astrend
its impact
on theits
impactontheatmosphericenvironment.
atmospheric environment.
stage1:Bibliometric Analysis Data
KeywordsearchWeb of sciencestage2:Metrological Analysis
keyword co-occurrence

Keyword clustering

VOSviewer

Research Topics

Journal co-citation
Knowledge Domains
Country cooperation
Publication screening

Bibliometric

Country cooperation

Country cited frequency

PyCharm
Future Research
Publication information

Number of publications

OriginFigure1.1.Workflow
Workflowofofthe
thesystematic.
systematic.
Figure2.2.
2.2.Data
DataCollection
Collection
The
data
The data inin thisthis paper
paper areare all
all fromfrom WebWeb ofof Science
Science Core
Core Collection,
Collection, which
which waswas seselected
becauseitcovers
a
wide
range
of
outstanding
publications
intheentireacademic
lected because it covers a wide range of outstanding publications in the entire academic
field
fieldand
andisisone
oneof
ofthe
thelargest
largestdatabases
databasesfrom
from1900
1900totothe
thepresent.
present.The
Thedatabasedatabasecontains
contains
authors,
citations,journals,
and
much
more
bibliographic
information
that
can
used
authors, citations, journals, and much more bibliographic information that
can
be be
used
for
for
analysis[5,6].
The
World
HealthOrganization
named
the
new
coronavirus
infection
analysis [5,6]. The World Health Organization named the new coronavirus infection pneupneumonia as COVID-19, and the International Committee on Classification of Virusesmonia as COVID-19, and the International Committee on Classification of Viruses named
named the new coronavirus SARS-CoV-2. Therefore, the search strategy was set to (COVIDthe new coronavirus SARS-CoV-2. Therefore, the search strategy was set to (COVID-19)
19) OR (SARS-CoV-2) AND (atmosphere OR air). The search record includes title, author,
OR (SARS-CoV-2) AND (atmosphere OR air). The search record includes title, author, ababstract, keywords, and references [7]. In the Web of Science Core Collection database,stract, keywords, and references [7]. In the Web of Science Core Collection database, thethe Boolean operators “OR” and “AND” are used to merge the individual components ofa search query.
An initial search using these keywords from the Web of Science Core Collection
database for the period 1 January 2020–31 December 2021. After filtering by publication
type and research direction, a total of 1781 relevant publications were obtained by removing
duplicates, including 1684 papers and 144 reviews. Research interests include environmen-

80Int. J. Environ. Res. Public Health 2022, 19, 11111

tal sciences, public environmental and occupational health, meteorology and atmosphericsciences, multidisciplinary sciences, and green and sustainable technologies, which are
related to the environmental field.
2.3. Data Processing
We analyzed and evaluated the collected data, including country, source, title, author,
publication year, affiliation, document type, field of interest, keywords, citation, etc. We
consider the number of published publications as a quantitative indicator of the research
productivity of authors, countries, institutions, sources, etc. The bibliometric analysis in thisstudy was conducted mainly on VOSviewer, and the co-occurrence network visualization
map was constructed based on the information extracted from 1781 publications to present
the publication data. VOSviewer can combine network visualization and spectral clustering
to analyze the underlying knowledge structure contained in many publications [8]. In
addition, the software can perform network analysis on different aspects of the datacollected, and quickly process text data downloaded directly from the Web of Sciencesite [5]. This study also utilizes the integrated development environment PyCharm, thescientific drawing software Origin, and the spreadsheet software Excel to further assist in
Int. J. Environ. Res. Public Health 2022, the19, xanalysis and processing of the data.
4 of 15
3. Results of the Metrological Analysis
This section conducts a qualitative and in-depth metrological analysis of publication
was published in Atmosphere in April 2020 [12]. The paper shows that NOx emissions indata from selected studies. It includes the trend of publication volume in the last two years
most parts of eastern China have decreased significantly due to the lockdown after the(Section 3.1), research content of high-influence authors (Section 3.2), national cooperaoutbreak of COVID-19. After the lockdown period, NOx emissions began to rise to various
tion relationship (Section 3.3), analysis of the number of national citations (Section 3.4),
degrees. Among them, January 2021 saw the maximum number of papers published, far
and analysis of publication sources (Section 3.5). In addition, high co-occurrence keyhigher
other months.
The
mainmayco-occurrence
be that paperscluster
publishedat thewords thanare analyzed(Section
3.6)
andreason
keywordanalysis
is beginningelaborated
of
the
year
have
a
longertime
toaccumulate
citationsthanpaperspublishedatthe end of(Section 3.7).
the year, which helps to improve the impact factor.
TheNumber
third phaseis from February
2021
toYears
December 2021, and this phase is the stable
3.1. Theof Publicationsin the Past
Two
growthphase.
The
numberofpublications
in
thisperiod canbegan
falltofrom
theitspeak,but
The variation in a research field’s publication output
be to
used
assessdevelopstill
maintained
a certain accumulation,
number of publications.
The [9].
research
focusesmore
on changes
mentstatus, knowledge
and maturity
As canbe seenin Figure2, theinairqualityandemissionsofatmosphericpollutantscausedby
COVID-19.
This
will benumber of publications in this research area has grown rapidly over time and is divided
ainto
research
directionThereof lasting
three phases.
were interest.
474 publications in 2020 and 1307 publications in 2021.

Figure 2.
2. TheThenumbernumberof
ofpublications
publicationsinin thethe past
past two
two years.
years.
Figure

The first Authors
phase isandfrom
January
to March
2020, this phase is the initial growth phase.
3.2. Influential
Their
Research
Interests
At the beginning of the COVID-19 epidemic, many countries began to focus on and study
Authors with
manyCOVID-19
highly cited
papersoften accompany
the research
hotspotsandthe correlation
betweenandthe atmosphericenvironment,
especiallythe influmethodologicaltrends
in
this
field
and
play
an
important
role
in
the
developmentofence of the atmospheric environment on COVID-19 transmission and mortality. The thisfirstfield. In this study, the top five authors were determined by a comprehensive evaluationof the number of authors’ publications and citation times, as shown in Table 1.
Zhang, Hongliang of Fudan
81 University published 7 papers and was cited 852 times,
with an average of 121.7 times [13–19]. His papers were cited the most frequently and had
the highest average number of citations per paper, indicating that his research received

Int. J. Environ. Res. Public Health 2022, 19, 11111

publication was published in January 2020 and focused on the extent of air and surface
contamination in a COVID-19 non-intensive care unit [10]. The second publication was
published in January 2020 and the study focused on the driving role of travel numbers on
the spread of COVID-19 [11]. Both publications were published in Bioscience.
The second phase is from April 2020 to January 2021, and this phase is the rapid
development phase. At this time, the COVID-19 outbreak has been going on for some time,
research in the field expanded and the number of publications exploded. With a bufferof time and sufficient data to support it, studies on the effects of COVID-19 on air quality
began to emerge and develop rapidly. The impact of COVID-19 on the atmosphere is clear
and immediate. A study on NOx reduction and recovery during COVID-19 in East China
was published in Atmosphere in April 2020 [12]. The paper shows that NOx emissions
in most parts of eastern China have decreased significantly due to the lockdown after theoutbreak of COVID-19. After the lockdown period, NOx emissions began to rise to various
degrees. Among them, January 2021 saw the maximum number of papers published, farhigher than other months. The main reason may be that papers published at the beginningof the year have a longer time to accumulate citations than papers published at the end of
the year, which helps to improve the impact factor.
The third phase is from February 2021 to December 2021, and this phase is the stable
growth phase. The number of publications in this period began to fall from the peak, but
still maintained a certain number of publications. The research focuses more on changes
in air quality and emissions of atmospheric pollutants caused by COVID-19. This will bea research direction of lasting interest.
3.2. Influential Authors and Their Research Interests
Authors with many highly cited papers often accompany the research hotspots andmethodological trends in this field and play an important role in the development of thisfield. In this study, the top five authors were determined by a comprehensive evaluation of
the number of authors’ publications and citation times, as shown in Table 1.
Table 1. Top 5 most productive authors on COVID-19 and air environment research.
Rank

AuthorInstitution

TP

TC

AC

Main Research Interests

1

Zhang,
Hongliang

Fudan University

7

852

121.7

2

Coccia, Mario

6

558

93

The impact of air pollution on the spread of outbreaks

3

Bashir,MuhammadFarhanCNR Natl Res
Council Italy

The impact of lockdown measures on air quality in China
and India

Cent South University

6

539

89.8

Environmental quality, climate indicators and the
COVID-19 pandemic

4

Querol, Xavier

Spanish Res Council CSIC

5

423

84.6

5

Wang, Peng

Hong Kong Polytech
University

7

358

51.1

Changes in air quality caused by COVID-19 lockdown andits implications
Changes and causes of air pollutant concentration underepidemic lockdown

Notes: TP = Total publications; TC = Total citations; AC = Average number of citations per paper.

Zhang, Hongliang of Fudan University published 7 papers and was cited 852 times,
with an average of 121.7 times [13–19]. His papers were cited the most frequently and had
the highest average number of citations per paper, indicating that his research received
high attention from other authors. In general, the research content of these five authors
has attracted much attention, and the research direction tends to be about the impact of
COVID-19 lockdowns on air quality and the impact of meteorological indicators on the
spread of the epidemic.
3.3. Cluster Analysis of National Cooperation
The number of publications is an important indicator to measure the development
trend of a certain field. To some extent, the number of publications in this field can reflect
the research strength of a country in this field [7]. As shown in Figure 3a, through the82which cooperates more with Asian and European countries. We can find Italy, the UnitedStates, China, Australia, and India as regional leading countries, as the close cooperation
between countries has played a substantial role.
The papers on COVID-19 and the atmospheric environment field are mainly concenInt. J. Environ. Res. Public Health 2022,trated
19, 11111
in China (469), the United States (399), India (209), Italy (173), the United Kingdom(137), and other countries such as Germany (94) and Spain (89), which also have more
prominent research capacity in this field. Among them, the United States is the country
with themost cooperation
severalcountries in theworld, onand30itApril
frequently
onlinebibliometricanalysiswith
websitebibliometric.com(accessed2022), cooperwe canates withobtain
China,
theUnited Kingdom,
Italy, andGlobally,
the international
codirectly
theinter-country
partnership
map,Canada.
where thearea occupied
by countries
operation links
in researchin the fieldof the atmosphericenvironment
under COVID-19
represents
the numberof national
publications
and the total
linkage intensity
represents
are degreestrong, ofandmany countries
pay attention to research and cooperation in this field.
theinter-country
partnership.

(a)

(b)
Figure 3. (a) Country partnership chart; (b) Country partnership clustering chart.
Figure 3. (a) Country partnership chart; (b) Country partnership clustering chart.

3.4. Country
Cited
Frequency
Analysis
Figure 3bshows
the graphof VOSviewer to generate the clustering analysis of country
cooperation
relations.
In this paper,
the analysis
processofoftheclustering
algorithm
is individedThe numberof citations
is an important
indicator
influenceof papersa cerintotain threefield. steps.
The total number of citations and the average number of citations of a country
in theConstructfield can anreflect
the international
influenceandresearchstrength of thecountryin1.
association
matrix for the
analysis
object.
The co-occurrence
matrixbased
the fieldtoacertainextent.
The
averagenumberofcitations
can
also
show
the
qualityofon the quantitative relationship between objects is a data-based square matrix. It isa country’s
papers,thusarereflecting
the incountry’s
scientificresearch
and level.
Thisassumedthat theren variables
the researchmatrix,xij is thecapacity
co-occurrence
observed
papervalue
usesofa thesimpleaverage andmethod.
The
averagecitationfrequency isobserved
the totalvalues
citation
i-th variable
the j-thvariable,andthe co-occurrenceoffrequency
dividedformby thenumbermatrixof publications.
In Figure
4a,b,
the darker the color,
all variables
a ntotal× n square[20]. The formula
is asfollows.
the higher the total or the average number
 of citations fora country. The total number ofx11 citation· · · x1n
citations in China was 13,568, with an averagerate of 28.93. Meanwhile, the total.
.
.
number of citations in the United States
an average citation rate of 28.23.
. . with.. 
M =was(1) ..11,263,
These two countries have the highest totalx citations,
which
are
much
higherthanother
n1 · · · xnn
countries, and the average citations are relatively good, indicating that both countries
have Calculate
strong researchstrengthrelationand influence
in dissimilarity
the field. Therelation
total number
citationsbein
2.
the similarity
value orvalue of(distance)
Indiatween
is 4135any
andtwothedataaveragenumberofcitationsis19.78,
which
indicates
that
India
has
objects in the matrix.
someAresearchstrength andinfluencein thetotal citationsof Australia
the3.
certain clustering
algorithm
is usedto field.
divideTheor merge
data objects
to form aandcertain
UnitedKingdom
are relatively high, with 2805 and 5781 respectively; the average citations
clusteringresult.
are high,with 48.36
and 42.20,
respectively,
influence
VOSviewer
clustering
algorithm
can beindicating
regardedthe
as aprofoundweightedacademic
variant basedonmodular clustering. The modular Q value calculation formula of VOSviewer is as follows,
and the optimal clustering result is obtained when the modular Q value is the largest [20,21].
ci c j1Q=Σ δ xi , x j wij cij − γ(2)2m i< j2m
In the above formula, m represents the total number of connections in the network; cijrepresents the number of connections between nodes i and j (cij = cji ≥ 0); ci represents allconnections of node i; xi represents the cluster to which the node belongs, the δ function is83

Int. J. Environ. Res. Public Health 2022, 19, 11111

1 for xi = xj and 0 otherwise; wij represents the weight value wij = 2m/ci cj , and γ represents
the clustering parameter.
The essence of VOSviewer co-occurrence clustering is that two related items appear
simultaneously and aggregate together. Different types of clustering groups can be obtained
based on the measure of index clustering of the strength and direction of item correlation.
VOSviewer adopts the algorithm of limiting parameter variables. By adjusting the valueof limiting parameter variables, it can control small clusters, and the generated clusters
have strong consistency and high stability. Using VOSviewer, the minimum number ofpublications is set to 7, and the minimum citation frequency is set to 10. Finally, a total of
56 countries were obtained, divided into 5 clusters. The color of the spheres represents
different regions, the size of the spheres represents the number of national publications,and the total link strength indicates the intensity of cooperation between the two countries.
The first cluster (red) is dominated by Italy, which mainly cooperates closely with
European countries; the second cluster (green) is dominated by the United States, which
has frequent cooperation with the Americas, Oceania, Asia, Europe, and Africa; the third
cluster (blue) is dominated by China, which cooperates more with European, Asian, andAfrican countries; the fourth cluster (yellow) is dominated by Australia, which cooperates
more closely with American countries; the fifth cluster (purple) is dominated by India,
which cooperates more with Asian and European countries. We can find Italy, the UnitedStates, China, Australia, and India as regional leading countries, as the close cooperation
between countries has played a substantial role.
The papers on COVID-19 and the atmospheric environment field are mainly concentrated in China (469), the United States (399), India (209), Italy (173), the United Kingdom(137), and other countries such as Germany (94) and Spain (89), which also have more
prominent research capacity in this field. Among them, the United States is the country
with the most cooperation with several countries in the world, and it frequently cooperates
with China, the United Kingdom, Italy, and Canada. Globally, the international cooperation
links in research in the field of the atmospheric environment under COVID-19 are strong,and many countries pay attention to research and cooperation in this field.
3.4. Country Cited Frequency Analysis
The number of citations is an important indicator of the influence of papers in a certain
field. The total number of citations and the average number of citations of a country in thefield can reflect the international influence and research strength of the country in the field
to a certain extent. The average number of citations can also show the quality of a country’s
papers, thus reflecting the country’s scientific research capacity and level. This paper uses
a simple average method. The average citation frequency is the total citation frequency
divided by the total number of publications. In Figure 4a,b, the darker the color, the higher
the total or the average number of citations for a country. The total number of citationsin China was 13,568, with an average citation rate of 28.93. Meanwhile, the total numberof citations in the United States was 11,263, with an average citation rate of 28.23. These
two countries have the highest total citations, which are much higher than other countries,
and the average citations are relatively good, indicating that both countries have strong
research strength and influence in the field. The total number of citations in India is 4135
and the average number of citations is 19.78, which indicates that India has some researchstrength and influence in the field. The total citations of Australia and the United Kingdom
are relatively high, with 2805 and 5781 respectively; the average citations are high, with
48.36 and 42.20, respectively, indicating the profound academic influence and first-classresearch level of these two countries in the field. The total number of citations in Ecuador,
Norway, and Denmark is low, only 577, 1194 and 938 respectively, but the average numberof citations is high, 82.43, 59.70 and 58.65, respectively, which indicates that these countries
have great potential for research in this field. In contrast, it can be found that the average
citation frequency data of most European countries and Australia are more prominent. This84

and first-class research level of these two countries in the field. The total number of citations in Ecuador, Norway, and Denmark is low, only 577, 1194 and 938 respectively, but
number of citations is high, 82.43, 59.70 and 58.65, respectively, which indiInt. J. Environ. Res. Public Health 2022,the19, average
11111
cates that these countries have great potential for research in this field. In contrast, it canbe found that the average citation frequency data of most European countries and Australia
moreprominent.
Thisthat the overallof theseandcountries’
papersshowsarethat
the overall
qualityof shows
these countries’
papers quality
may be higher,
subject tomoremay
be higher,
and subjectby
toscholars.
more attention and recognition by scholars.
attentionand recognition

(a)

(b)

Figure 4. (a) The
total 4.
numbercitationsby country;
(b)by
The
average(b)citationrate bycitation
country.
Figure(a) Theoftotal
numberof citations
country;
The averagerate by country.

Rank
1
2
3
4
5
6
7
8
9
10

3.5.Analysis
AnalysisofofJournal
JournalPublications
Publicationsand
andCo-Citation
Co-Citation
3.5.
A
total
of
137
journalshavepublished
between
2020
and
2021.
Table
A total of 137 journals have publishedrelevant
relevantresearchresearch
between
2020
and
2021.
Ta-2
shows
the informationon theon10themostproductive
journalsin thisinresearcharea. Interms
ble
2 shows
the information
10 mostproductive
journals
this researcharea.
Inof the ofnumberof publications,
ScienceScienceof The Total
Environment(203, 11.39%)
the most
terms
the numberof publications,of TheTotal Environment(203, is11.39%)
isactivejournal,International
Journal of Environmental
Research andResearch
Public Healththemostactivefollowed
journal, by
followedby International
Journal of Environmental
and(140, 7.86%),
Research (134, 7.52%),andAerosolandandAir Aerosol
Quality Research
PublicHealthEnvironmental(140, 7.86%), Environmental
Research(134,
7.52%),and Air(125,
7.02%).
Among
them,
Science
of
The
Total
Environmenthas
animpactfactor ofhas
10.753
Quality Research (125, 7.02%). Among them, Science of The Total Environmentanandacitationfrequency
of
12,176,
which
is
quite
influential.
Environmental
Pollutionhas
impact factor of 10.753 and a citation frequency of 12,176, which is quite influential. Envian impact Pollution
factor of 9.988
a citationof 1644.
Environmentalhasronmental
has anand
impactfactorfrequencyof 9.988 anda citation
frequency ofResearch
1644. Envian impact factorof 8.431
a citation
frequencyof 2323.
are amongthe most
influential
ronmental
Researchhasandan impactfactorof 8.431
and aThey
citationfrequencyof 2323.
They
journals
in
the
field
of
the
atmosphericenvironment.
Next,
Air
Quality,
Atmosphere&
Healthare among the most influential journals in the field of the atmospheric environment. Next,
hasQuality,
an impactfactor of 5.804
and a hascitationfrequency
of of
1115.
Environmental
andAir
Atmosphere& Health
an impactfactor
5.804
and a citationSciencefrequency
Pollution
Research
has
animpactfactor
of
5.190
and
a
citationfrequency
of
565.
These
of 1115. Environmental Science and Pollution Research has an impact factor of 5.190 andjournals
are also of
influential
in theof the areatmosphericenvironment.
atwo
citationfrequency
565. These
twofieldjournals
also influential
in the field of the atmospheric environment.
Table 2. The top 10 most active journals in terms of publication.
Journal co-citation is determined based on the number of times they are cited together.
Journal co-citation
analysiscan beIFused
journalson different
topics andJournal Title
TP
TP
(%)
TC
(2021)to classifySubject
Categoryof the Journal
identify the core journals in each category. This is very helpful for authors to understandScience of The Total Environment
203
11.39%
12,17610.753
Environmental Sciences
most relevant and influential journals for a particular research topic [22]. In this paper,
International Journal ofthe
Environmental
140
7.86%
1368
4.614
Public Environmental & Occupational HealthResearch and Public
weHealthuse VOSviewer to perform a co-citation analysis of the collected data according to difEnvironmental Research
134
7.52%
2323
8.431
Sciences
ferent sources, setting
the 7.02%
minimum
number4.53
of citations for aEnvironmental
source to 200.
The minimum
Aerosol and Air Quality Research
125
1384
Environmental Sciences
threshold can be adjusted
accordingnumber ofGreen
publications
renderedclusterSustainability
109
6.12%
542to the3.889& Sustainable
Science &andtechnology
Environmental Scienceanding groupings.
The89color ofthesphererepresents
different
journal
topics,andthesize of
5.00%
565
5.190
Environmental Sciences
Pollution Researchthesphereindicates
thenumberofcitations.
The
total
link
strengththenindicates
theAtmosphere
81
4.55%
466
3.110
Meteorology & Atmospheric Sciences
closeness
betweenjournals.
Scientific Reports
75
4.21%
832
4.996
Multidisciplinary Sciences
Environmental Pollution
16445 have
9.988a significant impact
Environmental
Sciences studying
The journals 60
shown3.37%
in Figureon authors
Air Quality Atmosphere and Health
55
3.09%
1115
5.804
Environmental Sciences
COVID-19 and the atmospheric environment. Science of the Total Environment is theNotes: TP = Total publications, TP (%) = Total publications (%), IF (2021) = Impact factor in 2021, TC = Total citations.
most-cited
journal, followed by Atmospheric Environment, Atmospheric Chemistry andPhysics, Environment Pollution and Environmental research. These journals are grouped
Journalco-citation
determined basedon thetimesincludes
they are sciencecited together.
into threemaingroups: isEnvironmental
Scienceandnumber
Ecologyof(blue)of theJournalco-citation
analysis
can
be
used
to
classify
journalsondifferent
topicsandidentify
Total Environment, Aerosol and Air Quality Research, Environment Pollution. Environthe corepublicjournalsin eachcategory.
This (red)is veryincludes
helpful for
authors to understandmostmentalhealthandMedicine
Environmental
research, theLancet,relevant and influential journals for a particular research topic [22]. In this paper, we use
VOSviewer to perform a co-citation analysis of the collected data according to different
sources, setting the minimum number of citations for a source to 200. The minimum
threshold can be adjusted according to the number of publications rendered and clusteringgroupings. The color of the sphere represents different journal topics, and the size of the85

Int. J. Environ. Res. Public Health 2022, 19, 11111

sphere indicates the number of citations. The total link strength then indicates the closenessbetween journals.
The journals shown in Figure 5 have a significant impact on authors studying COVID19 and the atmospheric environment. Science of the Total Environment is the most-citeds. Public Health 2022, 19, xof 15
journal, followed by Atmospheric Environment, Atmospheric Chemistry and 8Physics,
Environment Pollution and Environmental research. These journals are grouped into threemain groups: Environmental Science and Ecology (blue) includes science of the Total Environment, Aerosol and Air Quality Research, Environment Pollution. Environmental public
International Journalof Environmental
Researchand Public
Health,Science.
Meteorology
health andMedicine (red) includes
Environmental
research,
Lancet,International
Journaland Atmospheric
Sciences (green)
includesatmospheric
Environment,
Atmosphericof Environmental
Researchand Public
Health, Science.
Meteorology andAtmosphericincludes atmospheric
Atmospheric
Chemistry
and Physics,
Chemistry and Sciences
Physics,(green)
Environment
Science &Environment,
Technology,
Journal ofGeophysicalReEnvironment
Science&Technology,
JournalofGeophysicalResearch:
Atmospheres.
The
search: Atmospheres. The medical journal The Lancet (202.731) and the comprehensive
medical journal The Lancet (202.731) and the comprehensive journal Science (63.714) havejournal Science higher(63.714)have
higherfactors.journal
The more
influential journalin theimpactfactors.
Theimpact
more influential
in the environmental
field is Environenvironmental field
is Environment
Science& Technology (11.357).
ment Science& Technology(11.357).

Figure 5. Journal Figure
co-occurrence
chart.
5. Journal co-occurrence
chart.
3.6. Research Keywords

Table 2. The top 10 most active journals in terms of publication.

Keywords are highly generalized and concise to the subject of a paper. High-frequency
keywords also reflect the research direction to some extent.
TheCategory
keyword density
Subjectof thevisualizaJourJournal Title(%) observe
TC the knowledge
IF (2021) and research density of a certain field.
tion can TP
be used TP
to quickly
nal
VOSviewer sets the minimum keyword frequency to 20 and gets a co-occurrence graph ofcience of The Total Environment86 keywords
203 after11.39%
12,176 words.
10.753
Environmental
Sciences
removing nonsense
Each keyword
in the figure will
fill the color
accordingtothedensityofitssurrounding
keywords.
The
greaterthedensity,the&closer
rnational Journal of Environmental
Public Environmental
Oc- it
140
7.86%
1368
4.614
The density depends on the
Research and Public Health is to red; conversely, the smaller the density, the closer to blue.
cupational
Healthnumber and importance of keywords in the surrounding area.
Environmental Research
134
8.431 to air pollution
Environmental
Sciences
It canbe seen7.52%
from Figure2323
6 that in addition
and air quality,
keywords
erosol and Air Quality Researchrelated to125
7.02%
4.53 factors,Environmental
COVID-19
mortality1384
and environmental
SARS-CoV-2 and Sciences
meteorologicalconditions also appear frequently.
Green & Sustainable Science

109

6.12%

542

3.889

89

5.00%

565

5.190

Atmosphere

81

4.55%

466

3.110

Scientific Reports
Environmental Pollution

75
60

4.21%
3.37%

86832

4.996
9.988

Sustainability

onmental Science and Pollution Research1644& technology
Environmental Sciences
Meteorology & Atmospheric
Sciences
Multidisciplinary Sciences
Environmental Sciencesdensity, the closer it is to red; conversely, the smaller the density, the closdensity depends on the number and importance of keywords in the surrou
It can be seen from Figure 6 that in addition to air pollution and air qua
relatedto COVID-19 mortality and environmental factors, SARS-CoV-2 an
Int. J. Environ. Res. Public Health 2022, 19,11111
ical conditions also appear frequently.

Figure6. Keyword
densitydensity
visualization.
Figure6. Keyword
visualization.

3.7. Keyword Co-Occurrence Cluster Analysis

3.7.VOSviewer
Keyword takes
Co-Occurrence
Cluster
Analysisa distance-based
approachto visualizing bibliometric networks.

After theconstruction oftakes
the normalized
network is completed,the keywords
are positioned
VOSviewera distance-based
approachto visualizing
bibliome
to make the position of the strongly related keywords closer and the position of the weak
Afterkeywords
the construction
network
is completed,the keyw
relatedfurther away of[23].theThenormalized
keyword co-occurrence
networks
analyze the linkstrengthco-occurrence
keywords
bystrongly
studying theirco-occurrence
relationshiptionedbetween
to makethe positionof therelatedkeywords
closerinand themanypublications.
Itspurposeistodescribe
theinternal
composition
relationshipandweak related keywords further away [23]. The keyword co-occurrencenet
structure in a certain academic domain as well as to provide insights into the main research
the link
strength
betweenco-occurrence
keywords
by studying
their co-othemesof thedomain [24,25].
The clusters
group keywords
that are frequently
combinedina set of keywords[26].
Different color Its
spheres
indicateclusters,
andthe samecompo
tionshipin many
publications.
purposeisdifferent
to describe
theinternal
spherecolor
represents
clustersthatareconsistentwiththeresearch
topic.
Thesize
theship and structure in a certain academic domain as well as to ofprovide
inspheres is proportional to the frequency of keywords. The lines between the keywords
maintheresearchthemesof the[27].
domain[24,25].
The clusters
group
keywordreflect
strength oftheir relevanceA total of
5630 keywords
were obtained
fromquently combinedin minimum
a set of keywords[26]. Different
spheres
indicatepublications
by setting the
keyword frequency
to 20, and color
86 keywords
wereobtained
aftertheremovingwords.
They were divided
intothat
three aremajorconsistentclusters,
ters, andsamemeaninglesssphere color
represents
clusters
wit
as in Figure 7.

topic. The size of the spheres is proportional to the frequency of keywords
Air quality indicators (red) contain keywords “pollution”, “air quality”, “PM2.5 ”,
tween
the “lockdown”,
keywords“NOreflectthe strength of their relevance [27]. A total of 5“ozone”,
2 ”, “particulate matter”, etc. Cluster 1 focuses on changes
in theatmospheric
and airin thethe context
of COVID-19
and frequewereobtainedfromenvironment
publications
byquality
settingminimum
keyworddescribes
howlockdowns
dueto
COVID-19
affectair
quality.
86 keywords were obtained after removing meaningless words. They wer(2) Meteorological factors affecting the spread of the outbreak (green) contain keywords
three
major clusters,
as in “temperature”,
Figure 7. “transmission”, “humidity”, “weather”,“COVID-19”,“SARS-CoV-2”,(1)

Cluster
2 focuses
on meteorologicalfactors influencing
the spreadof COVID-19(1) etc.
Air
qualityindicators(red) containkeywords“pollution”,“air qua
and the special relationship between COVID-19 and environmental variables.
“ozone”,“lockdown”,”, “particulatematter”,
etc.“exposure”,
Cluster 1 focu
(3) Air
pollution andhuman health“NO(blue)2containkeywords “airpollution”,“mortality”,“health”, “pandemic”,
etc. Clusterand
3 focuseson the effectsof airpollutionof COV
in the atmosphericenvironment
air quality
in thecontext
onscribes
human healthandmortality.
how lockdowns due to COVID-19 affect air quality.
(2) Meteorological factors affecting the spread of the outbreak (green) con“COVID-19”, “SARS-CoV-2”, “temperature”, “transmission”,“weather”, etc. Cluster 2 focuses on meteorological factors influencing
COVID-19 and the special relationship between COVID-19 and envir
ables.
87(3) Air pollution and human health (blue) contain keywords “air pollution“mortality”, “health”, “pandemic”, etc. Cluster 3 focuses on the effec

Int.J. J.Environ.
Environ.Res.
Res.Public
PublicHealth2022,19,
19,x 11111
Int.
Health2022,

10 of 15

FigureFigure7.7.Keyword
Keywordco-occurrenceco-occurrenceclustering.
clustering.

3.7.1.COVID-19
COVID-19andandAir
AirQuality
Quality
3.7.1.
The
COVID-19
outbreakhashadhada asignificant
significantimpact
impacton
onalmost
almostevery
everyaspectaspectofofpeopeoThe COVID-19 outbreak has
ple’slives
livesaroundaroundthe
theglobe,
globe,resulting
resultinginina avariety
varietyofofdirect
directandandindirect
indirectimpacts
impactson
onthe
the
ple’s
atmosphericenvironment.
environment.Shutdowns
Shutdownsororclosures
closuresofoffactories
factorieshave
havereduced
reducedthetheamountamountatmosphericpollution,withwithananestimated
percent
reduction
N2and
O and
to theclosureofofpollution,
estimated
5050
percent
reduction
in in
N2O
COCO
duedueto theclosureofofheavy
industry
duringChina’s
lockdown[28].
Restrictions
on
travel,reducedmobility
heavy industry during China’s lockdown [28]. Restrictions on travel, reduced mobility ofof people,
reducedtransport
relatedactivities
havesignificantlyreduced
reducedmobile
mobilepeople,andandreducedtransport
andandrelatedactivities
havesignificantly
pollution
sourcesandpollutantemissions.
PM
,
PM
,
CO
,
and
NO
concentrations
have
2.5, PM1010
2
pollution sources and pollutant emissions. PM2.5, CO2,2and NO2 concentrations
havealldeclinedtovariousdegreesduringthelockdown
periodcomparedtothosebeforetheall declined to various degrees during the lockdown period compared to those before thelockdown[13,29,30].
[13,29,30].Volatile
Volatileorganicorganiccompounds
compounds(VOCs),(VOCs),including
includingbenzene,
benzene,arearemainly
mainlylockdown
producedbyvehicular
traffic
andotherincomplete
combustion
processes
that
lockdown
produced by vehicular traffic and other incomplete combustion processes that lockdown
has increasingly limited [31]. NO , one of the main indicators of world economic activity,
has increasingly limited [31]. NO2, 2one of the main indicators of world economic activity,
shows signs of decline in many countries, including the United States, Canada, China, India,
shows signs of decline in many countries, including the United States, Canada, China,
Italy, and Brazil [1,28,32]. At the same time, reduced activities following the lockdown have
India, Italy, and Brazil [1,28,32]. At the same time, reduced activities following the lockled to a broad reduction in greenhouse gas emissions. One estimate suggests that global
down have led to a broad reduction in greenhouse gas emissions. One estimate suggests
daily CO emissions during the lockdown were 17 percent lower than the 2019 average [33].
that global2 daily CO2 emissions during the lockdown were 17 percent lower than the 2019
It can be seen that the environmental conditions of various countries have changed greatly
average [33]. It can be seen that the environmental conditions of various countries haveduring the COVID-19 pandemic, indicating that policy intervention has played a great role.
changed greatly during the COVID-19 pandemic, indicating that policy intervention has
Different studies have shown that urban lockdowns have led to considerable improveplayed a great role.
ments in air quality. The positive impact of urban lockdowns on air quality is greater in cities
Different studies have shown that urban lockdowns have led to considerable imwith larger economies, more industrial activities, and higher traffic volumes [33]. According
provements in air quality. The positive impact of urban lockdowns on air quality is greater
to data from the Meteorological bulletin of the atmospheric environment, the national averageinnumbercities withlargereconomies,
more
industrial
activities,
andhighertraffic
volumes [33].
of hazedays
in 2020 and
2021
will decreaseby 1.5and
4.4 days,respectively,
comAccording
to
data
from
the
Meteorologicalbulletinoftheatmosphericenvironment,
thepared with 2019. In 2020, the meteorological data of Shanghai during the lockdown period
national
averagenumberhaze daysin 2020and 2021
will
decrease bydays,
and before
and after
the oflockdown
were
consistent.
The
comparisonof 1.514 andtrace4.4
elements
respectively,
compared
with
2019.
In
2020,
the
meteorological
dataof
Shanghaiduringin PM2.5 found that the concentration of trace elements in most fine particles showed athe
“V”
lockdown
periodand before
andafter themeasures
lockdown
were
consistent.
The [34].
comparisonofshape trend,
indicatingthat thelockdown
hada significant
impactComparing
14the
trace
elements
inPM
2.5 found that the concentration of trace elements in most fine parair quality index (AQI) results before and after the impact of COVID-19 across India shows
ticlesshoweda “V” shapetrend, indicatingthatthe lockdown measures had a significant
that mostpollutantconcentrations(PM10 , PM
2.5 , CO, SO2 , NOx ) show different patterns ofimpact[34].
Comparing
the air
quality(AQI)
results
and confirmed
after the impactofgradualto rapiddecreases[35,36].
A indexwaveletanalysisof before
COVID-19
data and
COVID-19
acrossIndia showsthatmost pollutant
concentrations(PMAQI10, PM
2.5, COVID-19
CO, SO2,
weather data
in California
from
1 Marchto 24 May
2020 found thatandNOx) showdifferent
patternscircles
of gradualrapiddecreases[35,36].
A wavelet
analysisofshowednegativecorrelation
duringtotheobservation
period,suggestingthat
COVID-19
COVID-19
confirmed
data
andweatherdata
inCalifornia
from
1
March
to
24
May
2020
leads to better AQI and less environmental pollution [37].
found that AQI and COVID-19 showed negative correlation circles during the observation
88Int. J. Environ. Res. Public Health 2022, 19, 11111

However, with the end of the lockdown and the resumption of normal activities,
pollutant emissions rebounded somewhat, and air pollution gradually returned to nearpre-COVID-19 levels. NO, NO2 , and NOx all exhibit abrupt decreases at the time the United
Kingdom locked down. But the return of vehicles to the road during early lockdown has
already offset much of the air quality improvement seen when locked down [38]. Most of
the lockdown’s impact on the atmosphere is short-term, but changes in human activities
caused by the ongoing COVID-19 pandemic can also have lasting effects on the atmosphere.
COVID-19 has also had some adverse effects on the atmospheric environment, withchanges in O3 due to changes in NOx and VOCs emissions. The significant reduction ofNOx during the lockdown was the main reason for the significant increase in O3 . Comparedwith the same period of the previous year, O3 in most parts of the world showed various
degrees of increase during the epidemic lockdown period [39–41]. AQI results in someparts of India comparing the 2020 lockdown period with 2019 show a sharp decrease in
NO2 and an increase in O3 [13,35]. During the nationwide implementation of restrictionsin China in 2020, NOx generated during transportation decreased significantly (>50%), O3
concentration in the air increased significantly, and the atmospheric oxidation capacity(AOC) in the Yangtze River Delta region increased significantly (up to 25%), which was
also a major reason for the increase of O3 level during city lockdown [14].
3.7.2. Meteorological Factors Affecting the Spread of COVID-19
Meteorological factors are unstable and diverse, and the impact of meteorologicalindicators on the spread of COVID-19 is comprehensive and complex. Analysis of Canadianmeteorological data and COVID-19 confirmed cases for 2020 revealed a direct negative correlation between air quality, temperature, humidity, and COVID-19 infection [42]. A studyof meteorological data and COVID-19 data in Istanbul and other regions also came to thesame conclusion that air quality and temperature significantly affect the number of COVID19 deaths in Istanbul [43]. In the same analysis of Wuhan, there was also a significant
agreement between AQI, humidity, and mortality rates. Humidity was negatively correlated with related COVID-19 deaths [44]. Temperature is the only significant meteorologicalindicator that has a significant correlation with the spread of COVID-19 [45]. Although
only in the short term, daily temperatures in Madrid, Spain, and California, the UnitedStates show a negative correlation between COVID-19 outbreaks and death rates [37,46].
This means that temperature plays an important role in limiting COVID-19, suggesting thattemperature may help contain COVID-19. Overall, empirical results suggest that risingtemperatures may reduce transmission of the SARS-CoV-2.
Statistical analysis of confirmed COVID-19 data and local meteorological variables in
Manaus revealed that low solar radiation cycles may lead to increased COVID-19 deaths due
to reduced solar radiation. Dry spells may impair nasal functions that prevent viruses andbacteria from entering the body, leading to increased mortality from COVID-19 [47]. A studyin Italy found that cities with high wind speeds had fewer COVID-19 infections, and inland
cities with low wind speeds and high air pollution had higher COVID-19 infections [48]. Low
wind speeds and high concentrations of air pollutants may contribute to the persistence ofvirus particles in urban air and thus to the indirect transmission of SARS-CoV-2.
3.7.3. Air Pollution and Human HealthAir pollution is one of the biggest environmental threats to human health. It isharmful to the human body in many ways, mainly in the form of respiratory diseases
and physiological disorders. It is of great practical significance to study the impact of air
pollution on aggravating COVID-19 infection in the population. People living in areas
with high levels of pollutants are more likely to develop chronic respiratory diseases andinfections with pathogens [49].
An analysis of the link between air pollution and COVID-19 in Indian cities foundan asymmetric relationship between PM2.5 and COVID-19 cases, where the positive impact of
PM2.5 concentration intensifies the spread of COVID-19. Environmental pollutants CO, O3 ,89

Int. J. Environ. Res. Public Health 2022, 19, 11111

and NO2 are also positively correlated with confirmed cases and deaths of COVID-19 [50].
Atmospheric particulate matter, upon exceeding the satisfactory level, serves as an important
cofactor in increasing the risk of SARS-CoV-2 transmission and related mortality [51].
Data from Tehran also shows a significant link between COVID-19 mortality and exposure to environmental pollution, with increased PM2.5 levels in the air likely to increase
SARS-CoV-2 mortality [52]. A similar study conducted in Germany showed that PM2.5 ,O3 , and NO2 were significantly correlated with COVID-19 outbreaks [45]. In many Italianprovinces, long-term air quality data are significantly associated with COVID-19 cases, further
demonstrating that long-term exposure to air pollution may be an enabling environment
for virus transmission [53]. Data from Wuhan also points to an increase in deaths due topoor air quality [44]. Environmental pollution is an important factor affecting the incidence
and death of COVID-19. Areas with higher levels of environmental pollution are prone torespiratory syndrome, which reduces the immunity of residents and affects their susceptibility
to COVID-19 [54]. The mortality rate decreases more significantly in countries with highlevels of greenery than in countries with low levels of greenery. A good air environment anda green environment are conducive to human survival and development [55].
4. Conclusions
At the beginning of COVID-19, many authors began to study the meteorological factors
affecting the spread of COVID-19 and the impact of air pollution on human health andmortality, which is also a continuing hot research direction. Studies have shown that good
air quality, high temperature, and humidity are relatively adverse to COVID-19 infection.
Dry air may impair nasal functions that prevent viruses and bacteria from entering thebody, leading to increased mortality from COVID-19. Low solar radiation cycles may leadto higher COVID-19 mortality due to reduced solar radiation [47]. Rising temperatures
could help curb the spread of COVID-19. Low wind speed and high concentration of air
pollutants may promote the persistence of virus particles in urban air, thus facilitating
the indirect transmission of SARS-CoV-2 [48]. People living in areas with high levels ofpollutants are more likely to suffer from chronic respiratory diseases and to be infectedwith pathogens [49]. The higher the level of environmental pollution, the more likely it is toaffect the susceptibility to COVID-19 [54]. Understanding SARS-CoV-2 transmission underenvironmental variables can help provide supporting evidence to healthcare policymakers
for formulating strategies to combat COVID-19.
Subsequently, there have been more and more papers on the impact of COVID-19 on
the atmospheric environment and air quality, which has become the hottest topic in thefield of the atmospheric environment under the impact of COVID-19. Obtaining sufficient
atmospheric environment data requires a time buffer, so papers on this topic appeared
relatively late. Air pollution is already a serious environmental problem. The COVID-19
pandemic has had a great impact on the atmospheric environment and improved theatmospheric environment to a certain extent. During the COVID-19 pandemic, lockdowns,
travel restrictions, and reductions in industry, transportation, and related activities havesignificantly reduced mobile sources of pollution and emissions of pollutants. The lockdown has also led to widespread reductions in air pollutants and greenhouse gas emissions,and considerable improvements in air quality. In general, the concentrations of the environmental pollutants PM2.5 , PM10 , CO2 , CO, SO2 , and NO2 all declined to various degrees,
while the concentration of O3 showed an increasing trend. Studying the special and complex relationship between COVID-19 and the atmospheric environment can provide new
insights for environmental managers to control good air quality and manage air pollution
under the impact of COVID-19.
Author Contributions: Conceptualization, Z.C. and D.S.; methodology, Z.C. and D.S.; software,
Z.C.; formal analysis, Z.C.; resources, Z.C. and D.S.; data curation, Z.C.; writing—original draft
preparation, Z.C. and D.S.; writing—review and editing, Z.C.; visualization, Z.C.; supervision, D.S.;
project administration, D.S.; funding acquisition, D.S. All authors have read and agreed to the
published version of the manuscript.

90

Int. J. Environ. Res. Public Health 2022, 19, 11111

Funding: This research was funded by National Natural Science Foundation of Hunan Provincial(2021JJ40538), Scientific research project of Hunan Provincial Department of Education (21B0133),
Foundation of Key Laboratory of Large Structure Health Monitoring and Control in Hebei Province(KLLSHMC2104).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.

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93

International Journal ofEnvironmental Researchand Public Health
ArticleCoronavirus Disease Pandemic Effect on Medical-Seeking
Behaviors Even in One Resource-Competent Community: A
Case Controlled Study
Fang Wang 1,† , Jin-Ming Wu 2,3,† , Yi-Chieh Lin 4 , Te-Wei Ho 2 , Hui-Lin Lin 1 , Hsi-Yu Yu 2, * and I-Rue Lai 2, *
1
2
3
4

*
†

Citation: Wang, F.; Wu, J.-M.; Lin,
Y.-C.; Ho, T.-W.; Lin, H.-L.; Yu, H.-Y.;
Lai, I.-R. Coronavirus Disease
Pandemic Effect on Medical-Seeking
Behaviors Even in One
Resource-Competent Community: A
Case Controlled Study. Int. J. Environ.
Res. Public Health 2022, 19, 10822.
https://doi.org/10.3390/
ijerph191710822
Academic Editors: Sachiko Kodera
and Essam A. Rashed
Received: 18 July 2022
Accepted: 24 August 2022

Department of Nursing, National Taiwan University Hospital, Taipei 100, Taiwan
Department of Surgery, National Taiwan University Hospital and National Taiwan University College ofMedicine, Taipei 100, Taiwan
Department of Surgery, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu 300, Taiwan
Department of Public Health, College of Health Sciences, Kaohsiung Medical University,
Kaohsiung City 807, Taiwan
Correspondence: hyyvpn@ntu.edu.tw (H.-Y.Y.); iruelai@gmail.com (I.-R.L.);
Tel.: +886-2-23123456 (ext. 65107) (I.-R.L.)
These authors contributed equally to this work.

Abstract: (1) Background: The coronavirus disease 2019 (COVID-19) pandemic had overwhelming
impacts on medical services. During its initial surge, Taiwan was unique in maintaining its medical
services without imposing travel restrictions, which provided an ideal environment in which totest if the fear of becoming infected with COVID-19 interfered with health-seeking behavior (HSB).
We tested this hypothesis among adults with acute complicated appendicitis (ACA). (2) Methods:
Adults with acute appendicitis were enrolled between 1 January and 30 June 2020 (COVID-19 period).
The first two quarters of the preceding 3 years were defined as a historical control group. Outcome
measures included the rate of ACA and the number of hospital stays. (3) Results: The COVID-19
era included 145 patients with acute appendicitis. Compared to the historical control (320 patients),
the COVID-19 era was significantly associated with a higher length of symptom duration until
presentation to the emergency room within >48 h (17.2% vs. 9.1%, p = 0.011), a higher incidenceof ACA (29.7% vs. 19.4%, p = 0.014), and a longer length of hospital stays (5.0 days vs. 4.0 days,
p = 0.043). The adjusted models showed that the COVID-19 period had a significant relationship witha higher rate of ACA (odds ratio (OR) = 1.87; 95% confidence interval (CI): 1.23–2.52; p = 0.008) andlonger length of hospital stays (OR= 2.10; 95% CI: 0.92 to 3.31; p < 0.001). (4) Conclusions: The fear of
COVID-19 may prohibit patients from seeking medical help, worsening their clinical outcomes. The
surgical community should take action to provide scientific information to relive mental stress.
Keywords: health-seeking behavior; COVID-19; appendicitis; surgical care

Published: 30 August 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
Health-seeking behavior is described as “steps taken by a patient who perceives aneed for help when he or she tries to solve a medical disease” [1]. In other words, healthseeking behavior is the behavioral element of healthcare utilization and is conceptuallyassociated with clinicodemographics, socioeconomic status, access to healthcare institutes,
and the views and experiences of both the patient and medical provider [2,3]. Andersen
developed a serial behavioral model to elucidate the mechanisms associated with healthcare
utilization [2,4], defined as the quantity of people using healthcare services and estimatedby costs and medical visits [5].
The new variant of severe acute respiratory syndrome coronavirus disease 2 (SARSCoV-2), also known as coronavirus disease 2019 (COVID-19), first broke out in China in
December 2019 and rapidly spread worldwide [6]. As of 30 September 2020, 33.83 million

95Int. J. Environ. Res. Public Health 2022, 19, 10822

confirmed cases and >1.01 million deaths have been cumulatively recorded worldwide
since the start of the pandemic according to the World Health Organization statistics [7].
The highly contagious potential of COVID-19 deepened the strain on healthcare institutes
and resulted in reduced healthcare utilization worldwide under a limited supply of medical
resources. Furthermore, patients with severe illnesses might be unwilling to visit a highly
contagious hospital due to their fear of being infected by the virus [8].
Acute appendicitis is not only one of the most common causes of acute abdominal
diseases in adults [9], but also one of the most common reasons for general emergency
surgery worldwide [10]. With the advancement of minimally invasive surgery, mostpatients can recover early after a timely appendectomy [11]. However, the COVID-19
pandemic became a barrier to timely surgical treatment, which increased the ACA rate incountries with compromised medical supplies [12,13].
Taiwan is very close to the coast of China and was predicted to be likely to experiencea high number of cases in 2020 due to the many flights that occur between the countries.
However, Taiwan surprisingly controlled its sporadic outbreaks of COVID-19 well byimplementing several public health responses [14] and did not suffer a shortage of medical
resources. As a result, it provides a unique environment in which to validate if healthseeking behavior may have interfered with clinical outcomes even in a resource-unlimited
setting. In this study, we hypothesized that the mental panic caused by the COVID-19
pandemic may have worsened the clinical outcomes of patients. Thus, we aimed to analyze
the rates of uncomplicated/complicated appendicitis during the COVID-19 pandemic andcompare them with those of previous years.
The rest of this paper is organized as follows. Section 2 is the Materials and Methods, Section 3 is the Results, Section 4 is the Discussion, and Section 5 is the Research
Conclusions.
2. Materials and Methods
A retrospective study was conducted to review the records of adult patients with acuteappendicitis (≥20 years) in one academic center during the COVID-19 epidemic period
from January to June 2020. Next, patients with acute appendicitis from the same months,
January to June, in 2017, 2018, and 2019 were considered as the control group. The sameinterval every year was analyzed because a previous study demonstrated that the incidenceof acute appendicitis was associated with seasonal variation [15]. The exclusion criteria
were being pregnant or pathological findings of appendiceal tumors.
The data analyzed included clinical demographics such as age, gender, body massindex (BMI), residence in Taipei/New Taipei City, body temperature measured at the
emergency room (ER), white blood-cell count (WBC), and the time interval from symptom
onset to ER arrival. The weighted Charlson comorbidity index (CCI) score was used toaccount for the comorbidity burden [16,17]. Further, daily confirmed COVID-19 cases wererecorded to represent the severity of the pandemic. The primary outcome measures were
the occurrence of ACA defined as abscess observed in computed tomography, presenceof appendiceal perforation determined by surgical documentation, or a description ofgangrenous appendicitis assessed by pathological reports [10]. The secondary outcome
measure was inpatient length of stay.
Furthermore, the trend of the total number of surgeries (elective and emergent operations) during the study periods was analyzed to reflect the overall impact of COVID-19 on
surgical patients seeking medical services.
Statistical analyses were performed using the Statistical Package for the Social Sciences(SPSS) version 26.0 software (IBM SPSS 26.0, Armonk, NY, USA). Continuous variables (age,body mass index, time from ER visit to surgery, white blood count, and length of hospital
stays) were presented as medians with interquartile ranges (IQRs), and categorical variables(gender, category of Charlson comorbidity index score, residence in Taipei/New Taipei City,
category of duration of symptoms until presentation to ER, body temperature > 38 degrees
Celsius, and appendectomy performed) were expressed as numbers (percentages). Categor-

96Int. J. Environ. Res. Public Health 2022, 19, 10822

ical variables were compared using the Chi-square test or Fisher’s exact test (the numbers
were <5). Continuous variables between groups were compared using the Mann–Whitney
U test. The binary logistic regression model was used on associated variables to determine
the odds of ACA occurrence. Furthermore, a linear regression model was created to predictthe length of hospital stays. Statistical significance was assumed at p < 0.05.
3. Results
During the 26-week COVID-19 study period, 145 patients with acute appendicitis
were treated in our institute (Table 1). No patient was diagnosed with COVID-19. Amongthem, 43 (29.7%) patients had ACA. The rates of appendectomy in the ACA and non-ACA
groups were 83.7% and 98.0%, respectively. Patients with ACA had a significantly higher
rate of body temperature > 38 ◦ C (46.5% vs. 26.5%, p = 0.019) and a significantly higher rateof symptom duration until presentation to the ER within >48 h (20.9% vs. 8.8%, p = 0.001)
in comparison with the non-ACA group. No differences were observed in the median age,gender, BMI, or CCI score category between adults with ACA and non-ACA.
Table 1. Clinical variables among 145 adult patients with acute appendicitis during the COVID-19
outbreak.

Age, year, median (IQR)
GenderFemaleMale
Body mass index, median (IQR)
Charlson comorbidity index score≤2>2
Residence in Taipei/New Taipei City
Duration of symptoms until presentation to ER, n (%)
≤48 h
>48 h
Time from ER visit to surgery (hours), median (IQR)
Body temperature > 38 degrees Celsius
White blood count, 109 /L, median (IQR)
Appendectomy performed
Length of hospital stays (day), median (IQR)

Non-ComplicatedAppendicitis(N = 102)

Complicated Appendicitis(N = 43)

55.5 (36.8, 65.1)

53.3 (36.4, 65.5)

46 (45.1%)
56 (54.9%)
23.0 (21.3, 25.2)

23 (53.5%)
20 (46.5%)
23.5 (21.0, 25.1)

97 (95.1%)
5 (4.9%)93 (91.2%)

38 (88.4%)
5 (11.6%)
38 (88.4%)

93 (91.2%)
9 (8.8%)11.0 (10.0, 14.0)
27 (26.5%)
11.0 (9.0, 14.8)
100 (98.0%)
4.0 (3.0, 7.0)

33 (79.1%)
9 (20.9%)
13.0 (11.0, 15.0)
20 (46.5%)
10.2 (7.9, 11.7)
36 (83.7%)
5.0 (3.0, 11.0)

p Value
0.710
0.360
0.970
0.140
0.690
0.001
0.190
0.019
0.170
0.001
0.080

ER: emergency room.

To determine the impact of the COVID-19 pandemic on the severity of appendicitis,
Figure 1 shows the association between the number of confirmed COVID-19 cases in
Taiwan and the distribution of patients with acute appendicitis. The majority of COVID-19
cases were diagnosed from week 6 to 16. The ACA ratio during the same period was
also increased.

97

Health 2022, 19, 10822

QR)

4 of 9Int. J. Environ. Res. Public Health 2022, 19, 10822

Figure 1. Weekly statistics for the number of adult patients with acute appendicitis (dotted verticalFigure 1. Weekly statistics
fornumberthe number
adult patients
with
acute
appendicitis(dottedverticalbar), theof cases ofof
complicatedappendicitis(solidverticalbar), and the numberof confirmedbar), the number of casesof complicatedappendicitis
COVID-19
cases in Taiwan(dashed line). (solid vertical bar), and the number of confirmed COVID-19 cases in Taiwan (dashed line).

For comparisons during the COVID-19 period, a control cohort comprising 320 patientswas identified over 18 months between 2017 and 2019 (Table 2). No differences were
For comparisons
duringthe COVID-19
period,cohortcomprising
paobserved
betweenthe two groups
in termsaofcontrol
median age,gender,median BMI,320
categoryof
CCI
score,
andresidencein
Taipei/New
TaipeiCity.
The
median
WBC
count
(10.8
vs.
tients was identified over 18 months between 2017 and 2019 (Table 2). No differences were99.4 × 10 /L, p = 0.010) and rate of symptom duration until presentation to the ER within
observed between thetwo groups in terms of median age, gender, median BMI, category>48 h (17.2% vs. 9.1%, p = 0.011) were significantly higher during the COVID-19 periodof CCI score, and residence
Taipei/New
TaipeiCity.
The medianWBCvs.
compared into thecontrol period.
Overall,a higherincidence of
ACAcount(29.7% (10.8
vs. 19.4%,
90.014)
andofa longermedianduration
length of hospital
stays (5.0 days vs.
days,= 0.043)
9.4 × 10 /L, p = 0.010)p =and
rate
symptom
until presentation
to4.0
the
ERp withinwere observed during the COVID-19 period.
>48 h (17.2% vs. 9.1%, p = 0.011) were significantly higher during the COVID-19 period
To validate the association between the COVID-19 period and ACA onset, one binary
compared to the control
period.
Overall,a highertoincidence
ACA (29.7%
19.4%, thatp=multivariate
model
was developed
predict ACAofoccurrence(Table vs.
3), showing
boththe
COVID-19
period
(odds
ratio
(OR)
=
1.87;
95%
confidence
interval(CI):
1.23–2.52;
0.014) and a longer median length of hospital stays (5.0 days vs. 4.0 days, p = 0.043) were
= 0.008) and symptom duration until presentation to the ER within >48 h (OR = 1.70;
observed during thepCOVID-19
period.
95% CI: 1.06–2.36; p = 0.044) were significantly associated with ACA development. Further,
the adjusted linear regression model was used to predict the length of hospital stays
Table 2. Comparison ofclinicalcharacteristics
adult ofpatients
with acute(Table4), demonstrating
thatbetween
both CCI scores>2 (coefficient= 2.51;appendicitis
95% CI: 1.25 todur3.16;
p < 0.001)
and theCOVID-19
period (coefficient= 2.10; period.
95% CI: 0.92 to 3.31; p < 0.001) were
ing the COVID-19 epidemic
periodandthe preceding
3-year control
significantly associated with a longer length of hospital stays.

Control Period(N = 320)
47.9 (33.4, 63.4)

COVID-19 Period(N = 145)
55.2 (36.8, 65.4)

172 (53.8%)
98
148 (46.3%)

69 (47.6%)
76 (52.4%)

p Value
0.088
0.220

Int. J. Environ. Res. Public Health 2022, 19, 10822

Table 2. Comparison of clinical characteristics between adult patients with acute appendicitis during
the COVID-19 epidemic period and the preceding 3-year control period.

Age, year, median (IQR)
GenderFemaleMale
Body mass index, median (IQR)
Charlson comorbidity index score≤2>2
Residence in Taipei/New Taipei City
Duration of symptoms until presentation to ER, n (%)
≤48 h
>48 h
Time from ER visit to surgery (hours), median (IQR)
Body temperature > 38 degrees Celsius
White blood count, 109 /L, median (IQR)
Complicated appendicitis
Length of hospital stays (day), median (IQR)

Control Period(N = 320)

COVID-19 Period(N = 145)

47.9 (33.4, 63.4)

55.2 (36.8, 65.4)

172 (53.8%)
148 (46.3%)
23.0 (21.0, 24.7)

69 (47.6%)
76 (52.4%)
23.1 (21.3, 25.1)

301 (94.1%)
19 (5.9%)290 (90.6%)

135 (93.1%)
10 (6.9%)131 (90.3%)291 (90.9%)
29 (9.1%)
10.0 (9.0, 13.0)
129 (40.3%)
9.4 (7.6, 11.6)
62 (19.4%)
4.0 (3.0, 6.0)

120 (82.8%)
25 (17.2%)
11.0 (10.0, 14.0)
47 (32.4%)
10.8 (8.9, 13.2)
43 (29.7%)
5.0 (3.0, 8.0)

p Value
0.088
0.220
0.210
0.690
0.920
0.011
0.340
0.100
0.010
0.014
0.043

ER: emergency room.

Table 3. Adjusted multivariate analysis used to predict complicated appendicitis.
Variables
Age (every one-year increment)
Male gender (ref: female)
Body mass index
Charlson comorbidity index score > 2 (ref: ≤2)
Residence in Taipei/New Taipei City
Duration of symptoms until presentation >48 h (ref: ≤48 h)
Body temperature > 38 degrees Celsius
White blood counts
COVID-19 period (ref: control period: 2017–2019)

Odds Ratio

95% CI

p Value

1.01
0.93
0.93
1.38
0.76
1.70
0.74
1.01
1.87

0.99–1.02
0.58–1.50
0.86–1.01
0.58–3.28
0.40–1.42
1.06–2.36
0.35–1.57
0.76–2.97
1.23–2.52

0.245
0.793
0.132
0.459
0.395
0.044
0.446
0.985
0.008

Table 4. Adjusted multivariate analysis to predict inpatient length of stay.
Variables

CoefficientsAge (every one-year increment)
Male gender (ref: female)
Body mass index
Charlson comorbidity index score>2 (ref: ≤2)
Residence in Taipei/New Taipei City
Complicated appendicitis

0.16
0.64
−0.15
2.51
−3.05
2.10

95% Confident Interval
Lower Limit

Upper Limit

−0.02
−1.93
−0.60
1.25
−7.4
0.92

0.13
3.21
0.31
3.16
1.29
3.31

p Value
0.524
0.627
0.530
<0.001
0.168<0.001On the other hand, the trend of the total number of surgeries during the study periods
is shown in Figure 2. The mean numbers of elective operations in the control period andthe COVID-19 period were 23,288 and 21,526 (p = 0.180), respectively, whereas the mean
numbers of emergent operations in the control period and the COVID-19 period were4576 and 4601 (p = 0.650), respectively. Although both were not statistically significant, the
COVID-19 pandemic caused a more significantly decreased number of elective operations
compared to emergent operations. This implies that the COVID-19 pandemic not onlychanged the behavior-seeking surgical services of patients without emergent needs but
also worsened the surgical outcomes of patients with emergent needs.

99Int. J. Environ. Res. Public Health 2022, 19, 10822

6 of 9Int. J. Environ. Res. Public Health 2022, 19, 10822

Figure 2. The trend of total number of surgeries (elective and emergent operations) during the
Figure 2. The trend of total number of surgeries (elective and emergent operations) during the study
study periods.
periods.

4. Discussion
4. Discussion
The COVID-19 pandemic was the most concerning health problem in 2020 because

The COVID-19
pandemic
was thehealth problem
in 2020 daily
becauseitit caused
not only healthdisordersbutmostalsoconcerning
extensive restrictions
on people’s
lives
causednot only
healthbut also
extensive
restrictions
people’s
lives
worldwide.
Duringourdisorders
study period,
Taiwanavoidedthe worst on
effectsof thedaily
pandemic
worldwide.
During our study
Taiwanavoidedthe worst andeffectsof the ofpandemic
due to its implementationof a period,
broad publichealthinfrastructuredomains
effective
due
to
its
implementation
of
a
broad
publichealthinfrastructureanddomainsofeffective
screening, isolation/quarantine, and facial mask use [18]. Although Taiwan’s government
screening,isolation/quarantine,
and facialuse [18].
Although
Taiwan’s
government
did not implement
travel restrictions
andmask
its medical
servicesweresufficient,
this study
did
not implement
travelwere
restrictions
and itsoperations
medical services
were
study
demonstrated
that there
fewer elective
compared
tosufficient,emergent thisoperations
due to the pandemic.
Further,a higherrate ofoperations
ACA and compareda longer lengthof hospital
stays indemonstrated
that there
were fewer
electiveto emergent
operations
patients
withacute appendicitis
during rate
the COVID-19
periodin comparison
to the control
due
to thepandemic.
Further, a higher
of ACA anda longerlength of hospital
stays
periodwere
noticed.
Notably, theduring
numberofCOVID-19
confirmedperiod
COVID-19
cases wastopositivelyinpatients
withacute appendicitis
thein comparison
the concorrelated
ACA rate.
Therefore,
patients’offear
of being COVID-19
contaminatedby COVID-19
trol
periodwith
werethenoticed.
Notably,
the number
confirmedcases
was posiinfluenced
their health-seeking
and worsenedtheiroutcomes.
tivelycorrelatedwith the ACA behavior
rate. Therefore,
patients’fearclinicalof being
contaminated by
In ourinfluenced
study, symptom
duration frombehavior
ER presentation
to >48 theirh after
was found
to be
COVID-19
their health-seeking
and worsened
clinicaloutcomes.
significantly
associatedwith duration
ACA, whichwasto the resultsa previous
reportto[19].
In our study,
symptom
from
ERsimilar
presentation
to >48 ofh after
was foundbe
Prolonged appendicitis
prompttreatment
mighttolead
moreofsevereinflammation
significantly
associated withoutwith ACA,
which
was similar
the toresultsa previous
reportof theappendiceal
wall and resultin prompt
perforation
or abscessformation[20]. Furthermore,[19].
Prolonged
appendicitis
without
treatment
mightlead to more
severe inflam-amoresignificant
association
wasobserved
betweenpatientswith
acuteappendicitis
duringmation of the appendiceal wall and result in perforation or abscess formation [20].
FurtheCOVID-19
periodandACA
ascompared
with
that
in
the
last
3
years.
Our
findings
thermore, a more significant association was observed between patients with acute apcoincided duringwith thoseof publishedreports
children[12,21] andpendicitis
the COVID-19
periodandon
ACA
as comparedwithadults
that in[22].
the last 3 years.
In
additiontotheimpactofclinicalandsocioeconomic
determinants
onadults
outcomes,
Our findings coincided with those of published reports on children [12,21] and[22].
poorInhealth-seeking
behaviorhas
been
proventoincreasemorbidity
andmortality[23].
addition to the impact of clinical and socioeconomic determinants on outcomes,
Patients’behaviors
shouldbeunderstood
at
this
level
in
order
to
improvetheirclinicalpoor health-seeking behavior has been proven to increase morbidity and mortality [23].
outcomesbehaviors[24]. Basedon ourbefindings,
one epidemic
communicabledisease could
Patients’shouldunderstoodat this levelin order to improvetheirinterfere
clinical
with
the
need
to
seek
out
medicalserviceseveninaresource-competentcommunity.
Weoutcomes [24]. Based on our findings, one epidemic communicable disease could interfere
hypothesize
that
fake
news
or
misinformationonsocial
media
may
have
causedpeopletowith the need to seek out medical services even in a resource-competent community. We
feel excessive
fear,
diminishing
their willingness
traveland seek
services[25].
hypothesize
that
fake
news or misinformationonto
socialmedia
maymedical
have caused
peopleConversely, scientific information and medical knowledge can help to improve healthto feel excessive fear, diminishing their willingness to travel and seek medical services
seeking behavior, which in turn leads to a healthy condition [23]. While social media can[25]. Conversely, scientific information and medical knowledge can help to improve
rapidly disseminate information, the spreading of false information can confuse and distract
health-seeking behavior, which in turn leads to a healthy condition [23]. While social
people. Therefore, reliable evidence is a cornerstone for promoting health awarenessand implementing health policies, especially during the COVID-19 pandemic. Moreover,100

Int. J. Environ. Res. Public Health 2022, 19, 10822

as educated scientists and physicians should be the leaders in delivering heath-related
information to the public [25,26], governments should implement anti-misinformation
actions to minimize possible adverse effects.
Although healthcare systems, disparities of medical supplies, and socioeconomic
status are the main barriers to medical services [27,28], the extensive health crisis that took
place during the COVID-19 pandemic may become an additional obstacle in providing
timely surgical intervention, a key aspect of any healthcare system with both elective andemergency procedures. To overcome the shortage of medical supplies, healthcare systems
should aim to rapidly prioritize available resources and adopt new policies to deliverclinical services [29,30]. Although surgical management varies widely according to theregional culture and healthcare system, each institution has customized plans to maintainappropriate surgical services to are sufficiently protect surgical staff.
Based on our experiences, surgical need declined during the COVID-19 pandemic
even if the outbreak was not severe in Taiwan. We allocated the members of surgical teams
to other departments in order to prevent infection and enable better care for patients with
COVID-19. During the study period, more than 24,000 man-hours of labor were provided
by our surgical teams. With this help, the workload of the departments in charge of carerelating to COVID-19 was partially relieved.
This study had some limitations. The study design was retrospective and it was
conducted in one universal healthcare system, which alleviated part of the barrier of accessto healthcare services. Second, the period from 1 January to 30 June 2020 was selected
because the spread of COVID-19 was more severe during this period. The use of different
study periods might lead to different results. Third, the referral policy was associatedwith delayed medical visits but was consistent during the study period in Taiwan. Somestudies addressed the fact that general practitioners preferred to offer medical treatment
for patients with acute appendicitis without referring them to the ER [31]. Based on
our findings, the number of patients with acute appendicitis (145) was higher than the
average number during the control period (around 107). Therefore, we considered that the
number of patients who were not referred for or who received no medical treatment to berather low.
5. Conclusions
Based on our findings, the COVID-19 era had a significant relationship with a delayed
presentation to the ER and led to an increased rate of acute complicated appendicitis
in a community with normally functioning medical services and no travel restrictions.
Medical professionals play a key role in delivering accurate information and knowledge,
helping to eliminate panic, and encouraging patients to pursue medical attention promptly.
With the recommendations made in this study, the collateral impact of this communicabledisease may be partially alleviated. However, if the COVID-19 outbreak continues, medical
services will be further negatively affected. Under these circumstances, healthcare systems
should adopt other policies and rationally allocate medical resources in order to effectively
maintain medical services.
Author Contributions: Conceptualization, F.W., J.-M.W., Y.-C.L., T.-W.H., H.-L.L., H.-Y.Y. and I.-R.L.;
methodology, F.W., J.-M.W., T.-W.H., H.-Y.Y. and I.-R.L.; software, J.-M.W. and T.-W.H.; validation,
J.-M.W., T.-W.H., H.-Y.Y. and I.-R.L.; formal analysis, F.W., J.-M.W., Y.-C.L., T.-W.H., H.-L.L., H.-Y.Y.
and I.-R.L.; investigation, J.-M.W., T.-W.H., H.-Y.Y. and I.-R.L.; resources, J.-M.W. and T.-W.H.; data
curation, F.W., J.-M.W., T.-W.H., H.-Y.Y. and I.-R.L.; writing—original draft preparation, J.-M.W.,
T.-W.H., H.-Y.Y. and I.-R.L.; writing—review and editing, F.W., J.-M.W., Y.-C.L., T.-W.H., H.-L.L.,
H.-Y.Y. and I.-R.L.; visualization, J.-M.W., T.-W.H., H.-Y.Y. and I.-R.L.; supervision, H.-Y.Y. and I.-R.L.;
project administration, H.-Y.Y. and I.-R.L.; funding acquisition, H.-Y.Y. and I.-R.L. All authors haveread and agreed to the published version of the manuscript.

101

Int. J. Environ. Res. Public Health 2022, 19, 10822

Funding: This study was supported by a grant from NTUH (109-P22) and grants from the Ministry of
Science and Technology (Numbers 110-2634-F-002-032 and 110-2811-E-002-528-MY3), Taiwan, which
had no role in the study design, data collection and analysis, decision to publish, or preparation of
the manuscript.
Institutional Review Board Statement: This study was reviewed and approved by the research
ethics committee of the National Taiwan University Hospital (202008052RINC).
Informed Consent Statement: Patient consent was waived due to retrospective analysis from deidentification data.
Data Availability Statement: The data are available upon requested from the authors.
Acknowledgments: We thank the staff of the Department of Medical Research, National Taiwan
University Hospital, for the Integrated Medical Database (NTUH-iMD).
Conflicts of Interest: The authors declare no conflict of interest.

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103

International Journal ofEnvironmental Researchand Public Health
ArticleThe Lived Experiences of Women without COVID-19 inBreastfeeding Their Infants during the Pandemic: A Descriptive
Phenomenological Study
Ka-Huen Yip * , Yuk-Chiu Yip

and Wai-King Tsui

Caritas Institute of Higher Education, School of Health Sciences, 2 Chui Ling Lane, Tseung Kwan O,
New Territories, Hong Kong SAR, China; jeffreyycyip@gmail.com (Y.-C.Y.); ztsui@cihe.edu.hk (W.-K.T.)* Correspondence: khyip@cihe.edu.hk

Citation: Yip, K.-H.; Yip, Y.-C.; Tsui,
W.-K. The Lived Experiences of
Women without COVID-19 in

Abstract: The coronavirus disease 2019 (COVID-19) outbreak in 2020 has led to several changes anddisturbances in the daily lives of the general public. Particularly for new (first-time) mothers, there
has been a significant impact on the practices of raising and feeding their babies. Social distancing
measures everywhere have made mothers hesitant to breastfeed their babies anywhere else but at
home. Combined with the fear of being infected with COVID-19, the present situation has createdunprecedented barriers for breastfeeding mothers to accessing various types of support: emotional,
instrumental, informational, and appraisal. There has been no research on the influence of thepandemic on social support regarding breastfeeding in Hong Kong. This study aimed to explore
the social support and impact of COVID-19 on mothers breastfeeding their babies. Semi-structured
interviews were conducted with 20 currently breastfeeding women in Hong Kong. Colaizzi’s sevenstep method was used for data analysis. Two key themes emerged from the interview data: (1) positive
influences on breastfeeding support during COVID-19 and (2) negative influences on breastfeeding
support during COVID-19. Our findings may help mothers prepare to breastfeed their babies inplaces other than their homes.

Breastfeeding Their Infants during
the Pandemic: A Descriptive

Keywords: burnout; COVID-19 pandemic; postnatal care; psychological distress; psychosocial support

Phenomenological Study. Int. J.
Environ. Res. Public Health 2022, 19,9511. https://doi.org/10.3390/
ijerph19159511

1. Introduction

Academic Editors: Sachiko Kodera

The coronavirus [SARS-CoV-2] disease or COVID-19 rapidly spread, infecting over
five hundred million people worldwide and causing over six million deaths [1]. People with
COVID-19, as an acute respiratory, disease may have varied symptom severity [2,3]. The
World Health Organization declared COVID-19 a public health emergency of international
concern in January 2020 [4,5]. According to studies, the low mortality rates of people
under 65 years, and those with no underlying health conditions, highlight that the death
rates are higher among individuals with clinical vulnerabilities [6–8]. There is a higher
predisposition to contract COVID-19 during the third trimester of pregnancy, and among
early postpartum women [9–11]. All of these women are advised to seek urgent medical
advice and care and stay at home to protect themselves and their babies, to prevent preterm
birth and pregnancy loss during the COVID-19 pandemic [12,13]. However, this vulnerable
group of women suffer a greater impact of COVID-19 during pregnancy compared toother groups, especially during the final trimester [9,14–16]. The main impact concerns theawareness of social support and the impact of COVID-19 on mothers breastfeeding theirbabies, and the need to seek more understanding about how to provide better resourcesand emotional support for those breastfeeding women, thus decreasing their stress andimproving relationships.

and Essam A. Rashed
Received: 7 June 2022
Accepted: 1 August 2022
Published: 2 August 2022
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distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Int. J. Environ. Res. Public Health 2022, 19, 9511

COVID-19, Social Distancing Restrictions, and Postnatal Vulnerabilities
Although breastfeeding initiation rates are high in Hong Kong, where over 87% of
mothers initiate breastfeeding, only half of these mothers exclusively breastfeed theirbabies [17]. Moreover, this 2019 survey reported that Hong Kong demonstrates a big dropin the number of mothers who practice exclusive breastfeeding in the first two months
following birth, while the proportion of infants who are exclusively breastfed in the first
4–6 months is low [17]. In 2020, the Hong Kong Government increasing statutory maternity
leave to 14 weeks, and forbidding discrimination on the ground of breastfeeding, was a
new contribution to breastfeeding progress [18] and increased the number of Baby-Friendly
Hospitals to deliver the continuum of care that supports mothers to feed their babies
optimally [19]. Breastfeeding in public is still considered taboo; 40% of breastfeeding
mothers who fed their babies in public have encountered uncomfortable conditions [20].
However, breastfeeding in Hong Kong is hard; most of the women who discontinue
breastfeeding do not terminate voluntarily but due to breastfeeding issues, including thelack of preparation for breastfeeding, in-hospital support services, post-natal support, andpublic areas for breastfeeding [21,22].
With the higher transmissibility and risk of infection of the omicron strain, on 3 March2022 [23] the Hong Kong Government strongly urged its citizens to abide by social distancing measures, to avoid going out, and to abstain from participating in unnecessary orcrowded activities and gatherings, such as religious or family activities. A similar stayat-home order was implemented by the United Kingdom Government [24]. This would
prevent the spread of COVID-19 and limit COVID-19 deaths in the community.
Studies have reported that pregnant women can easily be infected during
epidemics [25–27]. During the severe acute respiratory syndrome (SARS) outbreak inHong Kong [28], women described their psychological distress, such as frustration, anxiety,sleeping disturbance, and interference in their daily lives. The postnatal period is an important transition period for women and can alter many aspects of life, including the role andidentification of the infant’s mother, motherhood [29], lifestyle [30], mother-infant bonding,confidence, and satisfaction [31]. Postnatal mothers may, in part, face an increased risk ofexperiencing mental distress during the COVID-19 pandemic [32]. Hence, transitioning
into motherhood during COVID-19 presents unique stressors, which may worsen an already vulnerable period in a woman’s life [33]. The findings of a qualitative study that
explored the perinatal experiences of mothers during COVID-19, highlighted that virtual
consultations from healthcare professionals were viewed as impersonal and caused women
to feel too embarrassed to talk about their mental health concerns [34].
A web-based survey investigated the psychological experiences of women during
COVID-19 [35]. A rapid literature review on the impact of COVID-19 on maternal mental
health and perinatal mental health services, between October 2019 and September 2020,
indicated depression and anxiety levels among mothers before October 2019 as approximately 11% and 18%, respectively [32]. A percentage of anxious mothers attained above therelevant cut-off for September 2020 indicated levels of depression (43%) and anxiety (61%)
in clinical settings. A large percentage of mothers felt worry and panic because they may
not have adequate support to fulfill their needs. There are only a few qualitative studies thathave assessed women’s psychological experiences during the COVID-19 pandemic [36,37].
Most literature has focused exclusively on the higher transmissibility and risk of infection of
the omicron strain in Hong Kong [35–37]. Using qualitative research methods can provide
a richer and more in-depth insight into which elements of social distancing restrictions
have generated an impact on mothers’ emotional health. The experiences of women breastfeeding their babies, while not being infected with COVID-19, have not been investigated.
Therefore, this study aimed to explore the in-depth experiences of this vulnerable group of
women during the COVID-19 pandemic in Hong Kong, with regards to their psychologicalstate, perspectives, feeding methods, personal life, the experience of home quarantine, andthe impact of COVID-19 on their mental health.

106Int. J. Environ. Res. Public Health 2022, 19, 9511

2. Materials and Methods
2.1. Ethical Review
The participants were informed of the details of the study and informed consent was
obtained from all of them. Information about the study was provided to the participants
before conducting interviews, including its background, purpose, and procedure. Participation was voluntary, and all participants were informed that they could withdraw fromthe study without any consequences. The confidentiality and anonymity of the participants
were strictly ensured through encryption. Only the researchers had access to the study data.
Ethical approval was obtained from the Caritas Institute of Higher Education, Researchand Ethics Committee (HRE210136).
2.2. Design
A qualitative research design was used and data were collected through individual,
semi-structured interviews, on the women’s psychological experiences with breastfeeding
their babies during COVID-19. The data were analyzed based on the phenomenologicalmethodology suggested by Colaizzi [38]. With this method, the researchers attempted tounderstand the participants’ subjective feelings and experiences by having them mentally
return to the situation itself.
2.3. Participants
The participants in this study were 20 women breastfeeding their babies in Hong Kong.
Purposive sampling was used, and to recruit participants our research team approachedpeer support groups from online media platforms such as Facebook, and groups related
to mothers in Hong Kong, such as “Hong Kong Moms,” “Little Steps,” and “Mother
Kingdom.” The interviews were conducted by telephone or video calls (e.g., Zoom). The
research team members then individually followed up with the participants who showedinterest in participating in this study, who obtained our contact information (e-mail and/or
phone) through these online platforms.
All participants met the following inclusion criteria: (1) women having experiences inproviding their child with breast milk in any form (such as direct breastfeeding or using abottle to feed their babies with their own milk) at least once during the COVID-19 pandemic,(2) women having lived in Hong Kong during the 12-month period as aforementioned, and(3) women whose babies were within an age range of 0 to 12 months.
Individual interviews continued until data saturation was reached at the 17th interview, after which no new information arose in the next three individual interviews. Table 1
shows the participants’ characteristics. We enrolled 20 women between the ages of 22
and 46, with an average age of 32 years, who breastfed their babies during the COVID-19
pandemic. Among the participants, two women completed secondary education, ninepossessed a bachelor’s degree, three possessed a master’s degree, and six graduated witha higher diploma. Regarding the breastfeeding period, 12 participants were first-time
mothers using different feeding methods, seven participants had two children, and one
participant had three children.
Table 1. Participant demographic characteristics.
Alln = 20Characteristic

≤21
22–26
27–31
32–36
37–41

107

n

%

Age
0
3
6
8
2

0
15
30
40
10

Int. J. Environ. Res. Public Health 2022, 19, 9511

Table 1. Cont.
Alln = 20Characteristic
n

%

1
0

5
0

SecondaryHigher diploma
Bachelor’s degree
Master degree

Education level
2
6
9
3

10
30
45
15

1
2
3

Number of children
12
7
1

60
35
5

42–46
≥47

2.4. Data Collection
From December 2021 to February 2022, all interviews were conducted in Chinese by
the researcher (W.K.) at the participant’s preferred time schedule in a private passwordprotected Zoom (Nasdaq, San Jose, CA, USA) meeting room. Each interview took about
60–130 min, and the same interview guide was always used to reduce variation in the datacollection process. In addition, field notes were taken during the interviews to facilitate
the collection of contextual information for data analysis. The interview guide questions
are presented in Table 2. An academic qualitative scholar and a psychological consulting
specialist were employed to form an expert panel to review and affirm the validity of theinterview questions. All participants’ interviews were audio-recorded, with their writtenconsent. If the participant exhibited emotional distress during the interview, adequate
psychological support was provided to prevent further psychological harm.
Table 2. Interview guide.
No.

Probing Questions

1.

How did you feel when you were breastfeeding your baby during the COVID-19 pandemic?
How long have you been breastfeeding your baby? Other than breastfeeding, what other feeding methods have you
been practicing for your baby?
Have your feelings changed over time when you were feeding your baby day and night during the
COVID-19 pandemic?
Can you tell me how you coped with your own psychological needs concerning breastfeeding?
In what ways do you think your coping strategies have helped you on a psychological level uponbreastfeeding experience?
Do you think you are ready to stop breastfeeding, and what makes you think you should stop breastfeeding?
Recalling on your months of breastfeeding, what are your negative and positive feelings towards breastfeeding in yourexperience during the COVID-19 pandemic?

2.
3.
4.
5.
6.
7.

2.5. Data Analysis and TrustworthinessIn addition, data analysis was conducted immediately after data collection using
NVivo Version 12, QSR International [39]. Two research team members (K.H.Y. and Y.C.Y.)
transcribed the interview tapes verbatim and, subsequently, translated all transcripts
into English. Back translation was undertaken by another team member (W.K.T.) and aprofessional translator to ensure semantic equivalence. The data and field notes of allinterviews were organized systematically according to Colaizzi’s phenomenological analysis method [38]. This method follows a seven-step approach to expose emergent themes,
namely (i) to familiarize the researchers with the collected data, (ii) to identify the significant statements, (iii) to formulate the meanings and the use of reflection by the researchers,

108Int. J. Environ. Res. Public Health 2022, 19, 9511

(iv) to cluster and define themes, (v) to formulate an exhaustive description, (vi) to create afundamental composition of the phenomenon, and (vii) to verify the exhaustive description
and fundamental composition [38].
In particular, all recorded interview data were transcribed verbatim and analyzed
throughout the data analysis process [38]. All the interview data included meaningfulstatements and gave rise to different themes, which were independently reviewed by two
research team members (K.H.Y. and Y.C.Y.). They looked at all the analyzed data andcompared the subthemes, identifying the differences and similarities between them. The
authors of this study (K.H.Y., Y.C.Y., and W.K.T.) discussed the results of their analyses
to reach a consensus. The study referenced the stringent criteria established by Lincoln
and Guba to ensure a high level of rigor in this study [40–42]. It demonstrates howeach criterion regarding credibility, dependability, confirmability, and transferability wasachieved through member checking.
Given credibility in creating confidence that the results are accurate, true, credible,
reliable, and believable in: (1) prolonging and diversifying engagement with each composition, (2) having interviews with comprehensive process and techniques, (3) building
investigators’ authority, (4) gathering all referential adequacy materials, and (5) having
peer debriefing sessions regularly.
Engagement through participant observation in the field was infeasible at the time ofthis research. There was a strict “multi-household gatherings at private premises” policy (asimposed by the Government of Hong Kong Special Administrative Region) that prohibited
more than two households and groups gathering in the community. This policy was toprevent the spread of the virus to the community. However, several participants fromdifferent living and working venues were approached through peer support groups that
had regular communications about the research with the authors’ affiliated institution.
After being granted ethical approval of this study, the interview guide was assessed at
two induction meetings. Two pilot interviews via Zoom were performed, and these
two interviews’ data were also integrated in the final data analysis. All research teammembers had the required data management skills, knowledge, and practical experienceand involvement of more than 4 years in qualitative research to conduct their roles.
Furthermore, field notes were applied as a tool to analyze the transcripts and utilized
to aid the documentation of the contextual information mentioned by the participants
for accurate data analysis. Moreover, the research team members were scheduled fordebriefing sessions held regularly at 2-week intervals with the Fellows from the Hong Kong
Academy of Nursing, to guarantee that there were no taken-for-granted biases, attitudes,
perspectives, or assumptions on the part of researchers.
Enhancing dependability to guarantee the results of this qualitative inquiry would be
duplicable if the inquiry existed within the same cohort: (1) to apply rich description of
study methods, (2) to set up an audit trail, (3) to progress in a series of distinct stages in thereplication of data.
The research team tried to present the study methods in detail and clearly in the
research papers; they set up a detailed track-record of the data-collection process by all
research team members. Additionally, to ensure the validity of the accounts from the participants, member-checking was conducted to enhance the clarity of the meanings derived
from the participants. Research team members read the transcripts to the participants via
telephone to clarify the interpretative meanings. It provided participants the opportunity
to recognize and accept their narrative contributions, once they have been put into theresearch report in rough and final draft forms. All research team members appraised coding
accuracy and inter-coder reliability throughout the data-analysis process.
Confirmability was maintained by empowering confidence that the results would be
confirmed/corroborated by other researchers; it had self-reflection and reflexivity. Thisapproach evaluates reflexive journals and weekly investigator meetings were applied.
Moreover, transferability was enhanced by developing the degree to which the results
can be summarized/transferred to further contexts/settings; it was achieved through data109

Int. J. Environ. Res. Public Health 2022, 19, 9511

saturation and possessing thick description. This approach comprised two main issues:(1) data saturation was satisfied when no new themes appeared from the participants; allresearch team members gained a consensus on the attainment of data saturation; (2) lengthy
description was presented in the quotes of the participants, such that the meanings of thenarrative statements from the participants could be translated and interpreted in context.
3. Results
Using phenomenological methods, we explored the psychological experiences ofwomen breastfeeding their babies during the COVID-19 pandemic. Two themes wereidentified from our observations (summarized in Table 3).
Table 3. Themes and subthemes of the study.
ThemesSubthemes

Positive influences on breastfeeding support during COVID-19

Mothers’ Desire for Support
COVID is Not All bad for Mothers

Negative influences on breastfeeding support during COVID-19

Absence of Close and Personal Professional Coaching
Mothers Being Forced to Focus on Breastfeeding
Mothers Going Back to ‘Normal’ Life

3.1. Theme 1. Positive Influences on Breastfeeding Support during COVID-19
Participants were asked a series of questions about their daily activities and how theycared for their child while breastfeeding during the COVID pandemic, including how
various personal protective measures and restricted movement to curb the spread of thevirus had affected their breastfeeding and motherhood experience.
3.1.1. Mothers’ Desire for Support
Participants described both positive and negative breastfeeding experiences due tothe COVID-19 pandemic. They did not generally tend to need support for steps toward agood breastfeeding latch. However, regarding empathic and understanding support, they
had established friendships and peer support groups to share their questions or concerns
related to breastfeeding and caring for the baby, which, in turn, aided their ability to beopen and frank. Some participants tried to search for more information, including special
Chinese traditions recommended for postpartum dietary practices during this period.
“As a breastfeeding mother, I recognize that I need a lot of support in the earlystages of feeding. Thank goodness my son has grown up and I do not need any
support during COVID-19.”“In that time, I felt alone. She (breastfeeding mothers from peer support groups
via online platform) felt like someone who is close to me and gives me all experience that I need. It was almost like we were friends. I did not even know her
face-to-face. However, I really valued what she said; I would call it a friendship, I
would think.”“If I had a young child and was without any support, I would think hard to
decide if I should continue breastfeeding. I am lucky to have joined the ‘Hong
Kong Moms,’ which is an online social platform group for breastfeeding mothers.
I came across other breastfeeding mothers in this baby group in this online social
platform during COVID-19. I mean it is different because I know I am talking tosomeone whom I only know by their nickname, someone who has experienced iton some level, which I think is very important, and is helpful with different kindsof advice and neutral opinions, such as how to deal with their own disasters.
Besides, being anonymous, I think, was also important. It was nice to have that
support . . . I have searched some interesting new ways of ginger vinegar soup
and pig’s trotter (delivering service), online or by phone, which I shall continue

110Int. J. Environ. Res. Public Health 2022, 19, 9511

to establish and use in the future. I do not need to worry about it all, and take mytime for rest.”“In the mothers’ group, we would chat mostly about breastfeeding, although we
also chatted about other topics. They would inquire how my baby was sleeping
and provided help that way; they were reliable. The other mothers in my mothers’
group were coping with their own catastrophes. It was wonderful to have that
support.”During the analysis of other interviewees’ responses, the findings relating to many
subthemes expanded. It was interesting to note how a similar issue that took place forvarious interviewees could often be interpreted as either positive or negative, dependingon the individual and their perception of the circumstances.
Some participants described that they had more privilege in their living environments
without financial burdens. They enjoyed the breastfeeding journey with a better living
environment and family support, which impacted the health outcomes of breastfeeding
and baby care. With a shift in the mindset of men toward family care, some participants
reported that their partners were willing to stay at home longer to take care of their newborn
babies. Some fathers were granted paternity leave and spent extra time to support theirspouses in breastfeeding and maternal recovery, both physically and emotionally. Someparticipants’ partners were working from home and could spend more time being present
than if they were out at work all day. This shared and collaborative care increased thebonds between partners and their babies, and strengthened the relationship between theparent and infant:“My husband is working from home because of COVID-19 and has taken 10 days
off. He enjoys so many special moments with us every day. He can help withdiaper changes and baths, take care of our baby, and let me sleep when I need
it . . . Our family lives in a house of two floors. My husband slept in the otherbedroom. He is afraid of affecting my rest and lets me have enough space to restand exercise. Maya (Filipino domestic helper) also helped to cook lot of ginger,
eggs, and chicken to increase the nutritional content in my diet. Basically, myhusband and I share every little bit of happiness that is happening in our lives.
The two of us have been here with each other for the baby, which means I havemore time to focus on breastfeeding our baby.”
3.1.2. COVID-19 Is Not All Bad for Mothers
Some participants stated that during the social distancing period, they could only
allow a few visits from their friends and relatives to greet their newborn babies. They could
therefore relax at home and spend more time attending to their babies, and rest insteadof entertaining many friends and relatives eager to see their babies. Besides, with fewer
visitors, there was less unsolicited advice, and the mothers could focus on their own wayof breastfeeding their babies:“Oh! I recall being overwhelmed by my family and friends visiting me and myson during the first to fourth weeks after giving birth to my first son. They feltthat I did not know how to breastfeed my son because my son often burst intotears. When they wanted to help comfort my son, I was often unable to react atthat time. As such, my son could easily become irritated most of the time, which
was not good for his mental growth. At that time, I especially felt a lot of pressure
from my grandma and I needed to supplement my breastfeeding with formula
milk. Now that I am breastfeeding my second son during COVID-19, I do nothave that stress, and this time I can really feel the enormous difference in feeding
him and my mental health.”
Several participants expressed that during the period of social distancing, they could
experience a slow pace of life. There was no place to go and hence, they would have all thetime to focus on how and when to breastfeed their children. Some participants, especially111

Int. J. Environ. Res. Public Health 2022, 19, 9511

those who were struggling with the pain of bleeding nipples, breast engorgement, stress,
and anxiety, welcomed this peace of mind. They believed that before social distancing, theywould be busy going out and meeting friends and family to introduce them to their babies.
As they would still be experiencing the pain and pressure of visiting friends and family,
they would rather stop breastfeeding.
“I find it very difficult for me as a breastfeeding mother because I have breastfeedingproblems, such as bleeding nipples, tearing, and a lot of pain. Having been home
for maternity leave because of COVID-19, I was able to spend time to focus onbreastfeeding; my daughter is the only reason I persist in continuing breastfeeding.
I am having a lot of visits from family, mom, or other friends and I need to put up
with the stress that they cause to me. If it were not for the COVID-19 pandemic toreduce social gatherings, I am not sure I would be successful in breastfeeding mylittle daughter. I now have had six weeks of feeding!”
Coupled with the aforementioned factors, most participants were also concerned
about maintaining their own privacy during breastfeeding. All participants were Chinese,and the common reason they had for stopping breastfeeding sooner than planned was thediscomfort and embarrassment of feeding in front of somebody else.
Participants expressed that they felt at ease at home. On the contrary, they wereembarrassed and uncomfortable breastfeeding their baby this way outside, in front ofvisitors, or in public. Several participants were accustomed to feeding their baby on a
comfortable chair, shirtless, and through a lot of skin-to-skin contact with the baby. This
allowed them to gain breastfeeding confidence to position and support their baby for agood latch, the most natural and beneficial activity for the mother and baby.
“Not having family and friends visiting me after giving birth to my baby has
allowed me time to practice how to breastfeed my daughter without having toworry about people coming, and not being able to go out during the COVID-19
pandemic has given and enhanced my confidence in breastfeeding. Having said
that, I am scared and do not have faith to take my daughter to shopping malls
and parks and feed her in public. I think there should be suitable and appropriate
facilities for privacy for breastfeeding in the malls and parks before I breastfeed
my daughter in those areas.”
During the COVID-19 pandemic, participants felt that they had extra time without anypressure regarding breastfeeding. They could perform more natural feeding in responding tohunger cues and satiety from their baby, rather than following a mother-led routine. Someparticipants also appreciated the additional help from healthcare professionals via online communicating support systems, granted without social isolation during the COVID-19 pandemic.
Participants stated that they breastfed their babies more often due to no planning in their daily
activities; for instance, they did not need to plan things like going out to work on time or getting
home quickly. This positively affected perceptions of milk supply by mothers and increased theearly weight gain of their baby.
“I am working from home during COVID-19, so I have more time to focus on
feeding my little princess as and when she wants to be fed, and not feeling rushed
because I do not need to go anywhere . . . It was nice to have somebody who
could maybe talk through text-based communication on mobile applications. I
really valued that this support was able to listen and understand what I said.
They can absolutely direct me to correct information without any confusion.
Moreover, through interactions, I had no feelings of social isolation, and it also
helped me relax.”Some participants stated that their return to work after maternity leave collided withthe worsening of the COVID-19 pandemic. Some companies allowed employees to workfrom home. This relieved the mothers’ worries about sending their babies to a nursery, or
asking family members to take up babysitting while they were away for work. The mother112

Int. J. Environ. Res. Public Health 2022, 19, 9511

could have more intimate contact with her baby and more breastfeeding; hence, the baby
could have more breast milk rather than formula milk. The participants also felt relaxed
under such work arrangements.
“I am going back to work next Monday after maternity leave, and I do not actuallyknow what uncontrollable circumstances this will bring. Despite the public panicof the COVID-19 pandemic, it is unlikely I will not be at work. This means relief
from anxiety, and I can continue my breastfeeding because after maternity leave,
the company allowed me to work from home, so I am still at home. I do not needto ask my mother to come over to my house to take care of my daughter. At
the same time, I do not need to worry about pumping breast milk as reserve forfeeding my daughter.”
3.2. Theme 2. Negative Influences on Breastfeeding Support during COVID-19
Various participants explained the pessimistic impact on their own breastfeeding
experience during the COVID-19 pandemic.
3.2.1. Absence of Close and Personal Professional Coaching
During the stay-at-home requirement during the COVID-19 pandemic, most motherswith first babies came across a common problem of having less counseling support inbreastfeeding. Instead of healthcare providers standing close by during breastfeeding, theparticipants could only describe their difficulties through phone calls and online messaging.
They could not come near, observe, and note the incorrect breastfeeding steps or practicesof the new mothers. This is considered a significant barrier for first time mothers to startbreastfeeding. The new mothers were concerned about the lack of personal support andonly having support through phone calls.
Most participants really missed having someone who could look at what was happening and help them make small changes to improve their breastfeeding. This feeling meantthat the participants lacked close and professional coaching during breastfeeding. They
expressed a sense of sadness, anxiety, and unhappiness at losing the idea of breastfeeding
they hoped to have.
“The breastfeeding class was cancelled due to the COVID-19 pandemic. He is
my first child; I have no experience at all. I feel I am not breastfeeding properly. I
do not know what to do, I am depressed and retarded, and I am scared that mybaby needs to lose weight. I do not stop using my mobile phone to search fora lot of information via the Internet. I do not know if it is right or wrong, and I
am afraid that my baby will be infected with COVID-19. I am experiencing greatlevels of anxiety, stress, and uncertainty . . . I cannot have a get diagnosis dueto the current situation. I am also experiencing a lot of pressure from the stressthat my baby might have to be hospitalized because of his excessive weight. Thishas been a very difficult process, and I have been in great pain and confusion.
Three days since we have returned home, we have received no further support. I
was crying on the phone when asking for help from the medical support. I preferface-to-face coaching and guidance. Hands-on demonstrations could also help
me to learn the position for holding my baby properly for breastfeeding. It is
more effective than a Zoom video call on a mobile device . . . My fear is that I will
look back with unhappiness on what I have endured during this time. I think thetrauma will be ongoing and far-reaching.”
A typical housing phenomenon in Hong Kong involves many underprivileged people
living in tiny “coffin homes” (accommodation in a living unit subdivided into smaller
units, each of not more than 150 square feet). Poor living environment, lower educational
level, and economic burden impact the emotional stress of participants’ continuation ofbreastfeeding and affect baby care. Maintaining social distancing among the residents ofthese “coffin homes” is impossible, and mothers with newborn babies were concerned about113

Int. J. Environ. Res. Public Health 2022, 19, 9511

becoming infected with COVID-19 easily. Further, due to social distancing restrictions,some participants felt that medical support was becoming remote and only available
through online messaging or phone calls.
“My husband is a hawker with leg amputation, and I have a huge financial
burden of the family. Contrarily to other families, I have only obtained secondary
education level without any professional qualification. Everything made me
afraid and anxious, and I do not know how to express. After the completion of
my maternity leave, I must return to work as soon as possible. I do not think I
would have continued breastfeeding; it was a really big impact for me . . . I was
so stressed because I could not convey and do not know how to convey. Having
been seriously disturbed by COVID-19 now, I need to stay in my tiny coffin homewith my baby, which is making me worried and anxious, fearing that my baby
and I will be infected. I also avoid going to the Maternal and Child Health Center
for routine health checks. So, I have not been seeing nurses and receiving theircoaching on breastfeeding for a while. I did not realize there was any support
elsewhere . . . My failures in breastfeeding made me feel very frustrated; tears
always streamed down my face at night.”
Many participants mentioned that during social distancing restrictions, they could
not get together and have physical meetings with other breastfeeding mothers’ support
groups. They stated that they wished to connect with others by sharing their experienceand seeking support for their breastfeeding. Some participants felt they were alone in theprocess. Certain participants reported that they were isolated and lacked the emotional
support and care from their family members. This was harmful to their physical andpsychological health.
“Although my husband was by my side, I still value the presence of my motheraccompanying me. My mother was amazing and a strong supporter of breastfeeding. I really hoped she could come to visit me after my son was born. However,in the current situation, she stays in Mainland China and cannot come. Despite
being on WeChat [video message], it is not the same as having my mother by
my side. I feel like [I and my baby] need her, not just for help, but for emotional
support. I also struggle and feel depressed. It seems everything is becoming
difficult and frustrating. On the other hand, I want to participate in peer group
activities and receive face-to-face support. The lack of support for everything
really makes me sad. It is not what I expected. I read a lot of breastfeeding
information before my son was born. I could not, however, receive the support I
thought would be available.”
However, some participants recognized that staying at home with other toddlers
helped reduce some pressure. Others found that although there was no need to take theirolder children outside, they would still need to help them with online learning (homeschool
via Zoom). These daily issues intimidated participants with regard to breastfeeding. Someparticipants had partners working long hours outside, and who were not able to provide
support and help with household chores or caring for the babies. Since the suspension offlights from the United Kingdom to Hong Kong, one participant expressed:“My mum was not able to fly back and help me with my little son. I now havemuch less time to focus on breastfeeding. My partner is an A&E (Accident &Emergency) doctor who works from dawn to late night, and sometimes 24 h or
more depending on how the workload in the hospital develops. I am at homewith an energetic six-year-old boy, who now usually attends classes via Zoom
(video message) at home because face-to-face classes are suspended. I do not havetime to express my feelings and sometimes I do not even have time to breastfeed
my younger son because I feel that I also need to focus and take care of my older
son and simultaneously take care of all the household chores. I feel that most of
the time, I am like a single mother. In the first three months after the birth of my114

Int. J. Environ. Res. Public Health 2022, 19, 9511

first son, my mother and sisters returned from the UK to help me at my home.
Now, due to the pandemic, I do not have family or friends to help me or to help
take care of my elder son so that I can have time to focus on the baby.”
3.2.2. Mothers Being Forced to Focus on Breastfeeding
During the height of the COVID-19 pandemic, compared to other participants who
preferred having more or enough time for breastfeeding, several participants felt just the
opposite. As they had nowhere to go and not many things to do, they could focus theirattention on breastfeeding their babies. Some of them were eager to have a break and doother things to divert their attention.
“My little princess and I were afraid of being infected as if we were in confinement.
My focus on breastfeeding is very strong. This, in turn, makes me resent it because
it feels like my day revolves around it.”
Breastfeeding is a biological norm; some people still avoid having the taboo of breastfeeding in public, and mothers are still being forced to pump in toilets or storerooms insome shopping malls in Hong Kong. Several participants try to escape complaints orunpleasant experiences of breastfeeding in public; rather, they selected breastfeeding theirbaby at home.
“I fear men might see. They would image my breasts to seduce . . . I do not want
to breastfeed my baby uncovered in public. Now, during the pandemic, I think it
is a good and the best option for me and my baby; I have had a pretty warm andfree space for breastfeeding at home.”
3.2.3. Mothers Going Back to “Normal” Life
Different participants had varying views on breastfeeding during the pandemic. Someparticipants would rather not conduct breastfeeding anywhere except at home. Some participants were concerned that they might miss the unique experience of breastfeeding andlack the opportunity to share the experience and exchange their knowledge of breastfeedingwith other new mothers. One of them said that she was anxious and lost in not knowing
what will happen in the future, once the COVID-19 pandemic is over.
“If there is a chance, I can breastfeed together with other like-minded mothersin public places, such as the Maternal and Child Health Center, not only in
my home. Everyone can share with and coach each other on how to get into
the correct position to conduct breastfeeding in various situations and different
circumstances. It is a pity that there is now no such get together for happy
hours and support. I feel lonely. I do not know how to breastfeed my baby withconfidence in public. As such, I might give up breastfeeding earlier than I haveplanned.”Some participants resumed work soon after giving birth to their babies. During the
pandemic, they were required to work from home. They would have to breastfeed theirbabies during working hours. In view of the inconvenience and disturbance that this may
cause to work, this was an influencing factor for working mothers to give up breastfeeding
early. The experience of one participant, who worked in the Accident and Emergency
Department in a hospital, was different from those working at home. She stated that she
was buried in her work and was required to wear personal protective equipment (PPE) all
the time. She was tired, stressed, hot, sweaty, and dehydrated, with no time to rest. Thisparticipant could feel her engorged breasts and discomfort and had less milk.
“I work in the A&E unit, and in order to maintain infection control safety measures, I am required to wear PPE all the time. As I do not want to waste the PPE
stock, I try to drink as little water as possible to avoid going to the toilet during
my shift duty, and hence do not need to change my PPE. I am feeling helpless115

Int. J. Environ. Res. Public Health 2022, 19, 9511

because of work affecting my breastfeeding planning. I feel like I am pumping
less milk at the moment.”
4. Discussion
The current study used a phenomenological approach to explore women’s psychological experiences of breastfeeding during the COVID-19 pandemic, specifically in relation to
how social distancing measures affected their baby feeding decisions. Two major themes
demonstrated how different psychological and difficult experiences emerged in maintaining breastfeeding, alongside the impact that social distancing measures created for them.
The World Health Organization assured mothers that breastfeeding in public is safe,with suitable infection control measures during the COVID-19 pandemic [43]. However, thetaboo around breastfeeding in public places in Hong Kong [20] remains. Some participantsof this study reflected their worries about breastfeeding newborns while being infectedwith COVID-19. It is, therefore, important for policymakers to collaborate with healthcare
professionals to advance information on social media that helps encourage mothers tocontinue breastfeeding their babies, despite the worsening COVID-19 situation.
Breastfeeding mothers have been required to face the COVID-19 pandemic and sufferfrom anxiety [44]; therefore, there was impeded access to healthcare services, with individuals avoiding hospitals and canceling their healthcare appointments [45,46]. The participants’dialogues in this study, as in some other studies, demonstrated that breastfeeding protects
maternal and infant health in both the short- and long-term [47–52]. Moreover, breastfeeding protects against infections, providing passive and long-lasting active immunity.
Both physical and psychological aspects of mothers are affected by breastfeeding, such as
reducing the risk of inflammation, improving sleep distress, and reducing psychologicaldistress [53–55]. The findings of this study also indicate concerns regarding their experiences during the COVID-19 pandemic; their mental health is safeguarded when they fulfill
their breastfeeding goals [47–55]. Conversely, the mothers’ feelings of grief, depression,and trauma increase if they cannot meet their breastfeeding goals.
The findings of this study also demonstrated the mothers’ willingness to discontinue
breastfeeding during COVID-19 due to the lack of direct support and reassurance fromhealthcare professionals, and their relatives. During this critical period, mothers withyounger babies are likelier to face varying difficulties and feel uncomfortable with regards
to breastfeeding, thus being more likely to discontinue breastfeeding [56,57]. In addition,breastfeeding mothers with older babies expressed that the lack of face-to-face inquiries
and support also affected them during the pandemic [58,59]. To avoid contact with otherpeople due to social distancing requirements, breastfeeding mothers expressed that they
had negative feelings and stayed at home with limited social contact with people, resulting
in declining social opportunities and support services [59]. It is vital to provide professional
peer and healthcare support, and communication, during the early weeks of breastfeedingfor mothers and their babies, to enhance successful breastfeeding [60–63]. During the early
weeks of breastfeeding, participants of this study who particularly required care terminatedbreastfeeding due to an absence of practical, emotional, professional peer and healthcare
support, and social gatherings. Some participants were also dissatisfied and blamedsocial distancing requirements during COVID-19, which affected their decision not tocontinue breastfeeding. Previous studies reported breastfeeding mothers had an increased
risk of postnatal depression [64–67]; our analysis showed few participants planned toextend breastfeeding for a longer time. Importantly, clinicians should track the effectsof breastfeeding duration on subsequent physical and mental health outcomes in thispopulation group.
There seems to be a more pressing need for support for those women who are unable toachieve their breastfeeding goals, or who are struggling with a lack of support [68,69]. Studies
reported increasing rates of perinatal depression associated with the COVID-19 lockdown
in countries worldwide [64,70,71], including worry about health for both mother and baby,
professional healthcare support deficits, economic burden effects, strict controls on movement,

116Int. J. Environ. Res. Public Health 2022, 19, 9511

and social isolation. Policymakers must devise ways to provide holistic support to mothers,babies, and their families whenever they require it [72,73]. According to this study’s findings,
the experiences of participants during the COVID-19 pandemic were significantly unique anddifferent. There was an optimistic perception, with most of the participants stating that they
had an optimistic impact on their experiences of breastfeeding. They were forced to focus
more on breastfeeding their baby during COVID-19 and ensuring social isolation. Participants
expressed that they could access enough things for breastfeeding support, such as increasingbreastfeeding time for mothers and their babies, had fewer disturbances, kept away from
unpleasant opinions, and had extra communication time with supportive partners [65,74,75].
We believe that traditional beliefs and postpartum practices in mothers worldwide mainly
involve direct care, dietary intake, and sufficient rest [76–79]. However, it is very important to
pay attention to mothers’ mental health during the current pandemic.
Some studies state that mothers living on low incomes, due to poverty caused byhousing factors and lower education levels, are more likely to discontinue breastfeeding in
the early weeks [80,81]. Breastfeeding mothers are required to cope with more problems
and have less support within the current pandemic. The findings of our study indicated thatparticipants without a bachelor’s degree described having a more undesirable perspectiveof the lockdown and faced unnecessary hurdles while breastfeeding, especially those who
had less support in their living environments. Housing factors, like tiny, sky-high, anddense living spaces, influenced mothers in poorer families who had fewer perceived goodparenting qualities, affecting mother-baby relationships [78]. Based on the participants’experiences, the disadvantages of living circumstances and low educational qualifications
both affected successful breastfeeding.
An optimistic impact was observed in participants of this study who were more privileged in their living circumstances, such as having more spacious homes, access to plenty ofverdant spaces for exercise and rest, and fewer economic worries. Different components ofliving circumstances affect the health outcomes of breastfeeding and baby care. Past studies
show that mothers having difficulty with the milk ejection reflex showed an association
with stress and lactation [82–84]. While mothers with poor living circumstances experiencehigher levels of emotional stress regarding lactation, mothers without living space problems
may perceive breastfeeding as a unique and satisfactory experience. Mothers with poor
living conditions may experience, for example, the effect of postnatal depression, financial
problems, emotional stress, and social isolation during breastfeeding, making the care for
their baby more difficult [85,86].
Policymakers, healthcare professionals, and practitioners have been challenged tosupport a range of creative and resilient responses to strengthen the provision of breastfeeding for both mothers and their babies during COVID-19 [85]. To prevent transmissionof the virus, and any other hurdles, behavioral support must be provided for all mothers
in terms of positive and accurate information through in-person and telephone services,
including telehealth formats through the internet, peer group support, and community
resources. Some participants prefer to have more accurate messages through face-to-facebreastfeeding support, rather than online delivery support services.
Limitations and Future Directions
This study is limited as recruitment occurred from support sites; therefore, theirexperiences do not necessarily represent mothers in general. The research provided strong
evidence that social distancing and social isolation, due to Omicron in Spring 2022, have
had an adverse cumulative health impact on breastfeeding mothers, both physically andmentally. We used telephonic and Zoom interviews to collect data, which is an increasingly
common approach during the COVID-19 pandemic. Nevertheless, our sample excluded
participants with significant emotional stress, or a lack of internet connection, which areimportant factors for discontinuing breastfeeding during the early weeks, according to ourfindings. While a qualitative study addresses a small group of participants, the findings ofour study were not intended to generalize but to explore how mothers understood their117

Int. J. Environ. Res. Public Health 2022, 19, 9511

perceived reality during the COVID-19 pandemic with regards to practical preparation forbreastfeeding, taking care of their child, and supporting their families. Thus, it would be
advisable to carry out similar studies in other contexts, such as for different geographic
and cultural groups of mothers, to acquire a more thorough understanding of this issue.
5. Conclusions
This study used a qualitative approach to explore social support and how COVID-19
impacted mothers breastfeeding their babies during the COVID-19 pandemic in HongKong. An analysis of the results showed various positive and negative effects on breastfeeding mothers due to social distancing and social isolation, and different social economic
factors, together with their complicated feelings particularly affecting breastfeeding mothers’ feelings of guilt, anxiety, and depression. This occurrence may introduce potential
risks to breastfeeding if mothers cannot meet their breastfeeding goals. The results also
indicate that policymakers and healthcare professionals should implement tailored strategies to support new breastfeeding mothers, their babies, and families to better prepare
them, particularly among socioeconomically disadvantaged groups, to breastfeed in places
other than their homes during the pandemic. Further studies should focus on suitable
psychological interventions aimed at instilling positive emotions in breastfeeding mothers,
alleviating negative emotional states, and protecting their wellbeing in Hong Kong.
Author Contributions: Conceptualization, Y.-C.Y.; Data curation, Y.-C.Y.; Formal analysis, Y.-C.Y.,
K.-H.Y.; Investigation, Y.-C.Y., K.-H.Y., W.-K.T.; Methodology, Y.-C.Y.; Project administration, Y.-C.Y.,
K.-H.Y., W.-K.T.; Resources, K.-H.Y.; Software, K.-H.Y.; Validation, K.-H.Y., W.-K.T.; Writing—originaldraft, Y.-C.Y.; Writing—review & editing, Y.-C.Y., K.-H.Y. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was funded by the Institute Development Grant (IDG) of Caritas Institute ofHigher Education, grant number IDG210217.
Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Caritas Institute of Higher Education, Research and Ethics
Committee (Ref. no. HRE210136).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The interview guide has been provided in the manuscript using a table.
The transcripts that contain private and confidential data, such as the wards and the sites of practiceof the participants, will not be publicly available to protect the participants’ privacy.
Conflicts of Interest: The authors declare no conflict of interest.

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122International Journal ofEnvironmental Researchand Public Health
ArticleSurvey of Pharmacists’ Knowledge, Attitudes, and Practices(KAP) concerning COVID-19 Infection Control after Being
Involved in Vaccine Preparation: A Cross-Sectional Study
Nobuyuki Wakui 1, * , Mayumi Kikuchi 2 , Risa Ebizuka 1 , Takahiro Yanagiya 2 , Chikako Togawa 1 ,Raini Matsuoka 1 , Nobutomo Ikarashi 3 , Miho Yamamura 1 , Shunsuke Shirozu 1 , Yoshiaki Machida 1 ,Kenichi Suzuki 1 and Hajime Kato 2
1

2
3

*

Citation: Wakui, N.; Kikuchi, M.;
Ebizuka, R.; Yanagiya, T.; Togawa, C.;
Matsuoka, R.; Ikarashi, N.;
Yamamura, M.; Shirozu, S.; Machida,Y.; et al. Survey of Pharmacists’Knowledge, Attitudes, and Practices(KAP) concerning COVID-19
Infection Control after Being
Involved in Vaccine Preparation: A
Cross-Sectional Study. Int. J. Environ.
Res. Public Health 2022, 19, 9035.
https://doi.org/10.3390/
ijerph19159035
Academic Editors: Sachiko Kodera
and Essam A. Rashed
Received: 28 June 2022
Accepted: 23 July 2022
Published: 25 July 2022

Division of Applied Pharmaceutical Education and Research, Hoshi University, 2-4-41 Ebara,
Shinagawa-ku, Tokyo 142-8501, Japan; s171038@hoshi.ac.jp (R.E.); s181156@hoshi.ac.jp (C.T.);s181219@hoshi.ac.jp (R.M.); m-yamamura@hoshi.ac.jp (M.Y.); s-shirozu@hoshi.ac.jp (S.S.);
y-machida@hoshi.ac.jp (Y.M.); kenichi-suzuki@hoshi.ac.jp (K.S.)
Shinagawa Pharmaceutical Association, 2-4-2 Nakanobu, Shinagawa-ku, Tokyo 142-0053, Japan;
tomato_5mk@yahoo.co.jp (M.K.); yanagiya@tnb.co.jp (T.Y.); sina89@jewel.ocn.ne.jp (H.K.)
Department of Biomolecular Pharmacology, Hoshi University, 2-4-41 Ebara, Shinagawa-ku,
Tokyo 142-8501, Japan; ikarashi@hoshi.ac.jp
Correspondence: n-wakui@hoshi.ac.jp; Tel.: +81-3-5498-5760

Abstract: Vaccination is crucial for preventing the spread of COVID-19. Vaccination for COVID-19 was
implemented in Japan in community units, and community pharmacists were engaged in vaccine
preparation. Capturing the knowledge, attitudes, and practices (KAP) of pharmacists regarding
COVID-19 infection control is important for developing future community health action strategies
and plans. We conducted a cross-sectional study among 141 pharmacists who were members ofa pharmacist association in the Shinagawa Ward of Tokyo (1–31 July 2021) using a Google online
questionnaire. The questionnaire included demographic information and KAP questions regarding
COVID-19. A correlation test was used for analyzing KAP scores. Significant correlations were foundamong all KAP scores. Stepwise logistic regression analysis showed “age” as a significant knowledge
factor and “marriage”, “pharmacist careers”, “information source: official government website”, and“information source: word of mouth from family and friends” as significant attitude factors. Good
KAP scores were recorded in this study, indicating increased comprehension of infection control
measures and increased knowledge scores, as pharmacy pharmacists were practically involved in
COVID-19 infection control measures through vaccine preparation. Policymakers should understandthe value of pharmacists as healthcare professionals and should enhance public health through theeffective use of pharmacists.
Keywords: vaccinations; pharmacist; COVID-19; KAP; public health; infection control

Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affil-

1. Introduction

iations.

Severe acute respiratory syndrome caused by a novel coronavirus was first reported
in China in December 2019 [1]. Later, this severe acute respiratory syndrome coronavirus
2 (SARS-CoV-2) was found to be highly contagious and resulted in life-threatening andcomplex respiratory illnesses [2]. The World Health Organization declared a public healthemergency in January 2020, which made it an international concern [3]. Currently, thespread of SARS-CoV-2 has become a serious public health issue worldwide [4].
With the spread of SARS-CoV-2 and the unprecedented growing demand for associated healthcare, public health measures to stop the spread of infection are crucial [5].
Many people worldwide obtain information on infectious disease control by professionals
through the mass media [6,7]. However, because the prevalence of COVID-19 varies widelyin different regions [8,9], direct responses and guidance according to the context of each

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Int. J. Environ. Res. Public Health 2022, 19, 9035

region are required. In this regard, pharmacists from all over the world are in a position
to be directly or indirectly involved with patients and the local population, thus rapidly
responding to public health challenges to meet local circumstances [10] and playing a keyrole in preventing the spread of COVID-19 [11]. Specifically, these responses include administering vaccines [12,13], educating the population regarding practices and attitudes toward
preventing infection [14], participating in the supply and management of medicines [14],and actively conducting drug treatment evaluations and side effect monitoring [15]. These
responses have curtailed the spread of COVID-19 and reduced the burden on medical facilities. As a result, in addition to contributing to the avoidance of healthcare disruptions [16],pharmacists provided added value to patients and the entire healthcare system and gained
high confidence from the local population [17]. In Japan, pharmacists have been engaged
in vaccine filling and the drug management of vaccinations for COVID-19 since June 2021.
Thereby, vaccinations of local residents were efficiently carried out.
Awareness toward countermeasures is important for COVID-19 infection control, anda survey of knowledge, attitudes, and practices (KAPs) related to COVID-19 according to
KAP theory [18] has been conducted on healthcare professionals in many countries [19–22].
In addition, a KAP survey for pharmacists has been reported [23–26]. However, thesesurvey findings were conducted prior to COVID-19 vaccine development, and pharmacists’
KAP information has not been reported.
The spread of the COVID-19 infection is still ongoing, and the prospects for complete convergence remain opaque. In such a situation, pharmacists play an important role
through their involvement in a vaccination program to prevent the spread of the COVID-19
infection. Awareness of the KAP status for COVID-19 infection control among pharmacists
after being involved with vaccinations will be informative in determining future community health action strategies and plans from the perspective of improving and promoting
community public health. Therefore, we conducted a KAP survey on infection control
measures for COVID-19 among pharmacists.
2. Materials and Methods
2.1. Study Design and SettingThis was a cross-sectional study conducted with pharmacists who were members ofa pharmacist association in the Shinagawa Ward of Tokyo, Japan. Data collection took
place between 1 July 2021, and 31 July 2021. The survey was conducted online via a web
questionnaire that used a Google form. Participants responded anonymously to questions
on demographics and their KAP regarding COVID-19. Of the 255 pharmacists surveyed,
there were 141 respondents (a response rate of 55.3%), and all responded to all items andwere included in the analysis. All participants responded anonymously (Figure 1).

Figure 1. Flowchart of the cross-sectional study.

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Int. J. Environ. Res. Public Health 2022, 19, 9035

2.2. Content of the Survey Instrument
Data were collected from pharmacists to assess KAP on infection control measures for
COVID-19. The questionnaire consisted of two parts: demographic information and KAPquestions. The KAP questions assessed COVID-19 knowledge, attitudes, and practices. The
knowledge section consisted of 17 questions, and participants responded to each question
with a selection formula of “Yes”, “No”, and “Don’t know”. These questions assessed
their level of knowledge of the clinical manifestations, transmission route, prevention, andmanagement of COVID-19. The attitude section consisted of 16 questions that assessed
attitudes regarding vaccinating against COVID-19 and preventing the spread of infection.
The practice section consisted of 11 questions that assessed the measures that were taken
during daily living to prevent COVID-19 infection. Regarding the items of attitudes andpractices, the selection formula was “yes” or “no”. In addition, as an assessment of theinternal consistency of the KAP questionnaire, a reliability analysis was performed using
Cronbach’s alpha coefficient, which showed good reliability values of 0.80 for knowledge
and 0.74 for attitudes, and an acceptable value of 0.53 for practices.
2.3. Definition of Terms
The Japanese School Education Law was revised in 2006, and pharmacy universities
shifted from a 4-year system to the current 6-year system to strengthen clinical education.
With this revision, only those who had been educated at a 6-year pharmacy university
could take the national pharmacist examination. Therefore, some of the participants in thissurvey were pharmacists who graduated from a 4-year pharmacy school before the revisionof the law, and some were pharmacists who graduated from a 6-year pharmacy school.
2.4. Study Variables
Demographic variables included age, sex (men and women), marital status (married
and unmarried), academic background (4-year university, 6-year university, master’s
degree, and doctoral degree), and the information source of COVID-19. Knowledge,
attitude, and practice scores were labeled as adequate knowledge, positive attitudes, andgood practices, respectively, for knowledge, attitude, and practice scores based on Bloom’s
cutoffs and using 80% or more of the scores as the reference standard [27,28].
Knowledge scores were obtained by assigning 1 point for each correct answer, assigning 0 to an incorrect/“Don’t know” answer, and summing all items. Knowledge
scores ranged from 0 to 17 points, with 14 points or more labeled as adequate knowledge
regarding COVID-19 and less than 14 points as inadequate knowledge.
For attitude scores, we assigned 1 point for positive attitudes and 0 points for negative attitudes for each question item and summed those items. Attitude scores ranged
from 0 to 16 points, with 13 or more labeled as a positive attitude and less than 13 as anegative attitude.
For practice scores, a score of 1 was assigned if answers reflecting good practice wereselected, 0 was assigned if answers reflecting bad practice were chosen, and those items
were summed. Practice scores ranged from 1 to 11 points, with more than 9 points labeled
as good practice and less than 9 as bad practice.
2.5. Statistical Analysis
The resulting data were encoded, validated, and analyzed using the statistical software
R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria). By setting theGoogle form so that it could not be finished without answers, there were 0 missing data.
Numerical data were summarized with the mean and standard deviation. Categorical
data were summarized by frequency and ratio. A correlation test was used to determine
associations among knowledge, attitude, and practice scores. The assessment of the association between demographic variables and knowledge, attitude, and practice scores was
conducted using a logistic regression analysis. A p value of less than 0.05 was considered
statistically significant.

125Int. J. Environ. Res. Public Health 2022, 19, 9035

2.6. Ethical Considerations
This study was reviewed and approved by the Institutional Review Board Committeeof Hoshi University (Approval No. 2021-01). Signed informed consent was obtained fromall respondents, and participation was voluntary.
3. Results
3.1. Participant Information
The majority of the participants were women (65.2%) with a mean age of 44.8 years(SD: 11.9). Pharmacist career scores averaged 16.1 years (SD: 10.5), and 97 participants were
married (68.8%). The academic background of the 141 participants included 93 (66.0%)
bachelor’s degrees in pharmacy in the fourth grade, 32 (22.7%) bachelor’s degrees in pharmacy in the sixth grade, 13 (9.2%) master’s degrees, and 3 (2.1%) doctoral degrees. The main
sources of information on COVID-19 were 32 (22.7%) international health organizations,such as the CDC and WHO, 101 (71.6%) governmental office internet sites, 124 (87.9%)
news media sources, 50 (35.5%) social media sources, 28 (19.9%) cases of word of mouth
from family/friends, and 12 (8.5%) original papers (Table 1).
Table 1. Participant characteristics (n = 141).
VariableAge (years)
Sex, n (%)
Men
Women
Marital status, n (%)
Married
Unmarried
Academic background, n (%)
4-year university
6-year university
Master’s degree/Doctoral degree
Pharmacist career, n (%)
Less than 10 years
10 years or more and less than 20 years
Over 20 years
Information source of COVID-19, n (%)
Official website and media of the WHO or CDC
Official government website and media
Media such as news (TV, radio, internet, magazines, and newspapers)
Social media (Facebook, Twitter, and Instagram)Word of mouth from family/friends
Original paper
I don’t remember

44.8 ± 11.9
49 (34.8%)
92 (65.2%)
97 (68.8%)
44 (31.2%)
93 (66.0%)
32 (22.7%)
16 (11.3%)
46 (32.6%)
39 (27.7%)
56 (39.7%)
32 (22.7%)
101 (71.6%)
124 (87.9%)
50 (35.5%)
28 (19.9%)
12 (8.5%)
2 (1.4%)3.2. Pharmacists’ Knowledge of COVID-19
Table 2 shows the results regarding knowledge of COVID-19. Most respondents answered 15 of 17 knowledge items correctly (M = 14.4, SD = 2.5). Ten items had correct answer
rates of over 90%. Conversely, nearly 75% of the respondents could not answer the questionthat included “sneezing, runny nose, stuffy nose, and headache are not common symptomsof COVID-19.” Regarding the item “COVID-19 is caused by beta coronavirus”, only 41.8%
answered correctly, and nearly half (46.8%) answered that they did not know. Regarding thequestion that COVID-19 is transmitted by the ingestion of food, 70.9% of the respondents
answered correctly, 17% answered incorrectly, and 12.1% answered that they did not know.
On the basis of Bloom’s cutoff values, 118 (83.7%) of the pharmacists were labeled as having
good knowledge, with a knowledge score of 14 or higher, and 23 (16.3%) pharmacists werelabeled as having inadequate knowledge, with a knowledge score of less than 14.

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Int. J. Environ. Res. Public Health 2022, 19, 9035

Table 2. Respondents’ answers on COVID-19 knowledge items.

K1
K2
K3
K4
K5
K6
K7
K8
K9
K10
K11
K12
K13
K14
K15
K16
K17

COVID-19 is caused by beta coronavirus.
COVID-19 is transmitted by food intake.
Common clinical symptoms of COVID-19 are fever, dry cough,dyspnea, and malaise.
Sneezing, runny nose, stuffy nose, and headache are less common
symptoms of COVID-19.
PCR can be used to diagnose COVID-19.
Washing your hands with soap and water for at least 30 s is effective
in preventing COVID-19 infection.
Loss of taste and smell is characteristic of COVID 19 infection.
Symptom-free COVID-19 patients (during the incubation period) do not
spread the virus to others.
COVID-19 infection spreads through the infected person’s
respiratory droplets.
The incubation period of coronavirus is 1 to 14 days.
Elderly people, patients with chronic illness, DM, COPD can be severe.
Shaking hands and avoiding crowded areas and public transport can
prevent COVID-19 infection.
Keeping social distance is effective in preventing the spread of COVID-19.
Antibiotics are the first-line drug if COVID-19 infection is suspected
or confirmed.
Early response and supportive care are effective in recovering from
infection, as there is no effective treatment for COVID-19.
Isolating and treating individuals infected with COVID-19 is an effective
way to break the chain of infection.
Large-scale group activities can spread COVID-19 infection.
Knowledge Score

Correct
Answer

Incorrect
AnswerDon’t Know

59 (41.8%)
100 (70.9%)

16 (11.3%)
24 (17.0%)

66 (46.8%)
17 (12.1%)

128 (90.8%)8 (5.7%)5 (3.5%)36 (25.5%)

96 (68.1%)

9 (6.4%)126 (89.4%)

11 (7.8%)

4 (2.8%)132 (93.6%)6 (4.3%)3 (2.1%)129 (91.5%)

9 (6.4%)3 (2.1%)136 (96.5%)

0 (0%)

5 (3.5%)137 (97.2%)

1 (0.7%)3 (2.1%)126 (89.4%)
137 (97.2%)

8 (5.7%)0 (0%)

7 (5.0%)4 (2.8%)135 (95.7%)

2 (1.4%)4 (2.8%)135 (95.7%)

1 (0.7%)5 (3.5%)121 (85.8%)8 (5.7%)

12 (8.5%)

123 (87.2%)

8 (5.7%)

10 (7.1%)

138 (97.9%)

0 (0%)

3 (2.1%)138 (97.9%)

0 (0%)

3 (2.1%)14.4 ± 2.5

Of the questions K1–K17, “No” is the correct answer for questions K2: “COVID-19 is transmitted by food intake”,
K8: “Symptom-free COVID-19 patients (during the incubation period) do not spread the virus to others”, andK14: “Antibiotics are the first-line drug if COVID-19 infection is suspected or confirmed”. “Yes” is the correct
answer for all the other questions—K1, K3, K4, K5, K6, K7, K9, K10, K11, K12, and K13.

3.3. Attitudes of Pharmacists regarding COVID-19
Table 3 shows the results regarding attitudes of COVID-19. Many respondents indicated positive attitudes for 13 of 16 items (M = 13.0, SD = 2.6). In particular, for six
items, including “I would like to be vaccinated with COVID-19” (94.3%), “Pharmacists
can contribute to the promotion of vaccination of local residents from the public healthand social aspects” (91.5%), “Vaccination can prevent the spread of COVID-19” (94.3%),“all information regarding COVID-19 needs to be shared with other healthcare professionals” (96.5%), “it is the pharmacist’s social mission to work together to curb the spreadof COVID-19” (97.9%), and “I understand that this infection is very infectious” (97.9%),
more than 90% of the pharmacists responded with a positive attitude. Conversely, foritems related to ultimately being able to fully control COVID-19 infections (31.9%), there
were fewer positive attitude responses. On the basis of Bloom’s cutoff score, there were
95 (67.4%) pharmacists who were considered to have positive attitudes with an attitude
score of 13 points or more, and 46 (32.6%) pharmacists who were considered to havenegative attitudes with an attitude score of less than 13 points.
Table 3. Respondents’ answers on COVID-19 attitudes.

A1
A2
A3
A4

I would like to be vaccinated with COVID-19 (answer “yes” if already vaccinated).
I am motivated to be involved in citizens’ vaccination work (such as vaccine filling).
I don’t think there is any problem even if vaccination by a pharmacist is approved asin overseas.
If vaccination by a pharmacist is approved, I am willing to vaccinate the public.

127

Yes

No

133 (94.3%)
112 (79.4%)

8 (5.7%)29 (20.6%)

96 (68.1%)

45 (31.9%)

90 (63.8%)

51 (36.2%)

Int. J. Environ. Res. Public Health 2022, 19, 9035

Table 3. Cont.

A5
A6
A7
A8
A9
A10
A11
A12
A13
A14
A15
A16

If vaccination by a pharmacist is approved at a dispensing pharmacy like overseas, I think it
will be a great advantage for local residents.
If vaccination becomes possible at local pharmacies, I think it can contribute to improving
the vaccination rate of local residents.
I think pharmacists can contribute to vaccination of local residents from the public healthand social aspects.
In order to popularize antibody testing, I think it’s okay for pharmacists to do antibody
testing at pharmacies.
I think that vaccination can prevent the spread of COVID-19.
Ultimately, I think we can completely control the infection of COVID-19.
I think healthcare professionals need to be aware of all information regarding COVID-19.
I think all information related to COVID-19 needs to be shared with other
healthcare professionals.
I think the spread of COVID-19 can be prevented by taking the precautionary measuresindicated by WHO and the government.
I think it is necessary to use gowns, gloves, masks, and face shields when dealing withpatients with COVID-19.
I think it is the pharmacist’s social mission to work together to curb the spread of
COVID-19 infection.
I understand that this infection is very infectious.

Yes

No

115 (81.6%)

26 (18.4%)

116 (82.3%)

25 (17.7%)

129 (91.5%)

12 (8.5%)

109 (77.3%)

32 (22.7%)

133 (94.3%)
45 (31.9%)
122 (86.5%)

8 (5.7%)
96 (68.1%)
19 (13.5%)

136 (96.5%)5 (3.5%)

97 (68.8%)

44 (31.2%)

117 (83.0%)

24 (17.0%)

138 (97.9%)

3 (2.1%)138 (97.9%)

3 (2.1%)

Attitude score13.0 ± 2.6

3.4. Practices of Pharmacists regarding COVID-19
Most of the respondents answered that they were performing 10 of 11 practice items(M = 9.4, SD = 1.4). Six items, including keeping a social distance (97.9%), performing
handwashing with soap routinely (97.9%), avoiding crowds (91.5%), handling possessionsof patients suspected of having the coronavirus with infection prevention measures (96.5%),
covering the nose and mouth with tissues and handkerchiefs when sneezing or coughing(97.2%), and ensuring that used tissues were discarded in a trash box to prevent infection(97.9%), were scored at 90% or more for good practices, and the majority also indicatedgood practices for other items. Conversely, items such as “I tried not to take the elevator
as much as possible” (41.1%) and “I have attended workshop for COVID-19 infection
prevention” (53.9%) had low values (Table 4). On the basis of Bloom’s cutoff values, 111(78.7%) of the pharmacists were labeled with good behaviors with a score of 9 or higher,
and 30 (21.3%) were labeled with bad behaviors with a score of less than 9 points.
Table 4. Respondents’ answers on COVID-19 practices.

P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11

I have attended workshop for COVID-19 infection prevention.
I keep a social distance with others to prevent infection.
When I touch the front door, which is touched by an unspecified number of people,
I open and close it while being careful of infection.
I wash my hands with soap routinely.
I avoid crowds as much as possible.
I avoid meeting friends and relatives.
I try not to touch my eyes, nose, and mouth as much as possible.
When dealing with the belongings of a patient suspected of having a corona infection,
I take care to prevent infection.
I try not to take the elevator as much as possible.
When I sneeze or cough, I cover my nose and mouth with a tissue or handkerchief.
To prevent infection, I always try to throw used tissue in the trash.
Practice score

Yes

No

76 (53.9%)
138 (97.9%)

65 (46.1%)
3 (2.1%)120 (85.1%)

21 (14.9%)

138 (97.9%)
129 (91.5%)
125 (88.7%)
124 (87.9%)

3 (2.1%)
12 (8.5%)
16 (11.3%)
17 (12.1%)

136 (96.5%)5 (3.5%)58 (41.1%)
137 (97.2%)
138 (97.9%)

83 (58.9%)
4 (2.8%)3 (2.1%)9.4 ± 1.4

128

Int. J. Environ. Res. Public Health 2022, 19, 9035

3.5. Correlations between Knowledge, Attitude, and Practice Scores
Knowledge, attitude, and practice scores showed significant associations with eachother (Table 5). A strong correlation was found in the order of knowledge and attitude(r = 0.44, p < 0.001) > attitude and practice (r = 0.32, p < 0.001) > knowledge and practice(r = 0.21, p = 0.01).
Table 5. Correlations between knowledge, attitude, and practice scores.
Correlation (r)p Value

0.44
0.21
0.32

<0.0010.01<0.001

Knowledge and Attitude
Knowledge and Practice
Attitude and Practice

3.6. Relationship between Participants’ Attributes and KAP Scores
Univariate logistic regression analysis was used to assess attribute factors associatedwith KAP (Table 6). The findings showed that chronological age (30 s, OR: 6.3, 95% CI:
1.6–25.1; 40 s, OR: 11.1, 95% CI: 2.5–48.8; 50 s, OR: 3.8, 95% CI: 1.1–13.3; and 60 s, OR:
16.2, 95% CI: 1.78–147.1), marital status (OR: 2.9, 95% CI: 1.2–7.3), academic background(6 years-university, OR: 0.3, 95% CI: 0.1–0.8), and pharmacist career (over 20 years, OR: 4.1,
95% CI: 1.2–13.9) were factors associated with knowledge. Marital status (OR: 0.42, 95%
CI: 0.18–0.97) and source of information on COVID-19 (government official website andgovernment official site, OR: 2.5, 95% CI: 1.2–5.3; and word of mouth from family/friends,
OR: 0.4, 95% CI: 0.2–0.9) were associated with attitudes. No factors associated with practices
were found (Table 6).
Table 6. Evaluation of attribute factors related to KAP using univariate logistic regression analysis.
Characteristics

CategoryAge

20–29 years
30–39 years
40–49 years
50–59 years
60 over
Men
Women
Married
Unmarried

Sex
Marital Status
Academic
background

Pharmacist
career

Source ofinformation
on COVID-19

Good Knowledge
OR
1
6.3
11.1
3.75
16.2
0.52
1
2.93
1

95% CIGood Attitude
p Value

1.58–25.08
2.52–48.84
1.06–13.31
1.78–147.06
0.21–1.28

0.009
0.001
0.041
0.013
0.154

1.18–7.31

0.021

OR
1
1.11
1.75
1.06
1.00
1.00
1
0.42
1

95% CIGood Practicep Value

0.34–3.64
0.54–5.67
0.32–3.48
0.27–3.74
0.48–2.09

0.859
0.351
0.923
1.000
0.996

0.18–0.97

0.041

1
2.49
1.85
1.33
2.46
0.53
1
1.97
1

95% CI

p Value

0.64–9.70
0.53–6.38
0.38–4.67
0.51–11.80
0.23–1.20

0.188
0.332
0.659
0.260
0.126

0.86–4.54

0.110

4-year university

1

6-year university
Master’s degree/
Doctoral degree

0.30

0.11–0.78

0.014

0.70

0.30–1.59

0.391

1.04

0.40–2.74

0.934

0.94

0.19–4.70

0.939

2.06

0.55–7.79

0.285

2.04

0.43–9.71

0.370

Less than 10 years
10 years or more and lessthan 20 years
Over 20 years
Official website and mediaof the WHO or CDC
Official government
website and media
Media such as news
(TV, radio, internet,
magazines,
and newspapers)
Social media (Facebook,
Twitter, and Instagram)Word of mouth fromfamily/friends
Original paper

1

OR

1

1

1

1

1.22

0.43–3.41

0.708

2.27

0.85–6.06

0.101

1.73

0.57–5.21

0.331

4.09

1.20–13.87

0.024

0.98

0.44–2.19

0.955

1.04

0.41–2.60

0.934

1.48

0.46–4.71

0.509

1.99

0.79–5.02

0.145

2.19

0.70–6.83

0.176

1.8

0.71–4.58

0.215

2.48

1.16–5.31

0.019

1.35

0.57–3.21

0.498

1.11

0.29–4.24

0.874

0.4

0.11–1.48

0.172

0.77

0.21–2.88

0.697

1.04

0.41–2.65

0.941

1.05

0.50–2.19

0.907

1.13

0.48–2.64

0.784

0.65

0.23–1.84

0.416

0.4

0.17–0.92

0.032

0.48

0.19–1.21

0.121

0.35

0.09–1.26

0.108

0.97

0.28–3.39

0.956

1.39

0.29–6.70

0.684

129

Int. J. Environ. Res. Public Health 2022, 19, 9035

3.7. Extraction of Factors Associated with KAP Scores by Stepwise Logistic Regression Analysis
Factors associated with KAP were extracted using stepwise multiple logistic regression
analysis with factors that had a p value less than 0.20 as a result of univariate logistic
regression analysis (Table 7). As a result, “age” was extracted as an important variablefor knowledge. Regarding attitudes, “marriage”, “academic background”, “pharmacist
careers”, “information source: official government website”, and “information source:
word of mouth from family and friends” were extracted as important factors. Regarding
practices, no factors showing an association were extracted.
Table 7. Extraction of attribute factors related to knowledge, attitudes, and practices by stepwise
logistic regression analysis.
Characteristics

CategoryAge

20–29 years
30–39 years
40–49 years
50–59 years
60 over
Men
Women
Married
Unmarried

Sex
Marital Status
Academic
background
Pharmacist
career
Source ofinformation
on COVID-19

Good Knowledge

Good Attitude

Adjusted OR

95% CI

p Value

1
6.3
11.1
3.75
16.2

1.58–25.08
2.52–48.84
1.06–13.31
1.78–147.06

0.009
0.001
0.041
0.013

Adjusted OR

95% CI

p Value0.37
1

0.15–0.90

0.029

2.81

1.26–6.26

0.012

0.38

0.16–0.91

0.030

4-year university
6-year university
Master’s degree/Doctoral degreeLess than 10 years
10 years or more and less than 20 years
Over 20 years
Official website and media of the WHOor CDC
Official government website andmedia
Media such as news (TV, radio, internet,
magazines, and newspapers)
Social media (Facebook, Twitter,and Instagram)Word of mouth from family/friends
Original paper

Stepwise logistic regression analysis was performed with Age, Sex, Marital Status, Academic Background,
Pharmacist Career, and Source of Information on COVID-19 as explanatory variables. As a result, Age was
extracted for Good Knowledge, and Marital Status, Official Government Website and Media, and Word of Mouth
from Family/Friends were extracted as important variables for Good Attitude. Variables in blank cells were notextracted as significant variables.

The odds ratios and confidence intervals for “age” in knowledge were as follows: ages
in their 20 s (OR = 6.3, 95% CI = 1.58–25.08), 30 s (OR = 11.1, 95% CI = 2.52–48.84), 40 s
(OR = 3.75, 95% CI = 1.06–13.31), 50 s (OR = 3.75, 95% CI = 1.06–13.31), and 60 s (OR = 16.2,
95% CI = 1.78–147.06).
The odds ratio and confidence interval for “marriage” in attitudes were OR = 0.3 and
95% CI = 0.10–0.94 for married people. The odds ratio and confidence interval for academic
background were OR = 0.3 and 95% CI = 0.10–0.94 for graduating from pharmacy studies ina 6-year-university. Regarding information sources, the odds ratios and confidence intervals
were OR = 2.88 and 95% CI = 1.18–7.01 for governmental official websites and media(MHLW) and OR = 0.39 and 95% CI = 0.15–1.00 for word of mouth from family/friends.
4. Discussion
In this study, we conducted a KAP survey for pharmacists involved in community
vaccine preparation. The majority of the pharmacists were shown to have positive KAP,
and most of the pharmacists’ sources were news media (n = 124, 87.9%) and government
office sites (n = 101, 71.6%). In addition, 8.5% of the pharmacists obtained informationfrom academic journals, which was less than 10% of all participants. These results showedthat conventional mass media and the internet are important information sources for130

Int. J. Environ. Res. Public Health 2022, 19, 9035

pharmacists, and the current situation in which many pharmacists in Japan have not beenable to obtain the latest information from academic journals was indicated. Knowledge
was shown to be associated with attitudes and practices in previous KAP surveys related
to COVID-19 [29–31], and in this study as well, there was a correlation among knowledge,
attitudes, and practices. Our results suggested that each item of KAP was mutually
influential on the others, and that all items were essential components.
Previous KAP surveys of COVID-19 infection control among Japanese pharmacists
reported lower KAP scores [23]. It has also been reported that the KAP scores of pharmacists
were lower than those of doctors and nurses abroad [32,33]. However, our study showed
good scores for all items of KAP. Awareness and comprehension of infection control
measures may have increased as pharmacists became practically involved in COVID-19
infection control measures through participating in vaccine preparation. As a result,knowledge scores may have increased. At the same time, the pharmacists’ KAP scores
were lower than those previously reported for doctors and nurses, possibly because thepharmacists were not involved in COVID-19 infection control. Therefore, it was consideredthat practices were indispensable to obtain good knowledge scores.
The aggregated results for attitude scores revealed that 90% or more pharmacists
answered positively for six attitude items. Of these, almost all pharmacists answered “Iwould like to be vaccinated with COVID-19” and “I think that vaccination can prevent the
spread of COVID-19”. In addition, four items such as “I think all information related to
COVID-19 needs to be shared with other healthcare professionals”, “I think pharmacists cancontribute to vaccination of the local residents from the public health and social aspects”, “I
think it is the pharmacist’s social mission to work together to curb the spread of COVID-19
infection”, and “I understand that this infection is very infections” had more than 90% of
the pharmacists answering “I would think so”. Therefore, many pharmacists indicatedpositive attitude scores. Conversely, only 31.9% of the pharmacists answered “I would
think so” to the question “Ultimately, we can completely control COVID-19 infection”,
indicating the difficulty and seriousness of COVID-19 infection control.
As a result of extracting the related factors of KAP using stepwise logistic regression
analysis, the related factors for knowledge and attitudes scores were extracted. Regardingknowledge scores, age was extracted as in previous studies [23], with the lowest knowledge
scores for the respondents in their 20s. It may be conceivable that people in their 20s have alower risk of becoming severely ill with COVID-19, and it is thought that the percentage ofpharmacy managers who manage pharmacies is small. Conversely, regarding the attitude
score, four items were extracted: “marriage”, “pharmacist careers”, “information source:
official government website”, and “information source: word of mouth from family andfriends”. For “marital status”, the attitude scores were significantly higher in unmarried
than in married individuals. This is thought to be because the percentage of womenpharmacists in Japan is higher than that of men. In other words, it is conceivable thatmarried women take more time to do housework and raise children, and it is difficult forthem to be actively involved in COVID-19 infection control as a pharmacist. In addition,
pharmacists with less than 10 years of experience had the most positive attitude scoresin pharmacist history, and pharmacists with more than 10 years and less than 20 years
had significantly lower scores. This suggested that attitude scores fluctuated according
to the length of the pharmacists’ career. Furthermore, the attitude scores of pharmacists
who obtained information from the official website of the government were significantly
high, and those of pharmacists who obtained information by word of mouth from family
and friends were significantly low. These findings suggest that the presence or absenceof active information collection is associated with attitude scores. As for the practicescores, as in the previous reports [19,34], no relevant vital factors were extracted, anda significant relationship was found between knowledge and attitudes. From this, wesuggest that practical involvement with vaccine preparation brought about knowledge andattitude improvements.

131

Int. J. Environ. Res. Public Health 2022, 19, 9035

In this study, gender was not significantly associated with KAP scores. However,a KAP survey conducted with the general public reported an association between gender and knowledge scores [35,36]. This may be due to women being more likely thanmen to feel anxious regarding a variety of issues [37], and because of their anxiety, they
may become enthusiastic regarding collecting information on infection control. As a result, their knowledge scores may be higher. Conversely, there have been reports thatno association exists between gender and knowledge in KAP surveys conducted withhealthcare professionals [28,38,39]. It has been speculated that this is because healthcare
workers are involved in COVID-19 infection control regardless of gender. On the other
hand, a KAP survey administered to medical students reported an association between
gender and knowledge scores [40]. This is probably because medical students are not yet
directly involved in COVID-19 infection control as healthcare workers. Similarly, a KAP
survey conducted with Japanese pharmacists during the period prior to vaccine preparation
engagement, i.e., when they were not directly involved in COVID-19 infection control,
showed a significant association between gender and knowledge scores [23]. From theabove, we suggest that factors related to the KAP survey of COVID-19 were influenced by
the practical environment of the study participants.
Regarding pharmacists’ sources of information, most sources were news and government office sites, and few pharmacists obtained information from academic journals. Thisis a similar result to a KAP survey conducted in Japan before vaccine development [23].
Recently, the use of SNS has also been considered a useful information resource [19,41].
However, news alone does not alter the amount of information available compared to
the general public, and SNS alone may be affected by erroneous information. As medical
professionals involved with vaccinations need to obtain general information from the news,
they also need to obtain official information distributed by the government and the latest
specialized information from academic journals.
There are several limitations to this study. First, the survey was limited to pharmacistsin Tokyo, and no results were obtained from pharmacists in other cities. Second, becauseof the cross-sectional nature of the study, a causal relationship cannot be determined.
Third, the questionnaires may not correspond exactly to what the health professional
thinks and does in clinical practice. Fourth, the small sample size of the study may notreflect the opinions of the entire pharmacist community. Further, the response rate was
55.3%—a little low. It is possible that some pharmacists have stopped participating in web
questionnaires because they are not familiar with the operation. However, in Japan, thesame information is available from media news in all regions, and in fact, pharmacists
are involved in the development and manufacture of vaccines. Therefore, it is unlikelythat knowledge, behaviors, and attitudes regarding infection control will vary significantly
from region to region and from individual to individual. The distribution of gender andage in this study was similar to the distribution of the whole pharmacist community inJapan. Therefore, it is considered that the bias of the obtained results is small.
The points that should be emphasized in this study are that this was a cross-sectional
study conducted by a Japanese pharmacist during the period when he was engaged in
vaccine preparation to prevent the spread of COVID-19 in the local population. The
survey was conducted at a time when the number of people infected with COVID-19 per
day in Tokyo was rapidly increasing and the crisis of medical collapse was being hailed.
Local pharmacists were involved in vaccine preparation, even on holidays, so that as
many citizens as possible could get their first vaccination as soon as possible. In addition,community pharmacist associations actively held training sessions on vaccine preparation
for pharmacists. Previously, pharmacists in Japan only dispensed prescriptions basedon individual pharmacies and were not openly involved in activities such as community
infection control. In view of these circumstances, the results of this study were obtained
during the first practical involvement of Japanese pharmacists in community infectious
disease control in a critical setting of a spreading COVID-19 infection. This can be useful
information when considering the public health significance and social value of community132

Int. J. Environ. Res. Public Health 2022, 19, 9035

pharmacists who were practically participating in infectious disease countermeasures,such as vaccination, not only in Japan but also globally. Indeed, pharmacists’ knowledge
and attitude scores under situations where practical involvement was sought were very
positive. Furthermore, because more than 90% of the pharmacists replied that it is the dutyof pharmacists to engage in public health, such as infection control measures, it can be said
that pharmacists are very positive and highly aware of public health efforts. Moreover,as community pharmacists’ knowledge, attitude, and practice scores toward COVID-19
were positive, the active utilization of pharmacists could lead to better community publichealth and could provide great added value to patients and the entire healthcare system.
Therefore, the use of community healthcare professionals, such as pharmacists, may expand
the potential of human medical resources and may consequently lead to the activation ofcommunity healthcare. It is hoped that policymakers understand the value of pharmacists
as human resources and effectively utilize them for the development of public health.
Furthermore, we believe that the results of this study may contribute to strengthening therole of pharmacists in communities in different parts of the world, not only in Japan.
5. Conclusions
The KAP of pharmacists on COVID-19 was very positive under situations of practicalengagement in vaccination. This suggests that utilizing pharmacists may increase theprofessionalism of pharmacists and may provide new added value to patients and thehealth care system as a whole. It is hoped that policymakers will understand the value ofpharmacists, who are healthcare professionals, as human resources, and make effective useof them, thus contributing to the development of public health.
Author Contributions: N.W., M.K. and R.E. designed the study. N.W., M.K., R.E., M.Y., T.Y., C.T.,
R.M. and H.K. collected data. N.W. and R.E. analyzed the data. N.W. wrote the manuscript. M.K.,
R.E., N.I., M.Y., S.S., Y.M., K.S. and H.K. critically revised the manuscript for important intellectual
content. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: The study was approved by the Ethics Committee of the
Hoshi University (Approval No. 2021-01).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data are not publicly available as all participants have not consented to the public disclosure of the data online. However, the data presented in this study areavailable on request from the corresponding author.
Acknowledgments: We thank all the pharmacists who participated in this study.
Conflicts of Interest: The authors declare no conflict of interest.

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135

International Journal ofEnvironmental Researchand Public Health
ArticleImpact of COVID-19 on Urban Mobility and Parking DemandDistribution: A Global Review with Case Study in
Melbourne, Australia
Biruk G. Mesfin 1 , Daniel(Jian) Sun 2 and Bo Peng 3, *
1

2
3

*

Citation: Mesfin, B.G.; Sun, D.;Peng, B. Impact of COVID-19 on
Urban Mobility and Parking DemandDistribution: A Global Review with
Case Study in Melbourne, Australia.
Int. J. Environ. Res. Public Health 2022,
19, 7665. https://doi.org/10.3390/
ijerph19137665
Academic Editors: Sachiko Kodera
and Essam A. Rashed
Received: 29 April 2022
Accepted: 14 June 2022

State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering,
Shanghai Jiao Tong University, No. 800 Dongchuan Road, Min-Hang District, Shanghai 200240, China;
birukgmesfin@sjtu.edu.cn
Smart City and Intelligent Transportation Interdisciplinary Center, College of Future Transportation,
Chang’an University, Wei-Yang District, Xi’an 710021, China; jiansun@chd.edu.cn
School of International and Public Affairs, Shanghai Jiao Tong University, No. 1954 Huashan Road,
Xu-Hui District, Shanghai 200092, China
Correspondence: bpeng@sjtu.edu.cn; Tel.: +86-21-6193-3047

Abstract: The tremendous impact of the novel coronavirus (COVID-19) on societal, political, andeconomic rhythms has given rise to a significant overall shift from pre- to post-pandemic policies.
Restrictions, stay-at-home regulations, and lockdowns have directly influenced day-to-day urban
transportation flow. The rise of door-to-door services and the demand for visiting medical facilities,
grocery stores, and restaurants has had a significant impact on urban transportation modal demand,further impacting zonal parking demand distribution. This study reviews the overall impacts ofthe pandemic on urban transportation with respect to a variety of policy changes in different cities.
The parking demand shift was investigated by exploring the during- and post-COVID-19 parking
policies of distinct metropolises. The detailed data related to Melbourne city parking, generated by
the Internet of things (IoT), such as sensors and devices, are examined. Empirical data from 2019(16 March to 26 May) and 2020 (16 March to 26 May) are explored in-depth using explanatory data
analysis to demonstrate the demand and average parking duration shifts from district to district. The
results show that the experimental zones of Docklands, Queensbery, Southbanks, Titles, and Princess
Theatre areas have experienced a decrease in percentage change of vehicle presence of 29.2%, 36.3%,
37.7%, 23.7% and 40.9%, respectively. Furthermore, on-street level analysis of Princess Theatre zone,
Lonsdale Street, Exhibition Street, Spring Street, and Little Bourke Street parking bays indicated adecrease in percentage change of vehicle presence of 38.7%, 56.4%, 12.6%, and 35.1%, respectively.
In conclusion, future potential policymaking frameworks are discussed that could provide further
guidance in stipulating epidemic prevention and control policies, particularly in relation to parking
regulations during the pandemic.

Published: 23 June 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in

Keywords: COVID-19; urban mobility; parking demand; IoT parking sensors; explanatory data
analysis; parking policies

published maps and institutional affiliations.

1. Introduction
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Since the primary case of COVID-19 was recorded in Wuhan, China, on 9 December
2019, there has been an alarming and rapid spread of the virus to every corner of the globe,
highly catalyzed by the international passenger and cargo air transport network serving
major cities in East Asia, the United States, and elsewhere [1]. Owing to the emergency,
daily routines in cities throughout the world were chaotic during the first six monthsof 2020. The normal lives of people in many countries ceased as the pandemic crisis
worsened. The human toll has been extensive due to the exponentially increasing numberof cases and deaths around the world. Social distancing, stay-at-home regulations, and137

Int. J. Environ. Res. Public Health 2022, 19, 7665

various other social restrictions have brought about a tremendous shift from the previously
defined standards of human social interaction, such as greetings and hugging [2]. On theeconomic front, the endemic, epidemic, and then the pandemic has caused many businesses
to delay or cease operations. Importing and exporting in most countries throughout
the world have been highly disrupted due to the pandemic’s impact on the aviationindustry [3–5], maritime industry [6], regional railway systems [7] and urban mobility [8].
In the manufacturing industry, sectors have been advised by the World Health Organization(WHO) to concentrate on producing medical supplies for fighting the pandemic, which
has resulted in both collaborations and disputes regarding global transactions that involve
medical supplies such as masks, gloves, and ventilators [9]. In regard to global politics, thepandemic’s impact has been dependent on nations’ health security strategy and emergency
response capacity. The political impact has expanded due to significant global politicaldecisions regarding emergency flight cancellations throughout the world. The status quo
has impacted the aviation industry greatly, with 90% of passenger flights being cancelled [5].
In China, a number of airlines discontinued flights to and from the epicenter of the virus(Wuhan) and then across the entire country to avoid an escalation of the situation fromepidemic to pandemic status. Moreover, following a loss of revenue, airlines throughout
the world reacted to these changes by shifting from passenger to cargo flights, such as
Ethiopian Airlines [10] and United Airlines [11].
As the pandemic impacted socioeconomic status, the political focus moved fromthe global to urban scale, with urban mobility being one of the most highly affected
sectors. Modal travel shifts occurred, mainly on account of social distancing and contactfree policies. Urban mass transportation, such as car-sharing, ride-sharing, and ridehailing, has been the most severely affected service types. Ride-sharing and taxi-hailingservices, as well as urban electric commercial vehicles have been on the rise in recent
years owing to AI-driven smart applications, and management and is favored due to avariety of factors, including minimized travel costs, traffic congestion, emissions and energy
issues [12–14]. However, in the aftermath of the pandemic; for example, the ride-sharing
market is predicted to have lost a quota share of 50% to 60% during 2020 [15], but this isanticipated to rise by 70% to 80% with new countermeasures, such as barriers between
drivers and passengers to adhere to social distancing restrictions.
Acute social restrictions in several highly impacted cities with stay-at-home restrictions
have resulted in the emergence of more door-to-door delivery services to satisfy the basic
living requirements of those who cannot leave home. Moreover, panic buying and the fearof contamination during the crisis have encouraged many consumers to download smart
applications from service providers such as grocery stores and restaurants. This app-based
evolution has resulted in many grocery stores recording their highest ever daily revenues
at the end of March 2020, with consumer spending up by 87.4% [16]. The nature of droplet
transmission means that the most effective virus prevention is contact avoidance, which has
led to mass-gathering restrictions for theaters, bars, sports clubs, and schools, while trips topharmacies, grocery stores, and restaurants have increased. The shift of travel destination
followed by parking demand shift forced the reshaping of parking facilities in order tosatisfy the demand. Parking garages have been used as major emergency preparedness
spaces and temporary medical centers. When considering vacant garages to micro-mobility
maintenance disruptions and lay-offs, reacting to the demands of the public and ensuring
the safety of core workers have been essential. To this end, different parking policies havebeen introduced, from the lessening of parking restrictions and penalties to fee adjustments.
The parking service providers highly affected by the pandemic include metropolises,
academic institutions, and air terminals. Parking-related income is noteworthy for small
cities, contributing to more than 30% of the yearly budget income, and there has been
an estimated 90% income loss in this sector [17]. The question arises as to how countries
will quantify and counter such socioeconomic and political disturbance. To investigate
the impact of the pandemic on different sectors, a number of data-driven studies havebeen carried out globally. One of such interest is the MOBIS-COVID19 study [18], a joint138

Int. J. Environ. Res. Public Health 2022, 19, 7665

project of ETH Zurich and the University of Basel, using mobile phone GPS tracking datafrom 3700 participants to examine the impact of COVID-19 in Switzerland. Conversely, on
designing countermeasure frameworks, recent studies and approaches, such as avoid–shift–
improve [ASI] [19] and the PASS approach [20], in which “P”, “A”, “S”, and “S” correspondto different stages of the pandemic comprising categorical targets of transportation users
and service providers, as well as governments, have been utilized.
This study assesses the impact of COVID-19 via the visualized Melbourne dataset,released by the city data administration to promote understanding on the impact of
COVID-19 on city mobility. The visualizing and comparing of on-street car parking sensor
data for the two preferred periods, between 16 March and 26 May 2019 and 16 Marchand 26 May 2020 are carried out to quantify the impact of COVID-19 on different business
districts and socioeconomic classes for better assessing the impact of COVID-19. The
remaining part of the paper is structured as follows: Section 2 reviews the impact range of
COVID-19 and analyzes the impact on urban mobility; Section 3 focuses on analyzing theimpact of the pandemic on parking demand and revenue; Section 4 visualizes the parking
demand variation during the pandemic via the case study approach, using Melbourne CBDparking data; Section 5 provides conclusions and future research recommendations.
2. Impact of COVID-19 on Urban Mobility
From world-wide expert survey results, Zhang et al. [21] presented significant impactsof COVID-19 on transport and logistics. Following the disturbance, many cities haveenforced dynamic policy approaches to minimize the negative impact of the pandemic
on the general public. The major approaches to reduce the risk of the pandemic include
providing contactless mobility modes and creating open spaces, such as pedestrian space,cycle ways, and open street developments. A few summarized examples are provided inTable 1 to illustrate cities impacted by the pandemic and forced to shift mobility policy
measures and responses.
Table 1. Summary for mobility policy measures and responses.
City, Country

Measures and Responses

Paris, France [22]The COVID-19 pandemic accelerated/prioritized theimplementation of Paris Mayor Anne Hidalgo’s 2024
vision for 650 km of cycle.

Milan, Italy [23]35 km of streets were repurposed and made open for
active modes of transportation, and a 30 km/h speed limit
was introduced.

Bogota, Colombia [24]The Colombian capital has opened 76 km of temporary
bicycle lanes, and 22 km of the new lanes were opened on
17 March 2020 by repurposing vehicle lanes.

Budapest, Hungary [25]Bike lanes were added at the edges of multi-lane streets.

Brussels, Belgium [26]The government has created 40 km of new cycle paths andturned the city center into a 20 km long walkable area.

Auckland, New Zealand [27]The first authorities to fund pop-up bike lanes, widened
sidewalks and related infrastructures during lockdown.

Helsinki and Espoo, Finland [28]The COVID-19 outbreak forced the city to open the bikeseason earlier than the previous year with a total of
351 bike stations (242 in Helsinki and 109 in Espoo).

3. Impact of COVID-19 on Parking Demand and Revenue
The parking system is a direct replica of the urban mobility disturbance by COVID-19.
For instance, the parking demand to access essential destination’s parks increased during
the pandemic [29], while on the other side, parking demand to public gathering places suchas stadiums, theaters, malls, etc., dropped. Since the flare-up of COVID-19, the USD 131B

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Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW

4 of 16Int. J. Environ. Res. Public Health 2022, 19, 7665

131B parking industry has had difficulty, mostly in hotels and aviation vicinities, encountering a drop of 50–70% commuter parking movement and more than 95% accommodation revenue losses daily [30]. In addition, a reduction in income occurred from parking
parking industry has had difficulty, mostly in hotels and aviation vicinities, encountering aauthorization
with fewer parkers and from communities halting or unwinding authorizadrop of 50–70% commuter parking movement and more than 95% accommodation revenue
tion
[31].
A
small
known
realityis that municipalities’
budgets
depend on parklosses daily [30]. In
addition,a reduction
in income occurred
fromintensely
parking authorization
ing-related
incomes.andSomedetailed
illustrations
presented inTable 2, for [31].
indicatingwith fewer parkers
fromcommunities
haltingare
or unwinding
authorizationA
thedependenceofcitiesonparkingrevenue[17].
small known reality is that municipalities’ budgets intensely depend on parking-related
incomes. Some detailed illustrations are presented in Table 2, for indicating the dependenceTable
2. Municipalities
budget[17].
from parking-related revenues (in United States Dollars, 2019–2020
of cities
on parking revenue
fiscal year).
Table 2. Municipalities budget from parking-related revenues (in United States Dollars, 2019–2020
Total Parking Related Revenue Parking Tax Fees, Fines, and Citations
fiscalCities
year).

San Francisco
CitiesNew
York
San
Francisco
ChicagoNew York
Chicago

USD 439.7M

Total Parking
USDRelated
1.1B+ Revenue
USD299.7M
439.7M
USD
USD 1.1B+
USD 299.7M

3.1. COVID-19 Impact on On-Street Parking

USD 85.5M

USD 354.2M

USD
USD85.5M
134M
USD 300M
USD 134M

USD
354.2M
USD
25.1M
USD 785M
USD 25.1M

ParkingTaxFees, Fines,
and785M
Citations
USD 300M
USD

To demonstrate
theOn-Street
extent ofthe on-street parking affected by the pandemic, two
3.1. COVID-19
Impact on
Parkingstudies are reviewed in this section: the smart parking system developers Smarking [17],To demonstrate the extent of the on-street parking affected by the pandemic, two studwhich
was carried out by a random sampling of 511 zones over the US (averaging 136
ies are reviewed in this section: the smart parking system developers Smarking [17],parking
spaces),and
IEM
whichsampling
was studied
on 650installed
PrestoSense
parking
which wascarriedout
by [32],a randomof 511zones
over theUS (averaging
sensorsindifferentpartsofGeneva,Switzerland.
136 parking spaces), and IEM [32], which was studied on 650 installed PrestoSense parking
TheinSmarking[17] survey
showeda decrease in parking on the street at 80–90% sincesensors
different partsof Geneva,
Switzerland.
the US
emergency
statement
oninFriday,
2020.
Thenational
Smarking[17] survey
showed aissued
decreaseparking13onMarch
the street
at Before
80–90%andsinceafterthetheannual dynamics
parking
activities
havechangeda lot: a
the COVID-19
US nationaloutbreak,emergency
statement
issued on of
Friday,13 March
2020.
Beforeand after
the COVID-19 outbreak,theannualdynamics
of parking
activities
havechangeda lot:
year-over-yearincrease in10%,
to 80–90%
decrease,comparedto thesame
periodlastayear
year-over-yearin 10%,
to 80–90%
comparedto theparking
same period
lastsurveyed
year
(See
Figure 1). increase
Similarly,
the peakdaily decrease,occupancyof on-street
spaces(See Figure1). Similarly,
peakoccupancyof on-street
parkingacrossthe country
showsthethat
thedailypattern
before andafter Friday,
13 spaces
March surveyed
has changed
across the country shows that the pattern before and after Friday, 13 March has changed
significantly.
significantly.

Figure 1. On-street parking year-over-year (YOY) change (from a random sample of 511 zones/blocks

Figure 1. On-street parking year-over-year (YOY) change (from a random sample of 511
across the US).
zones/blocks across the US).

Conversely, IEM [32] studied data from 650 PrestoSense parking monitoring sensors
installed
in different
regionsof Geneva,
to illustrate parking
how the parking
patterns
Conversely,
IEM[32] studied
dataSwitzerland,
from 650 PrestoSense
monitoring
sensorsonthestreetshavechanged
in
areas
where
semi-confinement
wasannounced.
This
studypatinstalled in different regions of Geneva, Switzerland, to illustrate how the parking
focused(in termsof occupancy
and turnover) fluctuation
before andThis
terns
on on
theparking
streets demand
have changed
in areas
where semi-confinement
was announced.
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5 of 16

study
focused on parking demand (in terms of occupancy and turnover) fluctuation beInt. J. Environ. Res. Public Health 2022,
19, 7665
fore and after the emergency declaration date of 16 March 2020. The state of emergency
was declared between 16 March and 11 May 2020, in which the semi-confinement period
spanned16 March
to April,andtheMarch
progressive
de-confinement
periodafter the fromemergency
declaration
date
of 16
2020. The
state of emergency
wasspanned
declared
from
April
to
11
May
2020.
between 16 March and 11 May 2020, in which the semi-confinement period spanned from
Duringtothesemi-confinement
period,de-confinement
parking restrictions
such
as vehicle
16 March
April,and the progressive
periodspannedfromparking
April tocontrolweresuspendedinGeneva.
To
analyzethepolicyshift
impact,thePrestoSense
11 May 2020.
vehicleDuring
detection
were installed
in on-street
bays suchof residential
areas,
ofthe sensors
semi-confinement
period,parkingparking
restrictions
as vehicle
parking
fices
andculturalareas
todetectvehicleoccupancy
andtheparking
durationofaparkedcontrol were suspended in Geneva. To analyze the policy shift impact, the PrestoSense
vehicle.
vehicle detection sensors were installed in on-street parking bays of residential areas, offices
on-streetcomparisonandassessment
wasperformed
betweenvehicle.
resiandTheculturalareasparking
to detectdemandvehicle occupancy
the parking
durationof a parked
dentialTheareasversusparking
offices andculturalareas. In assessment
residential complexes,
beforethe semion-street
demandcomparison
was performed
betweenresiconfinement
period,parkingin residential
areas showed
an averageoccupancy
dential areas
versustheoffices
andusage
culturalareas. In residential
complexes,
beforethe semilevelof 82% (seeFigure2a)parking
with anusage
averagecar turnoverrateshowedof 7.2 perhere,occupancy
the turnconfinement
period,thein residential
areas
anday;
average
over
rate
is
definedastheaveragenumberofvehicles
using
the
same
parkingspacealevel of 82% (see Figure 2a) with an average car turnover rate of 7.2 per day; here,atthe
certain
time
gap.
In
the
same
area
duringthesemi-confinement
period,anincreaseintheturnover rate is defined as the average number of vehicles using the same parking space at
occupancy
levelgap.
to 93.4%
Figureand thea decreasein car turnoverrates
3.2 carsinpera certain time
In the(seesamearea 2b)
duringsemi-confinement
period,anto
increase
theday
was clearlyobserved.
occupancylevel
to 93.4% (see Figure 2b) and a decrease in car turnover rates to 3.2 cars per
day was clearly observed.

(a)

(b)
Figureselected residential
residentialarea
area(Rue(RuedesdesPâquis,
Pâquis,Geneve):
Geneve):beforeFigure2.2.Average
Averageoccupancy
occupancy ofof thethe selected(a)(a)
beforethethe confinement period (between 5 January and 5 February 2020), and (b) during the confinement
confinement period (between 5 January and 5 February 2020), and (b) during the confinement period
period (between 1 and 30 April 2020).
(between 1 and 30 April 2020).

Meanwhile,the semi-confinement
semi-confinementperiod,
period,ananaverage
averMeanwhile,ininoffice
officeandandcultural
cultural sites, before theageoccupancy
levelof 39.3%
Figurecar turnover
5.7 cars
per parking
occupancy
levelof 39.3%(see(seeFigure3a), 3a),andandcar turnoverrate rateof 5.7ofcars
per parking
space
space
per wasday observed,
was observed,
whereasthe sameduringthe semi-confinementpe- aper day
whereasin theinsame
area area
duringthe semi-confinement
period,substantial
drop inoccupancy
levels to
26.6%
Figureand3b)
a decreasein car turnoverriod,
a substantial
drop
in occupancy
levelsto (see
26.6%(see 3b)
Figureand a decreasein carrates to 1.7
perspace perday per
wasdaymarked.
turnoverrates
toparking1.7 per parking
space
was marked.
3.2. COVID-19 Impacts on Off-Street Parking
A study [17] under this topic was reviewed to show the impact of COVID-19 on
off-street parking garages. During the investigation, a collection of 935 off-street parking
locales, consisting of both private and city-owned parking garages was studied. The results141

Int. J. Environ. Res. Public Health 2022, 19, 7665

Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW

6 of 16

show a comparative drift from on-street parking prior to the COVID-19 emergency: 10%
YOY increment (see Figure 4). Then, after 13 March 2020, the parking demand dropped
dramatically by about 90%.

(a)

(b)
Int. J. Environ. Res. Public Health 2022,
19, 3.
x FOR
PEER REVIEW
7 of 16
FigureAverageoccupancy of the selected Office and Cultural site (Bd. Du théâtre, Geneve):(a)Figure 3. Average occupancy of the selected Office and Cultural site (Bd. Du théâtre, Geneve):
before the confinement period (between 5 January and 5 February 2020), and (b) during the confine(a) before the confinement period (between 5 January and 5 February 2020), and (b) during thement period (between 1 and 30 April 2020).
confinement period (between 1 and 30 April 2020).

3.2. COVID-19 Impacts on Off-Street Parking
A study [17] under this topic was reviewed to show the impact of COVID-19 on offstreet parking garages. During the investigation, a collection of 935 off-street parking locales, consisting of both private and city-owned parking garages was studied. The results
show a comparative drift from on-street parking prior to the COVID-19 emergency: 10%
YOY increment (see Figure 4). Then, after 13 March 2020, the parking demand dropped
dramatically by about 90%.

Figure 4. Off-street parking activities YOY change (from a random sample of 511 zones/blocks acrosstheFigureUSA). 4. Off-street parking activities YOY change (from a random sample of 511 zones/blocks
across the USA).

4. Case Study: Melbourne City, Australia
To investigate the impact of COVID-19 on parking demand distribution on different
142
types of urban areas and streets,
this study used the parking data from Melbourne CBDparking bays, Australia, visualized to illustrate how the parking demand on different central districts was shifted by the pandemic. The in-ground parking sensors recorded vehicle

Int. J. Environ. Res. Public Health 2022, 19, 7665

4. Case Study: Melbourne City, Australia
To investigate the impact of COVID-19 on parking demand distribution on different
types of urban areas and streets, this study used the parking data from Melbourne CBDparking bays, Australia, visualized to illustrate how the parking demand on different
central districts was shifted by the pandemic. The in-ground parking sensors recorded
vehicle arrival and departure times (from 1 January to 1 May 2020), which were released
upon customer request to grasp the strike of COVID-19 on city movement. On-street
car parking sensor data for 2019 [33] and from January–May, 2020 [34] were obtained tovisualize the impact variation of COVID-19 on parked vehicle volume and average parking
duration. Visualizing these parking sensors data will help to illustrate how the parking
demand in terms of parking volume and average parking duration on different central
districts is shifted by the pandemic during the state of emergency. The state of emergency
was declared by the Minister for Health on 16 March 2020, under section 198 (1) of the
Public Health and Wellbeing Act 2008 (Act) throughout the state of Victoria due to theserious threat to public health in Victoria due to the novel coronavirus 2019 [35].
On this case study, Melbourne city areas and streets were selected and analyzed toassess how the city areas and streets are impacted negatively with respect to parking
demand after the first stage of the state of emergency dated from 16 March 2020. The
assessment area selection criteria included the high shift in parking volume after the stateof emergency versus the rest of the areas listed on the dataset, and the authors think
the picked areas for analysis, named as Docklands, Queensbery, South banks, Titles, andPrincess Theatre will illustrate the image for the high impact of COVID-19 on parking
demand distribution. Further, at street-level impact analysis, streets of Princess Theatre
were evaluated with respect to parking volume and parking duration indicators. The
streets of Princess Theatre were chosen for analysis since this area shows a higher shift inparking demand out of the five selected areas.
The impact assessment and comparison were conducted between two periods, 16
March–26 May 2020 versus 16 March–26 May 2019. The parking demand indicators areparking volume, in terms of vehicles present, and average parking duration. The sensors
read whether there is a parked vehicle or not, in which the data are given by (false, true)or (0, 1). The sum of the values “true” or “1” gives the total number of vehicles present or
parked at a certain location during a certain period. The average parking duration was
obtained by dividing the parking load (vehicle-minutes) by the total number of vehicles
present during the entire duration of the assessment (Equation (1)). It can also be calculated
as the sum of parking duration of all vehicles divided by the total number of vehicles
parked during the survey period.
Average Parking duration (minutes) =Parking load (veh. minutes)
Parking volume (veh.)(1)

The authors believe that the study area (Figure 5) is planned to present the interior
CBD of Melbourne. The black dots in Figure 5b show the on-street parking bays in theselected experimental areas of Melbourne streets [36].
4.1. Areal Based IMPACT Analysis
Parking volume comparison: After the state of emergency, the vehicles present
in on-street parking bays showed a significant decrease. For almost two months from
16 March to 26 May 2020, Docklands, Queensbery, Southbanks, Titles, and Princess Theatre
areas showed a decrease in percentage change of vehicles present of 29.2%, 36.3%, 37.7%,
23.7%, and 40.9%, respectively, compared to the same period of the previous year from
16 March to 26 May 2019. Princess Theatre appeared to have the highest fall in parking
volume percentage change from the picked experimental areas. As presented in Table 3
and Figure 6, the on-street parking spot demand decreased relatively within these periods,
mainly due to the state of emergency restrictions, including the advice to stay home.

143

parked during the survey period.
Average Parking duration (minutes) =Parking load (veh. minutes)
Parking volume (veh. )(1)

The authors believe that the study area (Figure 5) is planned to present the interior
Int. J. Environ. Res. Public Health 2022, 19, 7665

CBD of Melbourne. The black dots in Figure 5b show the on-street parking bays in theselected experimental areas of Melbourne streets [36].

(a)

(b)

FigureFigure 5.
5. Study
Study area:
area: (a)(a) Melbourne
Melbourne CBD,
CBD, VIC,
VIC, Australia;
Australia; (b)(b) on-street
on-street parking
parking bays.
bays.
Table
3. Arealevel
comparisonsof parking volume in terms of number of vehicles present between
4.1.
Areal
Based
IMPACT
Analysistwo periods(16volume
March tocomparison:
26 May 2019 versus
May 2020). the vehicles present in
Parking
After 16
theMarchstatetoof26emergency,

on-street parking bays showedNumber
a significant
decrease. For almost two months from 16
of Vehicles Present
Percentage
March toArea
26 May 2020, Docklands, Queensbery, Southbanks, Titles, andPrincessChange
Theatre16 March–26 May 201916 March–26 May 2020
areas showed a decrease in percentage change of vehicles present of 29.2%, 36.3%, 37.7%,
482,426
29.2%from 16
23.7%,Docklands
and 40.9%, respectively,
compared to the same341,538
period of the previous−yearMarch
to 26 May 2019. Princess
Theatre appeared to havethe highest fall in−parking
Queensbery
330,106
210,257
36.3% volume percentagechangefromthepickedexperimental
areas.
As
presented
inTable 3 andSouthbanks
243,897
151,845−37.7%
Figure 6, the on-street parking spot demand decreased relatively within these periods,
Titles184,969
141,161−23.7%
mainly due to the state of emergency restrictions, including the advice to stay home.
Princess Theatre

178,102105,273−40.9%

Table 3. Area level comparisons of parking volume in terms of number of vehicles present between
two periods(16parking
March toduration
26 May 2019
versus 16 March
26 state
May 2020).
Averagecomparison:
Aftertotheof emergency from 16 March

to 26 May 2020, the average parking duration of on-street parking bays showed a significant
Number of Vehicles Present
increase.
As shown in Table 4 and Figure 7, Docklands, Queensbery,Percentage
Southbanks,
Titles,Area
Change16 March–26 May 2019 16 March–26 May 2020and Princess Theatre areas showed an increase in percentage change of average parking
Docklands
482,42638.2% and 89.3%, 341,538−29.2%
durationof 45.3%, 17.9%, 64.5%,
respectively, comparedto the same
Queensbery
330,106
210,257−36.3%
period of the previous year from 16 March to 26 May 2019. Princess Theatre appeared toSouthbanks
151,845−37.7%
havethe highest increase in243,897
average parking duration
percentage change fromthe picked
experimental
to Tables 3 and 4, Princess
Theatre is the area thatexperienced
Titles areas. Referring
184,969
141,161−23.7%
thehighestdrop in vehicles178,102
present, with −40.9%, andthe highest increase −40.9%
in percentage
Princess
Theatre105,273change of average parking duration, with +89.3%. This is mainly due to the socio-economic
nature of the Princess Theatre area, which is famous for being an entertainment andgathering site [37]. The reason for the high shift in parking demand of this kind of areas
was the state of emergency and restriction on gathering activities. Technically, the averageparking duration (in min.) is defined as the ratio of total vehicle hours to the number ofvehicles parked, i.e., parking load in Veh. min over parking presence (in Veh.). Therefore,
mathematically, as a vehicle parking presence decreases with a wider gap than the parking
load, the average parking duration may increase in a significant range.

144

Int. J. Environ. Res. Public Health 2022, 19, 7665

Figure 6. Bar graph representation of Princess Theatre’s area parking volume in terms of vehicles
present between two periods: (a) 16 March to 26 May 2019 versus (b) 16 March to 26 May 2020.
Table 4. Area level comparisons of the average parking duration change in minutes between periods(16 March to 26 May 2019 versus 16 March to 26 May 2020).
Area

Average Parking Duration (min)Percentage Change

16 March–26 May 201916 March–26 May 2020

Docklands

48.65

70.71

45.3%

Queensbery

90.15

106.2917.9%Southbanks

76.51

125.88

64.5%

Titles

39.51

54.61

38.2%

Princess Theatre

36.18

68.5

89.3%

145

Int. J. Environ. Res. Public Health 2022, 19, 7665

Figure 7. Bar graph representation, referring to the Princess Theatre area average parking duration
between two periods: (a) 16 March to 26 May 2019 versus (b) 16 March to 26 May 2020.

4.2. Street Level Impact Analysis
For this part of the impact assessment, the streets circling Princess Theatre area (Lonsdale Street, Exhibition Street, Spring Street, and Little Bourke Street) were selected forparking demand visualization.
Parking volume comparison: After the state of emergency, the vehicles present
in on-street parking bays showed a significant decrease. For almost two months from
16 March to 26 May 2020, Lonsdale Street, Exhibition Street, Spring Street, and Little Bourke
Street parking bays showed a decrease in percentage change of vehicles present of 38.7%,
56.4%, 12.6%, and 35.1%, respectively, compared to the same period of the previous year
from 16 March to 26 May 2019.
Exhibition Street appeared to have the highest fall in parking volume percentagechange from the streets around Princess Theatre. As presented in Table 5, Exhibition Street,146

Int. J. Environ. Res. Public Health 2022, 19, 7665

with a 56.4% drop in parking volume, lost the proportion of vehicles present within thearea, from 27% to 20%. This is mainly because Exhibition Street is famous for being home to
many heritage buildings, such as the Victorian Heritage Register [38], which are classified
as one of Melbourne’s visiting sites, and which was highly impacted by the COVID-19 stateof emergency measures.
Table 5. Street level comparisons of the parking volume in terms of the change in number of vehicles
present between two periods (16 March to 26 May 2019 versus 16 March to 26 May 2020).
Number of Vehicles Present

Princess Theatre
Streets

16 March–26 May 201916 March–26 May 2020

Lonsdale street

112,863

69,166

Exhibition street

47,29920,630Spring street

8040

9054

Little Bourke street

9900

6423

Percentage Change−38.7%−56.4%−12.6%−3.51%

Average parking duration comparison: After the state of emergency from 16 March
to 26 May 2020, the average parking duration of the selected streets’ on-street parking bays
showed a significant increase. As shown in Table 6, Lonsdale, Exhibition, Spring, and Little
Bourke streets showed an increase in percentage change of average parking duration of
84%, 103.6%, 153.4%, and 58.3%, respectively, compared to the same period of the previous
year from 16 March to 26 May 2019.
Table 6. Street level comparisons of the average parking duration change, in minutes, between two
periods (16 March to 26 May 2019 versus 16 March to 26 May 2020).
Average Parking Duration (min)Princess Theatre
Streets

16 March–26 May 201916 March–26 May 2020

Lonsdale Street

35.42

65.18

84%

Exhibition Street

37.19

75.71

103.6%

Spring Street

29.17

73.93

153.4%

Little Bourke Street

45.45

71.96

58.3%

Percentage Change

Spring Street appeared to have the highest increment in average parking duration
percentage change from the streets around Princess Theatre, and Little Bourke Street witha 58.3% increment in average parking duration lost the proportion of average parking
duration in the area, from 31% to 25%. This is mainly because Little Bourke Street is famous
for its department stores and many boutiques [39], in which most of them were closed
in relation to the COVID-19 state of emergency measures, having a direct impact on theinterest of on-street parking from visitors.
5. Conclusions
As presented in the previous section, the parking volume and average parking duration were highly impacted due to the COVID-19 state of emergency in Melbourne city.
Areal/zonal-based impact analysis, from 16 March to 26 May 2020, in Docklands, Queensbery, Southbanks, Titles, and Princess Theatre areas showed a decrease in percentagechange of vehicles present of 29.2%, 36.3%, 37.7%, 23.7%, and 40.9%, respectively, comparedto the same period of the previous year from 16 March to 26 May 2019. Then, considering
the average parking duration as a parameter, Docklands, Queensbery, Southbanks, Titles,
and Princess Theatre areas showed an increase in percentage change of average parking
duration of 45.3%, 17.9%, 64.5%, 38.2%, and 89.3%, respectively, within same period.
Furthermore, the street-level impact analysis of the Princess Theatre area, Lonsdale
Street, Exhibition Street, Spring Street, and Little Bourke Street parking bays showed147

Int. J. Environ. Res. Public Health 2022, 19, 7665

a decrease in percentage change of vehicles present of 38.7%, 56.4%, 12.6%, and 35.1%,
respectively. With the same parametrical comparisons as the zonal analysis, Lonsdale,
Exhibition, Spring, and Little Bourke streets showed an increase in percentage change of
average parking duration of 84%, 103.6%, 153.4%, and 58.3%, respectively. The variation in
the impact of the pandemic might come from a different basis. The social, infrastructural,and institutional nature of the urban areas and streets might have been impacted by the
pandemic regarding parking demand sensitivity.
Quantifying the impact throughout the urban area might be useful for implementing
different types of parking policies and interventions and to further help maximizing theperformance of COVID-19 urban mobility measures. The massive declines on intercity
transportation brought the direct drop in parking demand. Cities are scrambling to recover
not only the lost parking revenues, but also the significant reduction in fees and fines. As
parking is one of the significant incomes of most municipalities, the decline in income has
resulted in tremendous challenges to support their health care system.
Analyzing the mitigation measures and preparing for the coming “new normal” [40,41]
may assist in understanding the opportunities behind the risks regarding parking industries.
When dealing with policies and measures during the pandemic, it is critical to understandthe interrelationship between jobs and food access, housing, public health, and mobility.
The pandemic is an opportunity for private and public parking transit operators to advancemobility and parking needs and to strengthen economic resilience, particularly for the mostvulnerable populations with equity and accessibility concepts.
The parking industry response implementation from COVID-19 might include threebasic creative approaches of adaptiveness, pivoting needs, and replacing the lost revenue
approach [30]. Parking lots have been adapted to be used as COVID-19 screening andas real-time tracking platforms that support the city’s requirements with all the parking
KPIs and the most up-to-date information accessible for the governmental task forces. The
common topics on pivoting demands are that the industry should repurpose, be agile, andbe adaptable during this time. A few ways for pivoting the needs in times of instability topreserve the quality of the parking and mobility programs can be framed as operationaland financial sections. In operational prioritizing: moving all customer support to a virtualenvironment, transitioning enforcement staff to assist with healthcare operations, andrenegotiating travel contracts to lower rates and move transport drivers to on-demandbenefits. In financial prioritizing: plan and finance the year into smaller fiscal budgets rather
than completing the full budget process (due to the flexible nature of the pandemic impact)
and arrange to prioritize socio-economic zones based on needs targeting equity, accessibility,and vulnerability. In addition, a fast recovery plan to support the potential revenue loss has
to be developed and implemented as early as possible. Financial models are required tounderstand the revenue impacts on the ground, with varying possibilities on the expectedsaturation curve and with further extension for the coming years. Understanding the
pandemic projection, the estimated saturation line, and the nation’s health institutions’capacity helps to design an acceptable and successful financial recovery plan.
Planners and specialists witness new opportunities to sustain their projects, whichmainly push for smart working and reduce unnecessary labor intensity. The COVID-19
pandemic may require to examine the operations holistically and to be smarter about
our future programs. Some potential positive outcomes are becoming more virtual andcustomer friendly, expanding and exploring new technologies, and aligning parking pricing models with the true cost of our parking and mobility programs. Following this, the
COVID-19 impact on parking policy change in relation with the demand shift of commercial ridesharing vehicles can be modeled by discrete decision models, which introduce a
behavioral framework regarding a commercial vehicle parking choice [42]. With additional
considerations regarding the expected future COVID-19 factors, the model may be used for
capturing pick and delivery vehicles regarding parking policy sensitivity.
Future CITY-200 s studies of post-COVID-19 parking and urban space trends are divided into three categories: Smart Parking and Curb Management, Artificial Intelligence148

Int. J. Environ. Res. Public Health 2022, 19, 7665

and Data Driven Technology, and Autonomous Vehicles (AVs). Smart parking management and enforcement ethos can cure almost all parking problems during the emergency
period of the pandemic. Real-time IoT parking solutions and management are expected
to be used in future applications to optimize parking operation and management [43,44].
COVID-19 has also expedited the improvement in artificial intelligence in numerous diverse
aspects representing a lessening of human contact or nearness, which furthermore may
be regarded as a catalyst for the advancement of open source information and data. For
instance, recent publications such as [45–47] have mined and structured big data to forecast
and predict this and other illnesses. The rise of AVs may be placed in public transportation
services, which may serve as an advanced arrangement to defend the transit workforce.
Author Contributions: Conceptualization, B.G.M. and D.S.; methodology, B.G.M.; formal analysis,
B.G.M.; resources, D.S.; data curation, B.G.M.; writing—original draft preparation, B.G.M.; writing—review and editing, D.S.; visualization, B.G.M.; supervision, D.S. and B.P.; project administration,
B.P.; funding acquisition, D.S. and B.P. All authors have read and agreed to the published versionof the manuscript.
Funding: This research was funded in part by the National Nature Science Foundation of China(71971138, 52172319), the National Social Science Foundation of China (21AZD047) and the Social
Science Foundation of Shanghai (2020BZZ001, 2021BGL012), and the APC was funded by (21AZD047).
Any opinions, findings and conclusions or recommendations expressed in this paper are those of theauthors and do not necessarily reflect the views of the sponsors.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the designof the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, orin the decision to publish the results.

Abbreviations
AI
APP
AV
CBD
COVID-19
EIT
IATA
ICAO
IEM
IoT
MOBIS
TUMI
WHO
YOY

artificial intelligencesmart mobile/computer/Android/TV applications
autonomous vehicles
Central Business District
Coronavirus disease 2019
European Institute of Innovation and Technology
International Air Transport Association
International Civil Aviation Organization
Ingenierie Electronique et MonetiqueInternet of Things
Mobility Behaviour In Switzerland
Transformative Urban Mobility Initiative
World Health Organization
year-over-year

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International Journal ofEnvironmental Researchand Public Health
ArticlePsychological and Physical Changes Caused by COVID-19
Pandemic in Elementary and Junior High School Teachers:
A Cross-Sectional Study
Nobuyuki Wakui 1, * , Nanae Noguchi 1 , Kotoha Ichikawa 1 , Chikako Togawa 1 , Raini Matsuoka 1 ,Yukiko Yoshizawa 1 , Shunsuke Shirozu 1 , Kenichi Suzuki 1 , Mizue Ozawa 2 , Takahiro Yanagiya 2
and Mayumi Kikuchi 2
1

2*

Citation: Wakui, N.; Noguchi, N.;
Ichikawa, K.; Togawa, C.; Matsuoka,
R.; Yoshizawa, Y.; Shirozu, S.; Suzuki,
K.; Ozawa, M.; Yanagiya, T.; et al.
Psychological and Physical Changes
Caused by COVID-19 Pandemic in
Elementary and Junior High School
Teachers: A Cross-Sectional Study.
Int. J. Environ. Res. Public Health 2022,
19, 7568. https://doi.org/10.3390/
ijerph19137568
Academic Editors: Sachiko Kodera
and Essam A. Rashed
Received: 23 May 2022

Division of Applied Pharmaceutical Education and Research, Faculty of Pharmaceutical Sciences,
Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan; s181180@hoshi.ac.jp (N.N.);
s181012@hoshi.ac.jp (K.I.); s181156@hoshi.ac.jp (C.T.); s181219@hoshi.ac.jp (R.M.);y-yoshizawa@satsuki-ph.com (Y.Y.); s-shirozu@hoshi.ac.jp (S.S.); kenichi-suzuki@hoshi.ac.jp (K.S.)
Shinagawa Pharmaceutical Association, 2-4-2 Nakanobu, Shinagawa-ku, Tokyo 142-0053, Japan;
mitziozza1965@gmail.com (M.O.); yanagiya@tnb.co.jp (T.Y.); tomato_5mk@yahoo.co.jp (M.K.)
Correspondence: n-wakui@hoshi.ac.jp; Fax: +81-5498-5760

Abstract: This study aimed to determine psychological and physical differences in elementary andjunior high school teachers during COVID-19. This questionnaire-based cross-sectional study was
conducted among 427 teachers in Tokyo, Japan (between 15 and 30 October 2020). The questionnaire
explored school type (elementary and middle schools), sex, age, and COVID-19 changes (psychological changes, physical changes, impact on work, and infection control issues perceived to be stressed).
Post hoc tests for I cannot concentrate on work at all, found a significant difference for no change–improved
and male teacher in elementary school female teacher in junior high school (p = 0.03). Regarding stresssituation due to implementation of COVID-19 infection control, there were significant differences fordisinfection work by teachers between male teachers in elementary school female teachers in junior high school(p = 0.04) and female teachers in elementary school female teachers in junior high school (p = 0.03). COVID-19
produced differences in psychological and physical changes between male and female teachers inelementary and junior high schools. Some experienced psychological and physical stress, whereas
others showed improvement. Given that teachers’ mental health also affects students’ educational
quality, it is important to understand and improve teachers’ psychological and physical circumstancesand stress.
Keywords: COVID-19; elementary teacher; junior high school teacher; psychological change; physicalchanges

Accepted: 20 June 2022
Published: 21 June 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
A new type of coronavirus occurred in Wuhan City, Hubei Province, China, in December 2019, [1] which was later named SARS-CoV-2 [2,3]. SARS-CoV-2 rapidly spreadworldwide, and the World Health Organization (WHO) declared a pandemic in March
2020 due to the spread of COVID-19 caused by SARS-CoV-2 [4]. Subsequent lockdown
restrictions occurred throughout the world, which introduced significant changes andconfusion to daily life [5,6]. Currently, the total number of infected people worldwide ismore than 560 million, and the number of deaths is more than 6 million [7].
In Japan, an emergency declaration was issued for the first time in April 2020 [8], andthe government requested that restaurants and schools should close and working fromhome was encouraged [9,10]. A new way of life was proposed to reduce the spread of
infection, and a comprehensive behavioral change was required [11]. Specifically, basic
infection control measures, such as hand washing and physical distance [12,13], as well as

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Int. J. Environ. Res. Public Health 2022, 19, 7568

routine infection control measures, such as thorough avoidance of three Cs (closed spaces,
crowded places, close-contact settings) [14] and a reduction in human contact by 80%, were
implemented [15]. Such measures were also introduced in many parts of the world [16,17],
which is a stressor for many people [18].
A new method of education in schools was introduced to ensure the rights of children
to receive an education [19]. Specifically, in addition to basic infection control measures,
restrictions included eating silently and the promotion of online classes [19]. However,it has been recognized that these restrictions impose a physical and mental burden onteachers managing the learning environment of students [20], which affects the mental
health of teachers [21].
In previous studies, education has been considered as one of the highly stressful
occupations [22]; moreover, occupational stress affects mental and physical health, andincreases the risk of developing conditions, such as depression [23]. Indeed, teachers havea higher risk of developing depression compared with other occupations [24], and thedevelopment of burnout syndrome due to accumulated stress has also been reported [25].
During the COVID-19 pandemic, teachers may be more highly stressed than previously [26]as they are required to take various measures to prevent the spread of infection in addition
to their own personal safety [27].
In a survey conducted in Greece in 2020, changes were found in teachers’ mind andbody related to COVID-19, and results showed that many teachers felt anxious due to the
COVID-19 pandemic [28]. Compared with before the pandemic, mental changes wereassociated with decreased QOL [29], increased stress [30], and increased burnout [30,31].
There are some reports that female faculty are more likely to develop fear, depression,stress disorders and depression in comparison of sex [32]. In terms of physical changes,
worsening of diet, sleep, and alcohol consumption have also been reported [6,17,18].
However, these reports have not analyzed the differences between elementary school
teachers and junior high school teachers, and the stress situation of teachers by school type
has not been clarified. Teachers are required to provide support for elementary school
pupils and junior high school pupils in accordance with the students’ developmental stage;
hence, the nature of their work and the way they interact with pupils should differ [33].
This has led to the possibility that the stress status of teachers involved in teaching during
the COVID-19 pandemic can vary widely between elementary and junior high schools.
Therefore, it is of crucial public health importance to clarify the stress situation of teachers
by school type to improve teachers’ mental health [34].
Therefore, this study conducted a cross-sectional survey of elementary and juniorhigh school teachers to clarify the physical and psychological differences during the
COVID-19 pandemic.
2. Materials and Methods
2.1. Study Design and ParticipantsThis was a cross-sectional study conducted among elementary and junior high school
teachers in the Shinagawa Ward of Tokyo, Japan. The questionnaire (see Supplementary Materials) was designed to be answered in about 10 min. The participants included elementaryand junior high school teachers in Shinagawa Ward, Tokyo. Data were collected for over
16 days between 15 October 2020 and 30 October 2020. Questionnaires were distributed
to teachers of 18 elementary schools and 11 junior high schools selected randomly, andcollection was by mail. Teachers who participated in the study responded anonymously tothe questionnaire. Study participants provided informed written consent and participants
participated in the study voluntarily (Figure 1).

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Int.
Int. J.J. Environ.
Environ. Res.
Res. Public
Public Health
Health 2022,
2022, 19,
19, 7568
x FOR PEER REVIEW

3 of 13

Cross-sectional study
Elementary and junior high school teachers in the Shinagawa Ward of Tokyo, Japan
Schools were randomly selected (18 elementary schools, 11 junior highschools)
Informed consent was obtained and the questionnaire was distributed
Data were collected between October 15, 2020, and October 30, 2020
Participants responded anonymously to the questionnaire

Data analysis (n = 427)

Figure1.
1. Flow-chart
Flow-chart ofof thethe cross-sectionalcross-sectionalstudy.
study.
Figure2.2.
2.2. Survey
Survey Items
Items
The
questionnaire
The questionnaireconsisted
consistedof
offivefivesections,sections,one
oneforfor demographic
demographicdata
data(age,(age,sex,
sex,type
typeofof school),school), andand fourfour sections
sections asked
asked about
about thethe impact
impact ofof COVID-19
COVID-19 (psychological(psychological changes,
changes,
physical
physicalchanges,
changes,impact
impactononwork,work,and
andstressedstressedrelating
relatingto
toinfection
infectioncontrol
controlitems).
items).
Ofthefoursectionsofthequestionnaire,thepsychologicalchange,Of the four sections of the questionnaire, the psychological change, physicalphysical change,change,andand impact
impact on
on work
work sections
sections were
were preparedprepared using
using items
items related
related toto thisthis study
study fromfrom thethe
DisasterDisaster Reliever
Reliever Mental
Mental Health
Health Manual
Manual preparedprepared inin JapanJapan [35].
[35]. The
The “infection“infection countercountermeasure
measure mattermatter thatthat causes
causes stress”
stress”question
questionitem
itemwaswasmademadetotobe
be thethe mattermatter forfor whichwhich thetheimplementation is required in the infection countermeasure guideline for school educaimplementation is required in the infection countermeasure guideline for school education [36]. All the questions in the four sections investigating the impact of COVID-19 weretion [36]. All the questions in the four sections investigating the impact of COVID-19 wereclosed questions.
closed questions.
In terms of psychological change, 13 items were investigated, including, feeling high,In terms of psychological change, 13 items were investigated, including, feeling high,irritation, anger, indignation, regret, anxiety, chagrin, impotent feelings, depression, no
irritation, anger, indignation, regret, anxiety, chagrin, impotent feelings, depression, nosense of reality, no sense of time, emotions are paralyzed, unable to concentrate on work atsense of reality, no sense of time, emotions are paralyzed, unable to concentrate on work
all, and no longer wanting to engage with others.
at all, and no longer wanting to engage with others.
Six physical changes were investigated: insomnia/nightmares, palpitations, standing
Six physical changes were investigated: insomnia/nightmares, palpitations, standing
dizziness, sweating, dyspnea, and digestive symptoms. The following four items weredizziness, sweating, dyspnea, and digestive symptoms. The following four items were
investigated regarding the effect on work: excessively immersed in the work, reduction in
investigated regarding the effect on work: excessively immersed in the work, reduction inthinking ability, lower concentration ability, and lower work efficiency. These items werethinking ability,
lower
concentration
ability,andlowersame
workasefficiency.
These
items were
measuredat threelevels:more improved
than
before,before, moredeteriorated
measuredatthreelevels:more
improved
thanbefore,same
asbefore,more
deteriorated
than before.
thanAsbefore.
for the infection countermeasures that felt stress, nine items were surveyed, namely
As
for theainfection
countermeasures
feltstatus,
stress,disinfectionnine itemswork
werebysurveyed,always wearingmask, bodytemperature andthat
healthteachers,
namelyalways wearingmask, bodytemperature
andhealthstatus,disinfectionworksocialdistancing,
activity arestriction
duringclass, using
face
shields,
prohibition
or restricby
teachers,socialdistancing,
activity
restriction
duringclass,
using
face
shields,
prohibition of play and conversation, eating without talking/no refills, and not allowing childrention
or restrictionof wereplay measured
and conversation,eating without
talking/no
refills,andnot alto
serve.
These items
at two levels—feelingstressed
and not
feeling
stressed.
lowingchildrentoserve.
These
items
were
measuredattwolevels—feelingstressed
Subsequently, it was investigated whether there were significant differences between andthenot feelingstressed.
Subsequently,
it waswhether
there were significant diftypes
and sexof schools
according totheseinvestigatedquestionnaire
responses.
ferences between the types and sex of schools according to these questionnaire responses.
2.3. Sample Size
2.3. Sample
Size sample size calculator, Raosoft® was used to set the sample size. Sample
An online
sizes An
were
calculatedusing
response
rate, ®95%
5% error
onlinesamplesize 50%
calculator,
Raosoft
wasconfidence
used to setinterval,
the sample
size. margin,
Sampleandpopulationof teachers
in elementaryand95%
middleschools inTokyo assizesthewere
calculatedusing 50%
response rate,
confidence
interval,
5%approximately
error margin,
49,000(elementary
school, nin=elementary
33,912, juniorschool
Rao insoft,
n = 15,340).
As
and the[37]populationof teachersandhighmiddleschoolsTokyo
as approxiamately
result,49,000
the sample
size recommended
382. junior
Therefore,
numberof participants[37] (elementary
school, n was= 33,912,high theschoolRao soft,n = 15,340).
surveyedin thethissample
study waswhich included
an adequate
samplesize. of participants
As a result,
size427,
recommended
was 382.
Therefore,
the number
surveyed in this study was 427, which included an adequate sample size.
155

Int. J. Environ. Res. Public Health 2022, 19, 7568

2.4. Statistical Analysis
Free statistical software R version 4.0.5 (R Core Team, R Foundation for StatisticalComputing, Vienna, Austria) was used for statistical analysis. Pairwise elimination wasused to exclude missing data from the analysis if any of the two variables had missing
data, to use as much data as possible. Categorical data were tabulated with frequency
and percentage, and continuous data were tabulated with mean ± SD. Differences incharacteristics by school type and sex were assessed by Fisher’s exact test. In addition, foritems with significant differences, multiple comparison tests of proportions were performed
using the Hochberg methods. The level of significance in all tests was set at p < 0.05.
2.5. Ethical Considerations
The study was conducted with the approval of the Ethics Committee of the Hoshi
University (approval no. 2020-05). The study was conducted in accordance with the
Declaration of Helsinki Ethical Guidelines for Medical Research Involving Human Subjects.
All participants provided written informed consent.
3. Results
3.1. Demographic Features
There were 427 participants, of whom 305 (71.4%) were elementary school teachersand 122 (28.6%) were junior high school teachers (Table 1). A total of 418 participants
responded to sex—157 (37.6%) males and 261 (62.4%) females. A total of 403 participants
responded to age—92 (22.8%) in their 20 s, 139 (34.5%) in their 30 s, 79 (19.6%) in their 40 s,
64 (15.9%) in their 50 s, and 29 (7.2%) in their 60 s.
Table 1. Participants’ characteristics (n = 427).
Variables
School Type (n = 427)
Elementary school
Junior high schoolSex (n = 418)
Male
FemaleAge (n = 403)
20–29 years
30–39 years
30–49 years
50–59 years>60 years

305 (71.4 %)
122 (28.6 %)
157 (37.6 %)261 (62.4 %)
92 (22.8%)
139 (34.5%)
79 (19.6%)
64 (15.9%)
29 (7.2%)

3.2. Changes in Psychological Conditions before and after School Closure Due to COVID-19
Changes in psychological status before and after school closures are presented inTable 2. Fischer’s exact test revealed significant differences in the item “I cannot concentrate
on work at all” (p = 0.02). In addition, when multiple comparison tests were performed asa post hoc test for I cannot concentrate on work at all, a significant difference was foundin the part of no change–improved and male teacher in elementary school female teacher
in junior high school (p = 0.03). Regarding the significant difference found in the item “Icannot concentrate on work at all,” junior high school teachers (males, 9.2%; females, 11.1%)
had a higher response rate than elementary school teachers (males, 0%; females, 3.9%),
indicating that they had improved. Furthermore, female teachers had a higher response
rate to improvement than male teachers.

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Int. J. Environ. Res. Public Health 2022, 19, 7568

Table 2. Changes in psychological conditions before and after school closures due to COVID-19.
Elementary School
Male Teacher
Feeling high (n = 409)
Improved
11 (12.2%)
No Change
63 (70.0%)
Deteriorated
16 (17.8%)
Irritation (n = 410)
Improved
3 (3.4%)
No Change
66 (74.2%)
Deteriorated
20 (22.5%)
Anger (n = 408)
Improved
3 (3.4%)
No Change
73 (82.0%)
Deteriorated
13 (14.6%)
Indignation (n = 409)
Improved
3 (3.4%)
No Change
71 (79.8%)
Deteriorated
15 (16.9%)
Anxiety (n = 411)
Improved
9 (10.1%)
No Change
55 (61.8%)
Deteriorated
25 (28.1%)
Chagrin (n = 411)
Improved
7 (7.9%)No Change
69 (77.5%)
Deteriorated
13 (14.6%)
Impotent feeling (n = 409)
Improved
6 (6.7%)
No Change
67 (75.3%)
Deteriorated
16 (18.0%)
Depression (n = 412)
Improved
4 (4.4%)
No Change
63 (70.0%)
Deteriorated
23 (25.6%)
NSOR (n = 411)
Improved
8 (8.8%)No Change
72 (79.1%)
Deteriorated
11 (12.1%)
NSOT (n = 412)
Improved
5 (5.5%)
No Change
77 (84.6%)
Deteriorated
9 (9.9%)EAP (n = 412)
Improved
3 (3.3%)
No Change
82 (90.1%)
Deteriorated
6 (6.6%)ICCOW (n = 413)
Improved
0 (0%)No Change
78 (85.7%)
Deteriorated
13 (14.3%)
NLWTEWO (n = 412)
Improved
3 (3.3%)
No Change
74 (82.2%)
Deteriorated
13 (14.4%)

Junior High School

Female Teacher

Male Teacher

Female Teacher

20 (9.9%)143 (70.8%)
39 (19.3%)

7 (10.8%)
49 (75.4%)
9 (13.8%)

4 (7.7%)35 (67.3%)
13 (25.0%)

24 (11.8%)
129 (63.5%)
50 (24.6%)

11 (16.9%)
40 (61.5%)
14 (21.5%)

4 (7.5%)31 (58.5%)
18 (34.0%)

22 (10.9%)
146 (72.3%)
34 (16.8%)

7 (10.8%)
47 (72.3%)
11 (16.9%)

4 (7.7%)38 (73.1%)
10 (19.2%)

17 (8.4%)
155 (76.4%)
31 (15.3%)

5 (7.7%)50 (76.9%)
10 (15.4%)

2 (3.8%)33 (63.5%)
17 (32.7%)

26 (12.8%)
106 (52.2%)
71 (35.0%)

6 (9.2%)40 (61.5%)
19 (29.2%)

8 (14.8%)
29 (53.7%)
17 (31.5%)

24 (11.8%)
135 (66.5%)
44 (21.7%)

10 (15.4%)
44 (67.7%)
11 (16.9%)

8 (14.8%)
31 (57.4%)
15 (27.8%)

16 (8.0%)
149 (74.1%)
36 (17.9%)

8 (12.3%)
50 (76.9%)
7 (10.8%)

4 (7.4%)35 (64.8%)
15 (27.8%)

19 (9.4%)
129 (63.5%)
55 (27.1%)

8 (12.3%)
45 (69.2%)
12 (18.5%)5 (9.3%)34 (63.0%)
15 (27.8%)

26 (12.9%)
146 (72.3%)
30 (14.9%)

10 (15.4%)
47 (72.3%)
8 (12.3%)

5 (9.4%)41 (77.4%)
7 (13.2%)

19 (9.4%)
161 (79.7%)
22 (10.9%)

7 (10.8%)
51 (78.5%)
7 (10.8%)5 (9.3%)42 (77.8%)
7 (13.0%)

9 (4.4%)173 (85.2%)
21 (10.3%)

6 (9.2%)53 (81.5%)
6 (9.2%)5 (9.4%)42 (79.2%)
6 (11.3%)8 (3.9%)169 (83.3%)
26 (12.8%)

6 (9.2%)49 (75.4%)
10 (15.4%)

6 (11.1%)
39 (72.2%)
9 (16.7%)

13 (6.4%)
153 (75.0%)
38 (18.6%)

5 (7.7%)54 (83.1%)
6 (9.2%)4 (7.5%)41 (77.4%)
8 (15.1%)

p-Value
0.82

0.06

0.42

0.10

0.73

0.24

0.33

0.499

0.86

0.89

0.40

0.02

0.50

p-values were derived from Fisher’s exact test analysis. Abbreviations: NSOR = No sense of reality; NSOT = No
sense of time; EAP = Emotions are paralyzed; ICCOW = I cannot concentrate on work at all; NLWTEWO = No
longer wanting to engage with others.

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Int. J. Environ. Res. Public Health 2022, 19, 7568

3.3. Changes in Physical Symptoms before and after School Closure Due to COVID-19
Changes in physical conditions before and after school closure are presented in Table 3.
Fischer’s exact test showed significant differences for insomnia/nightmares (p = 0.009) andstanding dizziness (p = 0.03). Regarding insomnia/nightmares, many female teachers ofelementary (18.0%) and junior high schools (18.5%) answered that physical symptoms had
deteriorated. Moreover, at the same time, a small number of female teachers responded
that their insomnia/nightmare symptoms had improved (elementary female teacher: 7.8%,
junior high school female teacher: 9.3%). Regarding standing dizziness, many female
teachers in junior high school answered that it had deteriorated. In addition, a multiple
comparisons test was performed for insomnia/nightmares and standing dizziness as a post
hoc test, and no significant differences were found.
Table 3. Changes in physical symptoms before and after school closures due to COVID-19.
Elementary School
Male Teacher
Insomnia/nightmares (n = 413)
Improved
1 (1.1%)
No Change
82 (90.1%)
Deteriorated
8 (8.8%)Palpitations (n = 413)
Improved
1 (1.1%)
No Change
86 (94.5%)
Deteriorated
4 (4.4%)Standing dizziness (n = 414)
Improved
1 (1.1%)
No Change
85 (93.4%)
Deteriorated
5 (5.5%)
Sweating (n = 414)
Improved
1 (1.1%)
No Change
85 (93.4%)
Deteriorated
5 (5.5%)
Dyspnea (n = 414)
Improved
1 (1.1%)
No Change
87 (95.6%)
Deteriorated
3 (3.3%)
Digestive symptoms (n = 414)
Improved
1 (1.1%)
No Change
85 (93.4%)
Deteriorated
5 (5.5%)

Junior High School

Female Teacher

Male Teacher

Female Teacher

16 (7.8%)152 (74.1%)
37 (18.0%)

2 (3.2%)56 (88.9%)
5 (7.9%)5 (9.3%)39 (72.2%)
10 (18.5%)

12 (5.9%)173 (84.4%)
20 (9.8%)

1 (1.6%)59 (92.2%)
4 (6.2%)0 (0%)48 (90.6%)
5 (9.4%)16 (7.8%)
169 (82.4%)
20 (9.8%)

2 (3.1%)57 (89.1%)
5 (7.8%)0 (0%)
46 (85.2%)
8 (14.8%)

7 (3.4%)182 (88.8%)
16 (7.8%)

1 (1.6%)56 (87.5%)
7 (10.9%)

1 (1.9%)49 (90.7%)
4 (7.4%)5 (2.4%)190 (92.7%)
10 (4.9%)

1 (1.6%)59 (92.2%)
4 (6.2%)0 (0%)
51 (94.4%)
3 (5.6%)7 (3.4%)176 (85.9%)
22 (10.7%)

0 (0%)
58 (90.6%)
6 (9.4%)

1 (1.9%)48 (88.9%)5 (9.3%)

p-Value
0.009

0.13

0.03

0.83

0.93

0.55

p-Value derives from Fisher’s exact test analysis.

3.4. Impact of COVID-19 on Work before and after School Closure
The impact of COVID-19 on work before and after school closures presented inTable 4. Fischer’s exact test showed no significant differences among all four items. In sex
comparisons, a greater proportion of female respondents stated that they had deteriorated
in lowering of concentration ability compared with male teachers. In the comparison
between the types of school, a large proportion of junior high school teachers answered that
all four items deteriorated. In terms of lowering of work efficiency, >15% of the teachers
indicated that they had deteriorated, especially junior high school teachers who indicatedthat they had deteriorated to a greater extent than elementary school teachers.

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Table 4. Impact of COVID-19 on work before and after school closures.
Elementary School
Male Teacher
EIITW (n = 406)
Improved
6 (6.7%)
No Change
76 (85.4%)
Deteriorated
7 (7.9%)LOTTA (n = 408)
Improved
7 (7.9%)No Change
70 (78.7%)
Deteriorated
12 (13.5%)
LOTCA (n = 403)
Improved
6 (6.8%)No Change
66 (75.0%)
Deteriorated
16 (18.2%)
LOTWE (n = 406)
Improved
1 (1.1%)
No Change
73 (83.0%)
Deteriorated
14 (15.9%)

Junior High School

Female Teacher

Male Teacher

Female Teacher

18 (9.0%)154 (77.0%)
28 (14.0%)

4 (6.2%)54 (83.1%)
7 (10.8%)

6 (11.5%)
36 (69.2%)
10 (19.2%)

13 (6.5%)
154 (77.0%)
33 (16.5%)5 (7.7%)50 (76.9%)
10 (15.4%)

3 (5.6%)38 (70.4%)
13 (24.1%)

11 (5.6%)
151 (77.0%)
34 (17.3%)

3 (4.6%)53 (81.5%)
9 (13.8%)

4 (7.4%)37 (68.5%)
13 (24.1%)

11 (5.5%)150 (75.0%)
39 (19.5%)

3 (4.6%)46 (70.8%)
16 (24.6%)

3 (5.7%)36 (67.9%)
14 (26.4%)

p-Value
0.37

0.80

0.79

0.31

Abbreviations: EIITW = Excessively immersed in the work; LOTTA = Lowering of the thinking ability;
LOTCA = Lowering of the concentration ability; LOTWE = Lowering of the work efficiency. p-values were
derived from Fisher’s exact test analysis.

3.5. Stress Causes for Teachers Associated with COVID-19 Infection Control
The stress situation due to COVID-19 infection countermeasures is presented in Table 5.
Fischer’s exact test showed significant differences in four items, namely, always wearing
mask (p = 0.04), disinfection work by teachers (p = 0.03), activity restriction during class(p = 0.05), and prohibition or restriction of play and conversation (p = 0.04). In addition,
when the multiple comparison tests were carried out as a post hoc test for the four items thatshowed significant differences, there were two significant differences in faculty disinfection
work, between the two combination parts of male teachers in elementary school and female
teachers in junior high school (p = 0.04) and female teachers in elementary school andfemale teachers in junior high school (p = 0.03).
Table 5. Stress causes for teachers associated with COVID-19 infection control.
Elementary School
Male Teacher
AWM (n = 413)
29 (31.9%)
62 (68.1%)
BTAHSG (n = 413)
No
67 (73.6%)
Yes
24 (26.4%)
DWBT (n = 412)No
42 (46.2%)
Yes
49 (53.8%)
Social distance (n = 413)
No
59 (64.8%)
Yes
32 (35.2%)
ARDC (n = 413)
No
23 (25.3%)
Yes
68 (74.7%)
UFS (n = 413)
No
68 (74.7%)
Yes
23 (25.3%)

No
Yes

Junior High School

Female Teacher

Male Teacher

Female Teacher

50 (24.6%)
153 (75.4%)

27 (41.5%)
38 (58.5%)

12 (22.2%)
42 (77.8%)

144 (70.9%)
59 (29.1%)

52 (80.0%)
13 (20.0%)41 (75.9%)
13 (24.1%)

91 (45.0%)
111 (55.0%)

25 (38.5%)
40 (61.5%)

13 (24.1%)
41 (75.9%)

138 (68.0%)
65 (32.0%)

43 (66.2%)
22 (33.8%)

40 (74.1%)
14 (25.9%)

74 (36.5%)
129 (63.5%)

28 (43.1%)
37 (56.9%)

24 (44.4%)
30 (55.6%)

143 (70.4%)
60 (29.6%)

56 (86.2%)
9 (13.8%)

40 (74.1%)
14 (25.9%)

159

p-Value
0.04
0.54
0.03
0.71
0.05
0.08

Int. J. Environ. Res. Public Health 2022, 19, 7568

Table 5. Cont.
Elementary School
Male Teacher

Junior High School

Female Teacher

Male Teacher

Female Teacher

99 (48.8%)
104 (51.2%)

36 (55.4%)
29 (44.6%)

33 (61.1%)
21 (38.9%)

113 (55.7%)
90 (44.3%)

32 (49.2%)
33 (50.8%)

26 (48.1%)
28 (51.9%)

153 (75.4%)
50 (24.6%)

48 (73.8%)
17 (26.2%)

45 (83.3%)
9 (16.7%)

POROPAC (n = 413)
No
35 (38.5%)
Yes
56 (61.5%)
EWTNR (n = 413)
No
42 (46.2%)
Yes
49 (53.8%)
DNLCR (n = 413)
No
70 (76.9%)
Yes
21 (23.1%)

p-Value
0.04
0.42
0.62

Abbreviations: AWM = Always wearing mask; BTAHSG = Body temperature and health status grasping;
DWBT = Disinfection work by teachers; ARDC = Activity restriction during class; UFS = Using face shield;
POROPAC = Prohibition or restriction of play and conversation; EWTNR = Eat without talking /no refills;
DNLCR = Do not let children serve. p-values were derived from Fisher’s exact test analysis.

4. Discussion
In Japan, the number of teachers suffering psychologically and physical increased
more than twice over the 10-year period from 2002 to 2011, with an actual number of morethan 5000 [38]. Worldwide, teachers’ occupational mental health problems are serious [24],and main teachers’ reasons for leaving are mental illness resulting from stress disorders
and depression. Changes in schools due to COVID-19 have been linked with increased
stress for faculty staff. This study assessed psychological and physical changes in teachers
by school type and sex during the COVID-19 pandemic. The results showed significant
differences in psychological symptom changes for I cannot concentrate on work at all, andin physical symptom changes for insomnia/nightmares and standing dizziness. Additionally,
there were significant differences between males and females in elementary and junior highschool teachers in the items of always wearing a mask, disinfection work by teachers, activity
restriction during class, and prohibition or restriction of play and conversation in stressful situations
resulting from the implementation of COVID-19 infection control measures. Furthermore,
multiple comparison tests were performed on items for which a significant difference wasfound. As a result, two significant differences were found. The first was in the combinationof elementary school male teachers–junior high school female teachers and improved–
unchanged in I cannot concentrate on work at all. The second was in terms of stress—there
was a significant difference between elementary school male teachers–junior high school
female teachers and elementary school female teachers–junior high school female teachers
in disinfection work by teachers.
The significant differences in the combination of female teachers in elementary school
female teachers in male schools and improved–no change in I cannot concentrate on work at allof the psychological change may be attributed to the fact that the perceived stress and burdenof elementary and middle school faculty was alleviated by COVID-19 spread. Previous investigations have reported that junior high school teachers are stressed by instructing students
in club activities, and that elementary school teachers are stressed by the implementationof executive officials and committees [39]. Almost all students in junior high schools inJapan belong to clubs, and teachers provide guidance to students outside hours every day,including in holidays. In reality, junior high school teachers provide club activities even at
the expense of holidays, and this is regarded as a cause of stress for teachers in Japan. With
this in mind, it is possible that the burden on junior high school teachers was greatly reducedby the limitations on club activities due to the spread of COVID-19.
In Japan, more females than males work and are responsible for housework andparenting, and the decrease in teaching time for division activities requiring out-of-hours
work is believed to directly affect the reduction in the burden on female teachers in juniorhigh schools. Worldwide, it has also been reported that the burden of female teachers who

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are compatible with home and work is substantial [40,41], and the results of this survey
may be transferrable to other countries.
In terms of physical changes, Fischer’s exact test showed significant differences in two
items, insomnia/nightmares and standing dizziness. However, all post hoc tests for multiple
comparisons showed no significant differences. In terms of insomnia/nightmares, elementaryand junior high school female teachers showed higher improved response rates than maleteachers. In contrast, there was a higher number of female teachers who responded that
their insomnia/nightmares had deteriorated. Regarding this, the causes of insomnia/nightmares
is believed to be stress, and females are more likely to feel psychological stresses, suchas anxiety, than males. Therefore, while COVID-19 induces stress due to changes, at thesame time, the reduction in workload may reduce stress. Regarding standing dizziness, theresponse rate of the participants who answered that the standing dizziness deteriorated washigher in middle school teachers than the elementary school teachers. For females, 0% ofjunior high school teachers and 7.8% of elementary school teachers responded that standing
dizziness had improved, with the percentages of value differing by 7.8%. This may be
because club activities are a major burden for junior high school teachers, and furthermore,
female find balancing work with housework and childcare is stressful. However, someteachers answered that they improved in all items, and it is suggested that there somepositive aspects to the spread of COVID-19.
No significant differences were found between school type and teacher sex in changes
in impact on work. When checking each item, about 70% of teachers answered that there
was no change in all items. Moreover, regardless of the sex and school type, approximately
10–20% of teachers responded that they had deteriorated in terms of decreased thinking,concentration, and lowering of the work efficiency, and a small percentage of teachers responded
that they had improved. From this, it is suggested that the deterioration of thinking ability,
concentration, and work efficiency occurred in some school teachers irrespective of thetype of school and sex because anxiety [27] was newly generated by COVID-19.
The Fischer’s exact test found significant differences in the items always wearing amask, disinfection work by teachers, activity restriction during class, and prohibition or
restriction of play and conversation in the stress situation resulting from the implementation of COVID-19 infection countermeasure. Furthermore, the results of the multiple
comparison test revealed significant differences in teacher disinfection work. In always
wearing a mask, a higher proportion of females felt stressed compared with males. Thismay be because make-up adheres to the mask and make-up is spoiled [42]; in addition,hairstyles do not last well under the moisture from wearing the mask [43], and females
report feeling stress from this.
Regarding disinfection work by teachers, a higher proportion of junior high school
teachers reported feeling stressed compared with elementary school teachers. In addition,
the percentage of females who answered that they were feeling stressed was higher thanmales. This is thought to be due in part to the fact that junior high schools have club
activities and the range of activities is wider than that of elementary schools; therefore, therange of disinfection by teachers is also wider. In addition, generally, more females are
more sensitive than males about their appearance and hands, consequently, the continuous
use of disinfectants, which can cause rough hands [44], may make females more sensitiveand susceptible to stress than in males. Moreover, given that females have lower physicalstrength than young males, they are more likely to feel the burden considering that the disinfection range is broad, including desks, chairs, rockers, and other items [36], which takes
time and effort because carrying equipment, such as cleaning tools, can be a heavy burden.
Regarding the items of activity restriction during class and prohibition or restrictionof play and conversation, a higher proportion of elementary school teachers felt stressedcompared with junior high school teachers. Additionally, the percentage of males feelingstressed was higher than that of females. This is thought to have led to the burden andstress among elementary school teachers because there are more children in elementary
school who are mentally younger than those in junior high school, and it is difficult161

Int. J. Environ. Res. Public Health 2022, 19, 7568

to take command. Male elementary school teachers can be more stressed than female
teachers because male teachers are more in charge of instructing students. Male teachers,
in particular, often yell to discipline their students, which can be a reason for stress [45].
Equally, a number of teachers felt they had improved in some items, and it can be said
that the spread of COVID-19 is not only bad but also that it has had a positive effect insome areas.
There are several limitations to this study. First, Fisher’s exact test showed significant
differences, whereas the multiple comparison test showed that some of them did not
significantly differ. As 18 tests were performed for a single item in this study’s multiple
testing, it is possible that a so-called type 2 error occurred. Second, this study is aimed atteachers in Tokyo, and the opinions of teachers in other areas are not reflected. Thirdly, thisstudy did not perform model analysis. Accordingly, the current study tested and reported
the distribution of the results obtained during the COVID-19 pandemic and epidemic andthe differences between them. Some of the dependent variables included herein had threelevels, so all analyses were performed in a unified manner using Fisher’s exact test for easeof interpretation. Given that this survey found significant differences between the levels,
further research considering the use of models, such as logistic regression analysis, can beconsidered in order to evaluate the factors. Nonetheless, this study is the first to assess
differences in elementary and junior high schools among teachers and psychological andphysical differences between sex due to the COVID-19 pandemic.
The emphasized point of our study is that previous reports have only assessed therelationship between sexes [29] or school differences [46] and psychological and physicaleffects, and no survey like this one has previously been conducted. In addition, it isinteresting to evaluate not only the rate of increasing stress due to the spread of COVID-19
but also the rate of improvement. In fact, statistically significant differences between maleand female teachers in elementary and junior high schools were also observed in the rate of
improvement in this study. Reports evaluating the percentage of points that were improved
by COVID-19 spread are unusual and remarkable. Even after COVID-19 has ended, it will
be important to conduct surveys to assess the differences in improvements and deterioration
between schools and sex, as well as overall improvement and deterioration, and to evaluate
the causes objectively.
Teachers play an important role not only in their studies but also in the developmentof children’s mental health, as experts in education in daily contact with students andchildren [47]. Teachers’ mental health also affects students’ educational quality, so it isimportant to understand and improve teachers’ psychological and physical situations andstress. While the spread of COVID-19 has increased the stressors of many teachers, it isalso possible that the work has been streamlined. It is recommended that administrative
personnel involved in education and members of local educational committees conduct
surveys such as this on a regular basis and assess the results to understand the mental
health of faculty members and to strive to improve the working environment.
5. Conclusions
COVID-19 resulted in differences in psychological and physical changes between maleand female teachers in elementary and junior high schools. While most teachers showed nochange, some experienced psychological and physical stress, whereas others experienced
improvement. Policymakers of administrative and educational committees should conduct
surveys like this on teachers on a regular basis to understand the psychological and physicalstress situations of teachers in a multidimensional manner as well as to decide the policy
for the improvement.
Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph19137568/s1, COVID-19 questionnaire survey.

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Author Contributions: N.W. and M.K. designed the study. N.N., K.I., C.T., R.M., M.O., T.Y. and M.K.
collected data. N.W. and N.N. analyzed the data. N.W. wrote the manuscript. N.N., Y.Y., S.S., K.S.
and M.K. critically revised the manuscript for important intellectual content. All authors have readand agreed to the published version of the manuscript.
Funding: This research did not receive any external funding.
Institutional Review Board Statement: The study was approved by the Ethics Committee of the
Hoshi University (approval no: 2020-05).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data is not publicly available as all participants have not consented
to the public disclosure of the data online. However, the data presented in this study are available on
request from the corresponding author.
Acknowledgments: We thank all the teachers who participated in this study.
Conflicts of Interest: The authors declare no conflict of interest.

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165

International Journal ofEnvironmental Researchand Public Health
ArticleThe Control of Metabolic CO2 in Public Transport as a Strategy
to Reduce the Transmission of Respiratory Infectious Diseases
Marta Baselga 1 , Juan J. Alba 1,2 and Alberto J. Schuhmacher 1,3, *
1
2
3

*

Citation: Baselga, M.; Alba, J.J.;
Schuhmacher, A.J. The Control of
Metabolic CO2 in Public Transport asa Strategy to Reduce theTransmission of Respiratory

Institute for Health Research Aragon (IIS Aragón), 50009 Zaragoza, Spain; mbaselga@iisaragon.es (M.B.);
jjalba@unizar.es (J.J.A.)
Department of Mechanical Engineering, University of Zaragoza, 50018 Zaragoza, Spain
Fundación Agencia Aragonesa para la Investigación y el Desarrollo (ARAID), 500018 Zaragoza, Spain
Correspondence: ajimenez@iisaragon.es

Abstract: The global acceptance of the SARS-CoV-2 airborne transmission led to prevention measures
based on quality control and air renewal. Among them, carbon dioxide (CO2 ) measurement has
positioned itself as a cost-efficiency, reliable, and straightforward method to assess indoor air renewal
indirectly. Through the control of CO2 , it is possible to implement and validate the effectiveness of
prevention measures to reduce the risk of contagion of respiratory diseases by aerosols. Thanks to
the method scalability, CO2 measurement has become the gold standard for diagnosing air quality
in shared spaces. Even though collective transport is considered one of the environments with thehighest rate of COVID-19 propagation, little research has been done where the air inside vehicles
is analyzed. This work explores the generation and accumulation of metabolic CO2 in a tramway(Zaragoza, Spain) operation. Importantly, we propose to use the indicator ppm/person as a basis
for comparing environments under different conditions. Our study concludes with an experimental
evaluation of the benefit of modifying some parameters of the Heating–Ventilation–Air conditioning(HVAC) system. The study of the particle retention efficiency of the implemented filters shows apoor air cleaning performance that, at present, can be counteracted by opening windows. Seeking a
post-pandemic scenario, it will be crucial to seek strategies to improve air quality in public transport
to prevent the transmission of infectious diseases.

Infectious Diseases. Int. J. Environ.
Res. Public Health 2022, 19, 6605.
https://doi.org/10.3390/

Keywords: airborne; CO2 ; collective transport; SARS-CoV-2; tramway; filtration; infectious diseases;
epidemiology; public health; COVID-19

ijerph19116605
Academic Editors: Sachiko Kodera
and Essam A. Rashed
Received: 26 April 2022
Accepted: 27 May 2022
Published: 28 May 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
Public health strategies are modulated by adjusting to the development of knowledge about the transmission routes of COVID-19. The viral transmission of SARS-CoV-2
human–human has been described from direct respiratory dissemination and indirect
dissemination. On the one hand, direct respiratory dissemination, where the symptomaticor asymptomatic patient expels contaminated particles in respiratory events, and, on theother hand, indirect dissemination or via fomites, where transmission is due to contact
with contaminated surfaces. On the other hand, it is possible to differentiate between thedroplet and bioaerosol models with indirect dissemination. While droplets predominate
in close contact, bioaerosols can be transmitted through the air over time and distance [1].
Regarding this pandemic, the scientific community has redefined the concept of bioaerosol,
extending its consideration to airborne particles smaller than 100 µm, based on evidence
and common factors related to the aerodynamics of the particles [1,2]. The spread patternsof SARS-CoV-2 could not be explained by traditional epidemic models, where homogeneity
in the transmission is assumed [3]. As recognized by the WHO in April 2021 [4], a predominance airborne way has been suggested compared to other propagation models [1,5].
The size of the SARS-CoV-2 virion varies between 70 and 90 nanometers [6,7], and an
average concentration of the virus in the sputum of 7.0 × 106 copies/mL and a maximum of
167

Int. J. Environ. Res. Public Health 2022, 19, 6605

2.35 × 109 copies/mL [8]. Consequently, the viral load occupies 2.14 × 106% del bioaerosol
on average. With this value, Lee [9] estimated a theoretical minimum and initial aerosol
size of 4.7 µm to contain SARS-CoV-2. However, experimental bioaerosol sampling studies
suggest the presence of the virus in smaller particle sizes (even < 0.25 µm) [10–13]. Despite
numerous factors influencing the airborne transmission of pathogens, such as dynamics or their aerial persistence, contagion events can only be explained by a medium andlong-distance airborne transmission model [5]—for example, among small animals [14,15],from viral superspreading events [16], in the long-distance transmission where infectedindividuals do not come into contact direct [17], by asymptomatic individuals transmission
rates [18], and by the prevalence of spread in closed spaces [19]. Specifically, a superspreading event affected public transportation. Shen et al. [20] reported a massive infection of
24/68 (35.3%) people from a single infected individual while being transported in a bus
with air recirculation and poor ventilation.
At the pandemic’s beginning, this route of contagion was dismissed, and more attention was paid to contagion by droplets and fomites. Consequently, there was controversy
about whether asymptomatic infected individuals could be transmitters of SARS-CoV-2.
However, currently, it has been estimated that 44% (CI95; 30–57%) of secondary cases were
infected during the incubation period [21], where the individuals were asymptomatic. The
global acceptance of the COVID-19 airborne spread allowed an improvement in the preventive methods, including new techniques for epidemiological management, such as themeasurement of exhaled carbon dioxide (CO2 ) as an indicator of the risk of contagion [2,22].
Carbon dioxide measurement began to be used in the 19th century to design ventilation
systems in architecture [23]. In the pre-pandemic period, CO2 measurement helped improveacademic performance in schools and colleges [24] and, sporadically, control infectious
diseases [25]. Due to the COVID-19 pandemic, CO2 measurement has become one of thepreferred preventive strategies to reduce the risk of contagion by aerosols [22,26,27]. The
direct measurement of aerosols to determine the risk of contagion by SARS-CoV-2 is highly
complex and expensive since it requires highly specialized equipment. While there are
handheld instruments or simple sensors to direct measure of aerosol concentrations, these
instruments present different limitations such as they can not discriminate human-exhaledversus environmental aerosols; usually, they cover a limited range of particle diameter andhardly measure the submicronic particles. To overcome these hurdles, the CO2 level has
been suggested as an indirect indicator of respiratory infectious diseases’ transmission [22].
CO2 is co-expired with bioaerosols that may contain SARS-CoV-2 in infected people [28–30].
Its quantification provides an idea of indoor air renewal and establishes the risk of infection
as it depends on the viral load [31]. Consequently, the measurement of indoor CO2 issuggested as a reasonable ventilation proxy for respiratory infectious disease. Through its
reading, it is possible to determine what percentage of the air has been exhaled by anotherindividual (y) according to the expression y = Ce x + Ca (1 − x ), where Ce corresponds
to the concentration of CO2 in exhaled air (estimated at 40,000 ppm), Ca to ambient CO2
concentration, and x to the fraction of exhaled air. For example, if we assume a basal valueis 440 ppm (fresh air outdoors), a group of people manages to increase it to 2300 ppm. In
that case, the approximate percentage of air that those individuals have already breathedwill be around 4.7%.
Despite the ventilation rates being known to influence the concentration of microorganisms in the environment [32], the increase in the exhalation rate of aerosols depending
on CO2 has been poorly explored [30]. The concentration of airborne particles and the levelof CO2 cannot be directly related due to a disparity between the bioaerosols generated andthe respiratory activity [28]. For example, aerosol generation during forced vocalizationor coughing is not comparable to emission rates during respiration [33]. Thus, two different scenarios (for example, a library versus a gym) with similar CO2 levels have to beinterpreted individually.
To date, COVID-19 superspreading events have been reported indoors [34–40]. Thus,
the use of air renewal proxy indoors is crucial for maintaining spaces with a low risk of con-168

Int. J. Environ. Res. Public Health 2022, 19, 6605

tagion by aerosols. Many countries are making high economic investments to equip schools
with CO2 meters [41,42]. In addition, other isolated initiatives have successfully implemented this methodology in shopping centers [43], collective transport [44–46], offices [47],or university and school classrooms [47–52]. Specifically, a recent study in Italy reported
an 82% reduction in secondary COVID-19 infections in schools where they controlled air
renewal from CO2 measurements [53].
Currently, CO2 concentration limits have been proposed as a reference to minimize
COVID-19 spreading. Usually, it is set between 700 and 1000 ppm regardless of theevent [54,55]. Urban collective transport is one of the policies designed to promote sustainable cities [56]. To prevent respiratory infectious diseases spread, it is important toanalyze the risk involved in every specific means of transport. References on the emissionof bioaerosols in collective transport are scarce despite being the environment with thesecond-highest transmission rate of SARS-CoV-2. Lan et al. [57] point to 18% of cases in thetransport sector, only behind the health sector (22%) in the transmission rate of COVID-19
disease. Before the pandemic, some reports pointed to metabolic CO2 concentrations of
up to 3700 ppm in buses [58–61], suggesting poor air renewal. However, due to the pandemic, it has been possible to reduce it to <800 ppm by implementing simple ventilation
measures [45]. The operating conditions of the subways require reinforcement of artificial
ventilation, for which values close to 1000 ppm have been found [44,62,63].
Trams have similar characteristics to buses since they circulate outside, and the contribution of natural ventilation can substantially favor air renewal. However, no specificinformation on air quality in trams has been reported. This work evaluates the accumulation of metabolic CO2 in the Zaragoza Tram (Spain) in circulation under different conditions.
On the one hand, the objective was to analyze the concentration of CO2 in different events(e.g., weekend versus midweek, with and without air recirculation or with different weather
conditions). To compare air renewal regardless of the event, the ppm/person indicator
was used. However, secondary air purification methods that affect contagion risk, suchas added air filtration, must also be considered. Then, the performance of the installed
filtration system is analyzed against the concentration of submicron aerosols (such as theairborne virus SARS-CoV-2). The work concludes with suggestions for measuring CO2 andrecommendations to reduce the risk of contagion in collective transport.
2. Materials and Methods
2.1. Measurement of Metabolic CO2
The metabolic CO2 level was measured using Aranet 4 Pro meters (Aranet WirelessSolutions España SL, Madrid, Spain), with technical characteristics shown in Table 1. The
increase in CO2 (∆CO2 ) was determined according to Equation (1):∆CO2 = CO2,indoors − CO2,outdoors(1)

Table 1. Technical characteristics of the Aranet 4 Pro meters.
CO2
Temperature
Relative humidity
Atmospheric pressure

Parameters measured<9999 ppm
0–50 ◦ C
0–85%
0.3–1.1 atm

Sensor type

N-DIR (Non-Dispersive Infrarred)

Communication technology

Bluetooth (−12–4 dBm)

Sampling frequency

1 min

Precision±50 ppm (CO2 )Dimensions/Weight

70 × 70 × 24 mm/104 g

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Int. J. Environ. Res. Public Health 2022, 19, 6605

4 of 20

Precision
Dimensions/Weight±50 ppm (CO2)70 × 70 × 24 mm/104 g

Int. J. Environ. Res. Public Health 2022, 19, 6605

The Urbos
Urbos 33 tramtram models
Themodels (CAF,(CAF, Beasain
Beasain ES)
ES) have
have aa total
total length
length of
of 33
33 m,
m,aawidth
widthof
of
2.65
m,
and
a
height
of
3.2
m.
They
have
a
capacityof200seats,
of
which
54
are
2.65 m, and a height of 3.2 m. They have a capacity of 200 seats, of which 54 areseats.
seats.
Travelers wore
wore aa mask
windows
remainedpartially
open
duringall
Travelers
mask atatall
alltimes,
times,and
andthethewindows
remainedpartially
open
duringroutes.
EightCO
2 meters were installed at a 2.25 m height at different points of the Tram,all routes. Eight CO2 meters were installed at a 2.25 m height at different points of theaccordingto the to
distributionof Figure
1a. Thewas towas
obtainrealistic
and uniTram,
accordingthe distributionof Figure
1a.objective
The objective
to obtain
realisticandform
measurements,
representative
of
the
level
of
exposureexperiencedbyanuniform measurements, representative of the level of exposure experienced by anaverage
averageuser without
without runningrunning theuser
the risk
risk thatthat thethe measurement
measurement would
would bebe alteredaltered due
due toto thethe direct
directexhalationofthepassengers.
As
shown
in
Figure1b,
themeterswere
installedon grabexhalation of the passengers. As shown in Figure 1b, the meters were installed on grab
bars,
bars,
for which
was necessary
to manufactureanti-vandal
housings
to guarfor
which
it wasitnecessary
to manufactureanti-vandal
housings
withwithholesholes
to guarantee
antee
air transfer.
airtransfer.

Figure 1. Schematic representation (a) of the meters distribution in the Tram and (b) installed senFigure 1. Schematic representation (a) of the meters distribution in the Tram and (b) installed sensors
sors in the Tram. Where, # refers to the meter ID.
in the Tram. Where, # refers to the meter ID.

2.2. Probability
Probability ofof Contagion
2.2.
Contagion Determination
Determination by
by thethe CO
CO22 Level
Level
The CO22 measurement
was
used
as
a
tool
to
determinemeasurement was used as a tool determine thethe risk
risk of contagion.
contagion. ThisThis
was possible
possible thanks to the theoretical model
wasmodel updated
updated by
by PengPeng andand Jiménez
Jiménez[22][22]andandthetheAireamos consortium
consortium [31].
Aireamosriskofofairborne
airborneindoors
indoorstransmission
personin onehour)
was deThe risk
transmission(for(foroneonepersonin onehour)
P was described
scribedan alternativeequationthatof Wells–Riley(Equation
enunciatedfrom
anfromalternativeequationto thattoof
Wells–Riley[64] [64](Equation(2)),(2)),
enunciatedby
byRudnicketal.
[65](Equation(3)):
Rudnick et al. [65] (Equation (3)):
P = 1 − e−n
(2)
(2)
= 1
−

Itq f
R
(3)
P = 1 − exp(3)= 1 − exp nwhere I is the number of infected people in a space, t is the exposure time measured in hours,
where is the number of infected people in a space, is the exposure time measured in
q is the number of pathogens spread per hour, f is the fraction re-inhaled ([C − C0 ]/Ca ), n
hours, is the number of pathogens spread per hour, is the fraction re-inhaled ( −is the number of people exposed to the infectious individual, and R is the particle retention/ ), is the number of people exposed to the infectious individual, andis the parefficiency, that is, the fraction of retained aerosols by the PPE from the exposed individual.
In turn, C is the concentration of CO2 indoors, C0 outside, and Ca the concentration exhaledduring respiration, defined in parts per million (ppm). The value of n corresponds to theinfectious dose inhaled by a susceptible person. However, Rudnick et al. [65] assumed
some conditions for the model’s description: (1) the indoor air is thoroughly mixed, so

170

Int. J. Environ. Res. Public Health 2022, 19, 6605

the infectious aerosol generated can be found anywhere in the space. (2) The external
concentration of CO2 remains constant during the event. (3) Removal of viral aerosols due
to virus survival, filtration, or other mechanisms is negligible compared to ventilation.
Peng and Jiménez [22] applied another alternative to the Wells–Riley formulation
regarding the COVID-19 pandemic. The authors derived analytical expressions for the
probability of infection indoors through the concentration of CO2 . The expected value ofhni can be calculated for an uninfected person, assuming the probability that the individualis immune ηin according to Equation (4):hni = (1 − ηin )C p BD (1 − min )(4)

where C p corresponds to the average number of viruses (quantos.m3 ), B to the respiratory
rate of the susceptible person (m3 h−1 ) that will vary depending on the activity carried out
at that time, D the event duration (h), and min the filtration efficiency of the mask during
inhalation. Consequently, assuming no pre-existence of viral aerosols before the event, theanalytical expression for the expected value of C p can be described by Equation (5):ηin ( N − 1)E p (1 − mex )
Cp =
V



1
1 − e−λD
−
λ
λ2 D



(5)

where N is the number of occupants, E p is the exhalation rate of SARS-CoV-2 per infected
person (quantos.h−1 ), mex is the filtration efficiency of the mask during exhalation, V is thevolume of air in the space (m3 ), and λ the global rate constant of virus infectivity loss (h−1 ),including all those mechanisms that may affect virus survival (filtration, ventilation, etc.).
Assuming that the increase in CO2 (∆CO2 ) of the indoor air concerning that of the outdoor
air is only produced by human activity, it can be described as follows (Equation (6)):
n∆CO2 = ∆C p.CO2 BD(6)

where the CO2 increment volume and the CO2 exhalation rate per person mixing ratio(∆CO2 ), in m3 .h−1 , can be described as (Equation (7)), where λ0 corresponds at ventilation
rate (h−1 ):NE p.CO2 1
1 − e − λ0 D
∆C p.CO2 =
(7)
−
V
λ0
λ0 2 DAs a result of this model, Peng and Jiménez [22] propose an acceptable probability of
infection limit of p = 0.01%. Although it does not imply safety in any situation, since withhigh N and/or D and/or the event occurs many times, the probability of infection for thesusceptible person is understated.
2.3. Studied Routes of the Zaragoza Tram
Eighty-eight round trips (44 complete trips) with an average of ~40 min each wereanalyzed. As shown in Figure 2, each complete path stops at 42 stations. The routes
included in stations #7–#10/#33–#36, and #10–#16/#27–#33 correspond to the university
area and the city center, respectively.
The CO2 meters were installed for three months in the vehicle. We hypothesized thatthe variation in the meteorological data obtained during the study days could translate
into variations in the Tram’s ventilation capacity. The variables of interest for five referencedays of December 2020 are shown in Table 2. According to data provided by the Zaragoza
weather station, the average wind speed value in December was 3.25 (±1.65) m/s, so it canbe considered that on days B, C, and D, the values of wind speed and maximum gusts were
low. Low wind speed was an unfavorable condition for the natural ventilation of the Tram.

171

B
C
D
E

8.2 °C
5.4 °C
7.9 °C
8.9 °C

4.4 °C
3.3 °C
3.8 °C
6.2 °C11.9 °C
7.4 °C12.0 °C
11.6 °C

16 °
10 °
16 °
31 °

1.7 m/s 6.1 m/s
1.9 m/s 5.0 m/s
1.9 m/s 5.6 m/s
4.7 m/s 11.1 m/s

996.8 atm
998.2 atm
994.7 atm
994.9 atm

990.0 atm
996.0 atm
990.8 atm
992.4 atm

: temperature (average);Int. J. Environ. Res. Public Health 2022, 19, 6605

: temperature (minimum);: temperature (maximum);:: wind speed (average);: wind speed (maximum);: atmosphericwind direction;,,pressure (maximum); and: atmospheric pressure (minimum).

Figure 2. Route of the Zaragoza Tram. Where, # refers to the station ID.
Figure 2. Route of the Zaragoza Tram. Where, # refers to the station ID.

2.4. Determination of Filtration Efficiency against Submicron Particles and Filters’ Pressure
Table 2. Meteorological variables of the reference days A, B, C, D, and E. Information prepared by
Drop
the Agencia Estatal de Meteorología of Spain (data collected at the Valdespartera Station, Zaragoza
The filter’s performance was studied in-vitro to assess its effectiveness against subSpain, 23 December 2020).

micron particle sizes, as is the case with the SARS-CoV-2 virus and other respiratory viruses.
filter usedduring Tthewasspeciallydue
current
Day TheTaver
Tmin
Ws,aver implemented
Ws,maxP
max tests Dw
max to the P
min
◦
◦
◦
◦
COVID-19
pandemic(Coarse75%
accordingtoUNE-ENISO
16890,
Merak
Long
Life
FilA
9.6 C
6.1 C
13.1 C30
3.1 m/s
8.9 m/s
996.8 atm 990.0 atm◦ C As4.4◦C◦C
ter,BMadrid
depictedin Figure1,
in two8.2SP).
11.9
16◦two filters
1.7 m/swere
6.1arranged
m/s
996.8
atm HVAC
990.0 units
atm
C
D
E

5.4 ◦ C7.9 ◦ C8.9 ◦ C3.3 ◦ C3.8 ◦ C6.2 ◦ C7.4 ◦ C12.0 ◦ C
11.6 ◦ C

10◦
16◦
31◦

1.9 m/s
1.9 m/s
4.7 m/s

5.0 m/s
5.6 m/s
11.1 m/s

998.2 atm
994.7 atm
994.9 atm

996.0 atm
990.8 atm
992.4 atm

Taver : temperature (average); Tmin : temperature (minimum); Tmax : temperature (maximum); DW : wind direction;
Ws,aver : wind speed (average); Ws,max : wind speed (maximum); Pmax : atmospheric pressure (maximum); and Pmin :atmospheric pressure (minimum).

2.4. Determination of Filtration Efficiency against Submicron Particles and Filters’ Pressure Drop
The filter’s performance was studied in-vitro to assess its effectiveness against submicron particle sizes, as is the case with the SARS-CoV-2 virus and other respiratory viruses.
The filter used during the tests was specially implemented due to the current COVID-19
pandemic (Coarse 75% according to UNE-EN ISO 16890, Merak Long Life Filter, Madrid
SP). As depicted in Figure 1, two filters were arranged in two HVAC units installed in theTram, which drive a total flow of 2800–3300 m3 /h, with an air ratio of 1:3 fresh/return air.
As shown in Figure 3a, NaCl aerosols were produced using a Topas-ATM226 generator
with a saline solution of sodium chloride (3 wt.% NaCl in distilled water). Microdroplets
were evaporated using a tubular silica air dryer to produce solid particles. The particle size
distribution (Figure 3b) inside the cabin was measured using an SMPS TSI 3936 composedof an electrostatic classifier (DMA TSI 3081) and a condensation particle counter (CPC
TSI 3782). An 0.6 L/min flow rate drags the particles. The filter was placed between
bronze discs sealed with Teflon tape, with 30 × 20 mm Teflon washers on each side. The
desired flow rate was adjusted variating the exposed filter area (2.05, 4.1, and 8.1 mm). The
measurements lasted 120 s and were made in duplicate. Measurements were made passing172droplets were evaporated using a tubular silica air dryer to produce solid particles. The
particle size distribution (Figure 3b) inside the cabin was measured using an SMPS TSI
3936 composed of an electrostatic classifier (DMA TSI 3081) and a condensation particle
counter (CPC TSI 3782). An 0.6 L/min flow rate drags the particles. The filter was placed
between bronze discs sealed with Teflon tape, with 30 × 20 mm Teflon washers on eachInt. J. Environ. Res. Public Health 2022, 19, 6605
side. The desired flow rate was adjusted variating the exposed filter area (2.05, 4.1, and
8.1 mm). The measurements lasted 120 s and were made in duplicate. Measurements weremade passing through a free tube between measurements to calculate relative efficiency
througha freetube between
measurements
to calculate
relative efficiency
toaccordingto Equation(8), where
standsfor concentration
upstreamaccordingandEquation
(8),
where
C
standsforconcentration
upstream
andCstandsforconcenup downstream. The retention efficiency is expresseddown
stands for concentrationin global effitration
downstream.
The
retention
efficiency
isexpressed
inglobalefficiency as ‘global
ciency as ‘global number of particles’:number of particles’:Cup − −
Cdown
100
(8)
η ==
100
×
(8)
Cup

Figure 3. Performance test. (a) Diagram of the equipment used to characterize the filters andFigure3. Performancetest.
(a) Diagram
the equipmentused to characterize
the filters
and
(b)
(b)
particle
concentration
distribution
forofefficiency
determination
measurementsin therange
particle concentration distribution for efficiency determination measurements in the range 0.1–1.0 µm.
0.1–1.0 µm.

Accordingto
toBernoulli’s
Bernoulli’s principle,
principle, thethe pressurepressure drop
drop waswas carried
carried out
Accordingout usingusing alcohol
alcoholcolumns
connected
to
the
free
ends
of
the
tubes.
Measurements
were
also
made
withwith
a 0.6acolumns connected to the free ends of the tubes. Measurements were also madeL/minvolumetric
flow
rate.
0.6
L/min
volumetric
flow
rate.
2.5.Statistic
StatisticAnalysis
Analysis
2.5.
Thestatisticalstatistical analysis
analysis ofbeen
carriedout outusing
the R-UCA
v.4.0.2v.4.0.2
softTheofthe
thedatadatahashas
beencarriedusing
the R-UCA
ware (Universityof Cadiz
Spain,
2017)[66].[66].
Meancomparisons
wereweremademadewith with
the Stusoftware(Universityof Cadiz
Spain,
2017)
Meancomparisons
thedent’s t-test
at at
a 99%
confidence
interval(CI99).
Student’s
t-testa 99%
confidence
interval(CI99).
Results
3.3.Results
3.1.
3.1.COCO2 2Levels
Levelsalong
along thethe Route
Route Are
Are Closely
Closely Related
Related toto Occupancy
OccupancyAs
concentrationinside
insidethetheTram
TramgradgradAsshown
shownininFigure
Figure4a,b,
4a,b,thethe increase
increase inin thethe CO
CO22 concentrationually
increases
asit
approaches
thecity’sdowntownarea#33–#36
and#27–#33
stations;
ually increases as it approaches the city’s downtown area #33–#36 and #27–#33 stations;approximately,routes, respectively.
respectively. The
The
approximately,atatminutes
minutes20
20and
and70
70on
onthe
the outward
outward andand return
return routes,
CO
concerningCO
increasecorresponds
corresponds toto thethe difference
difference between
between the
the Tram
Tram indoor
indoor values
values concerning
2 2increasethe(atmospheric)registeredregisteredwith
withsensor
sensorAnalyzing
intheexternal
externalreference
reference value
value (atmospheric)#8.#8.
Analyzing
the theincrecrement
makes
it possible
to determine
globalcorresponding
toment makes
it possible
to determine
the theglobal
CO2CO
concentration
corresponding
to met2 concentration
metabolic
CO
to
rule
out
possible
external
contamination.
The
calculated
ppm/person
abolic CO2 to2 rule out possible external contamination. The calculated ppm/person ratio
ratio(Figure4c) suggests
a concentrationof ∆COthe areas
final areas
the routeassociated(Figure4c) suggests
a concentrationof ∆CO
2 in thefinalof theof
routeassociated
with
2 in
with
an
accumulation
of
CO
in
the
vehicle,which
beginstobeevident
after
driving
2 vehicle, which begins to be evident after driving throughan accumulation of CO2 in thethroughcity Itcenter.
It maybe explained
the numberof travelersinthe city thecenter.
may beexplained
because because
the numberof travelers
increasesincreases
in the city
the city center and accumulates CO2 not recirculated at subsequent stops. On average, theTram doors open for 16.6 ± 3.6 s at each stop.
∆CO2 concentration is closely related to tram occupancy (Figure 5a), although there
is some dispersion associated with external variables (Figure 5b). In absolute CO2 values,
the maximum average was 835 ± 232 ppm, reaching a maximum value of 1229 ppm. In
contrast, the lowest average was 541 ± 82 ppm. The pattern of ∆CO2 concentration on
weekdays compared to weekend days is different, although it follows similar trends. As
shown in Tables S1 and S2, the average of the trips made on weekends in the morning was
565 ± 318 ppm; in the afternoon, it was 580 ± 323 ppm, and, at night, it was 602 ± 330 ppm.
On weekdays, an average of 592 ± 319 ppm was obtained in the morning, 595 ± 324 ppm
in the afternoon, and 541 ± 292 ppm at night.

173

is some dispersion associated with external variables (Figure 5b). In absolute CO2 values,
the maximum average was 835 ± 232 ppm, reaching a maximum value of 1229 ppm. In
the maximum average was 835 ± 232 ppm, reaching a maximum value of 1229 ppm. In
contrast, the lowest average was 541 ± 82 ppm. The pattern of ∆CO2 concentration oncontrast, the lowest average was 541 ± 82 ppm. The pattern of ∆CO2 concentration on
weekdays compared to weekend days is different, although it follows similar trends. As
weekdays compared to weekend days is different, although it follows similar trends. As
shown in Tables S1 and S2, the average of the trips made on weekends in the morning
shown in Tables S1 and S2, the average of the trips made on weekends in the morning
was6605565 ± 318 ppm; in the afternoon, it was 580 ± 323 ppm, and, at night, it was 602 ± 330
Int. J. Environ. Res. Public Health 2022, 19,
was 565 ± 318 ppm; in the afternoon, it was 580 ± 323 ppm, and, at night, it was 602 ± 330
ppm. On weekdays, an average of 592 ± 319 ppm was obtained in the morning, 595 ± 324
ppm. On weekdays, an average of 592 ± 319 ppm was obtained in the morning, 595 ± 324
ppm in the afternoon, and 541 ± 292 ppm at night.
ppm in the afternoon, and 541 ± 292 ppm at night.

Figure 4. CO
levels(a)(a)
in in
all all
weekday
andand(b) (b)in allroutes,and (c)FigureCO2 2increment
incrementaverage
averagelevelsweekday
in weekend
all weekend
routes,andFigure 4. COratio
2 increment average levels (a) in all weekday and (b) in all weekend routes, and (c)ppm/person
averageandandmaximum
gap gap
alongroutes.
The error
bars in(a,b)
correspondto theto(c)ppm/person ratio
averagemaximumalongroutes.
The errorbars
in (a,b)correspond
ppm/person
ratio averageand maximum gapdata
alongroutes.
The errorbars
in (a,b)correspondto thedifference
betweenthe themaximum/minimum
andthe averagedataof allstudiedroutes.
thedifference
betweenmaximum/minimum
the average
all studiedroutes.
difference
betweenthe maximum/minimum
datadata
and andthe averagedata dataof allofstudiedroutes.

Figure 5. ∆CO2 increment and tram occupancy as (a) a function of time, and as (b) a function of tram
Figure5.5.∆CO∆CO
2 increment and tram occupancy as (a) a function of time, and as (b) a function of tram
Figuretram occupancy
as (a) a function
time,and as (b) a function
ofand
tram
occupancy.
Theerror bars andcorrespondto the difference
betweenof
themaximum/minimumdata
2 increment
occupancy.
The
error
bars
correspondto the difference between the maximum/minimum data andthe averageThe
dataerrorof allbars
the studiedroutes.
occupancy.
correspondto the difference between the maximum/minimum data andthe average data of all the studied routes.
the average data of all the studied routes.

3.2. CO2 Levels Distribution Is Similar at Different Points Inside the Tram
∆CO2 dispersion measurements at the different points of the Tram were assessed using
the records from each sensor, as shown in Figure 6. Passenger occupancy is rarely uniform
along the Tram, and differences in the capacity distribution can lead to spatially disparate
values. The average Relative Standard Deviation (RSD) was determined to determine thehomogeneity of the CO2 distribution in the Tram. The RSD of 0.09 ± 0.02 suggested thatthe measurements were relatively homogeneous, although accumulation tendencies are
typically observed in the central area of the Tram.

174

ing the records from each sensor, as shown in Figure 6. Passenger occupancy is rarely
uniform along the Tram, and differences in the capacity distribution can lead to spatiallydisparate values. The average Relative Standard Deviation (RSD) was determined to determine the homogeneity of the CO2 distribution in the Tram. The RSD of 0.09 ± 0.02 suggested that the measurements were relatively homogeneous, although accumulation
Int. J. Environ. Res. Public Health 2022, 19, 6605
tendencies are typically observed in the central area of the Tram.

Figure 6. Distribution of ∆CO2 in the Tram (z-axis) as a function of time (x-axis) and ∆CO2 measures
Figure
6. Distribution∆CO2 inthe#32–#33,
Tram (z-axis)
a functionof time (x-axis)
2 measures(y-axis)on routes #2,of#4-#25,
andwhere asthere
is homogeneity
in the andCO2 ∆COmeasurement(y-axis)onroutes#2,#4–#25,and#32–#33,
where
there
is
homogeneity
in
the
CO
measurement
2along the tram and a strong relationship with occupancy.
along the tram and a strong relationship with occupancy.

3.3. Improving the Air Renewal by the Closing of the Air Return3.3. Improving the Air Renewal by the Closing of the Air Return
To study the influence of the return of air from inside the Tram to the air conditioning
To study theof theof air frominside Sensors
the Tramthe#7,
air locatedconditioning
equipment,
we influence
worked withthereturndata obtained
through#3toandjustequipment,
weworked
with
the
data
obtainedthroughSensors#3
and#7,
locatedjustbelow the grilles of the air return ducts. Days C and D were selected as a reference
for thebelow
thegrillesofsimilarity
the air returnducts.
Days C and variables.
D were selectedas2avaries
reference
the
study
due
to thebetweenmeteorologicalThe ∆COwhenforthestudy
due toisthesimilarity
betweenmeteorologicalvariables.
∆COout,when the2 varies
air returnclosed,as deducedin Figure
7. From theanalysesThecarriedthe extreme
airvalues
returnatistheclosed,as deduced
7. corresponding
From the analyses
out, thebeginningand endinofFigure
the routeto thecarriedaccumulation
ofextreme
gas invaluesatthebeginning
and
end
of
the
route
corresponding
totheaccumulationof gas in
the Tram have been removed, offering a more realistic view of the internal atmospheretheduring
Tram thehave
been
removed,offering athemorerealistic
view of theatmosphere
tour.
Under
these conditions,average
ppm/person
rate internal
without returnwasduring
the
tour.
Under
these
conditions,theaverage
ppm/person
rate
without
returnwas
3.5 ± 0.1 (Sensor #3) and 5.1 ± 0.1 ppm (Sensor #7) without air return, and 4.9 ± 0.7(Sensor
3.5±
0.1(Sensor
#3)
and
5.1
±
0.1
ppm
(Sensor#7)
withoutair
return,
and
4.9
±
0.7
(Sensor#3)6605
and 6.0 ± 0.4 (Sensor #7) with air return. A reduction in ∆CO2 between 9% and 10
36%
canInt. J. Environ. Res. Public Health 2022, 19,of 20#3)beand
6.0concerning± 0.4 (Sensor#7) with
return. A reduction in ∆CO2 between 9% and 36%
seen
air return∆COair2 levels.
can be seen concerning air return ∆CO2 levels.

Figure7.7.Registeredvaluesdepending
dependingreturn.
FigureRegisteredppm/person
ppm/person values
onon
thetheair air
return.

3.4. Wind Speed Contributes to Increasing Ventilation Rates
The days were divided 175
depending on the wind speed into Set A (Day A and E) and
Set B (Day B to D). Sets were made to assess whether the weather plays a crucial role inthe ventilation. The objective of this section is to compare the ventilation pattern on days

Int. J. Environ. Res. Public Health 2022, 19, 6605

3.4. Wind Speed Contributes to Increasing Ventilation Rates
The days were divided depending on the wind speed into Set A (Day A and E) and
Set B (Day B to D). Sets were made to assess whether the weather plays a crucial rolein the ventilation. The objective of this section is to compare the ventilation pattern ondays with different weather, with special attention to the average wind and gusts andthe average temperature. The average weather conditions of interest for the days of each
Set are shown in Table 3. In Set A, the average wind speed was 3.9 ± 1.1 m/s, while, in
Set B, it was 1.8 ± 0.1 m/s, with maximum gusts of 10.0 ± 1.6 m/s and 5.6 ± 0.6 m/s,
respectively. Figure 8a,b represent the ppm/person index for Set A and Set B, respectively.
In addition, 88.4% of the ppm/person indices was higher in Set B than Set A. It was foundthat the means of the data from Set B were significantly lower than those from Set A
using a hypothesis contrast (CI99; −3.26–−1.38). Limits are harmful in the CI, confirmingthat higher data on Set A. A Student’s t-test shows that the average ∆CO2 concentration
increases on days with lower wind speeds are higher. However, the difference between the
ppm/person index in Set B compared to Set A is 2.3 ± 3.3 ppm, compared to averages of
5.2 ± 3.7 ppm (Set A) and 7.5 ± 3.0 ppm (Set B), which represents a reduction of between
31 and 44%. These data suggest that the weather can substantially affect the recirculationof air inside the Tram, as shown in Figure 8.
Table 3. Meteorological variables of the reference Sets A and B. Information prepared by the Agencia
Estatal de Meteorología of Spain (data collected at the Valdespartera Station, Zaragoza Spain, 23
December 2020).
Day

Taver

Tmin

Tmax

Dw

Ws,averWs,max

Set A
Set B

9.6 ± 0.5 ◦ C

6.1 ◦ C13.1 ◦ C

30 ± 0.5◦

3.9 ± 1.1 m/s
1.8 ± 0.1 m/s

11.1 m/s
6.1 m/s

7.2 ± 1.5 ◦ C

Int. J. Environ. Res. Public Health 2022,
T 19,: 6605
temperature (average); T

3.3 ◦ C12.0 ◦ C

14 ±3.5◦

11 of 20aver
min : temperature (minimum); Tmax : temperature (maximum); DW : wind direction;
Ws,aver : wind speed (average) and; Ws,max : wind speed (maximum).

Figure8.8. ∆CO∆CO2 perperson ratio (ppm/person) in (a) Set A, and in (b) Set B depending on time; (c)Figure2 per person ratio (ppm/person) in (a) Set A, and in (b) Set B depending on time;difference between Set B and Set A depending on time.
(c) difference between Set B and Set A depending on time.

3.5. Tram Speed Does Not Influence the Indoor Ventilation Rate
Tram speed while circulating did not seem to have a substantial effect on the reduc176
tion of ∆CO2 (Figure 9a), nor on the average reduction rate of ∆CO2 at 3 min (Figure 9b).
The Student’s t-test showed no significant relationship between the rate of reduction of∆CO2 compared to two different speed ranges: 1–20 km/h and 21–40 km/h. It may be due

Int. J. Environ. Res. Public Health 2022, 19,6605 8. ∆CO2 per person ratio (ppm/person) in (a) Set A, and in (b) Set B depending on time; (c)Figuredifference between Set B and Set A depending on time.

3.5.Tram
TramSpeed
SpeedDoes
DoesNot
NotInfluence
Influencethe
theIndoor
Indoor Ventilation
Ventilation Rate
Rate
3.5.
Tramspeed
speedwhile
whilecirculating
circulatingdid
didnotnotseemseem
have
a substantial
effectreducTramtoto
havea substantial
effectonon
thethereduction
2 (Figure 9a), nor on the average reduction rate of ∆CO2 at 3 min (Figure 9b).
tion
of
∆CO
of ∆CO2 (Figure 9a), nor on the average reduction rate of ∆CO2 at 3 min (Figure 9b). The
The Student’s
showedno significant
relationshipbetween
theofrateof reductionofStudent’s
t-test t-test
showedno significant
relationshipbetweenthe rate
reductionof ∆CO
2∆CO
2 compared to two different speed ranges: 1–20 km/h and 21–40 km/h. It may be due
compared to two different speed ranges: 1–20 km/h and 21–40 km/h. It may be due to theto theof the system,
HVAC system,
which generates
so thattheof
inflowof airflow
of flow
the HVAC
which generates
internalinternal
drafts sodrafts
that theinflow
air throughthroughthewindow
does
not
alter
the
ventilation
rates
substantially.
the window does not alter the ventilation rates substantially.

Int. J. Environ. Res. Public Health 2022, 19, 6605

12 of 20

Figure9.9.CO
CO22 measurements
measurements depending
depending on
on Tram
Tram speed.
speed. (a) ∆CO∆CO22 perFigureperperson
personratio,
ratio,andand(b)(b)average
averagereductionrate
rateofof∆CO∆CO22 depending
depending on the Tram speed.
reduction

3.6.
SystemIs NotEfficientagainstSubmicron
Matter
3.6.TheTheFiltration
Filtration
SystemIs NotEfficientagainstSubmicron
Matter
The tests have been carried out with the filter usually installed on the Tram (Coarse
The tests have been carried out with the filter usually installed on the Tram (Coarse
75% Filter Media). However, the Coarse 75% filter specially implemented due to the cur75% Filter Media). However, the Coarse 75% filter specially implemented due to the current
rent COVID-19 pandemic has been characterized in the laboratory. The Air Changes per
COVID-19 pandemic has been characterized in the laboratory. The Air Changes per Hour
Hour (ACH) of the Zaragoza Tram remained in the unit’s regular operation at 25 ACH.
(ACH) of the Zaragoza Tram remained in the unit’s regular operation at 25 ACH. As shown
As shown in Figure 10, the Coarse 75% filter presented an approximate retention effiin Figure 10, the Coarse 75% filter presented an approximate retention efficiency of 27.9%
ciency of 27.9% for 300 nm particles at a flow rate3of ~2500 m3/h. The filtering efficiency
for 300 nm particles at a flow rate of ~2500 m /h. The filtering efficiency decreases up
decreases up to 2.4 and 2.3% using ~162 and 3~622 m3/h, respectively. The largest particles
to 2.4 and 2.3% using ~162 and ~622 m /h, respectively. The largest particles present
present more inertia at high flow rates [67,68], resulting in a higher retention rate in themoreinertia Evenat highflowtherates[67,68],resultingin a observed
higher retention
rate
intests
the filter
filtermedium.
thoughcloggingof NaCl
particlesin the headloss
medium.
Even thoughthese
the clogging
of NaCl
particlesin thetests maymay
have overestimatedresults, which
could
be seenobserved
as an increase
upheadto 440loss
Pascalshaveoverestimatedtheseresults,
which
could
be
seen
as
an
increaseup
to440
Pascalsof pressure drop (Table 4), this flow would be the most representative of the workingofpressuredrop(Table
4),
this
flow
would
be
the
most
representativeof
theworking
conditions in the HVAC of the Tram system.
conditions in the HVAC of the Tram system.

Figure10.
10.Coarse
Coarse75%
filterretention
efficiency
dependingonparticle
the particle
diameterat different
Figure75%
filterretention
efficiency
dependingon thediameterat different
speeds(flow(flowrates).
speedsrates).
Table 4. Conditions used in the filtration tests and pressure drop determination.

Area
2281.6 cm2
2

177
Flow Rate~161.8 m3/h
3Velocity in Filter
19.4 cm/s

Pressure Drop
6 Pa

Int. J. Environ. Res. Public Health 2022, 19, 6605

Table 4. Conditions used in the filtration tests and pressure drop determination.
Area

Flow Rate

Velocity in Filter

Pressure Drop

2281.6 cm2~161.8 m3 /h

19.4 cm/s
75.7 cm/s
303.0 cm/s

6 Pa
34 Pa
440 Pa2281.6 cm2
2281.6 cm2~621.8 m3 /h~2488.8 m3 /h

3.7. Probability of Infection
The probability of infection and the attack rate were calculated following the models
proposed by Peng and Jiménez [22] and Aireamos [31]. Based on the average daily CO2
values collected in Tables S1 and S2, an attack rate of 0.06% was determined in the least
favorable case (higher CO2 values) and an attack rate of 0.04% in the average case (global average CO2 ). According to Peng et Jiménez’s proposal, the probability of contagion <0.01% is
acceptable, so the Tramway did not represent a high risk of contagion under the conditions
studied, as shown in Table 5.
Table 5. Determination of the infection probability and attack rate by aerosols in two different
scenarios simulates the Tram’s ventilation conditions, using Covid Risk Airborne [31] based on themodel Wells–Riley [64].
Scenario

Facemask

Occupation

Exposure Time#1(global average)

Surgical mask

60 pax10 min#2(maximumaverage)

Surgical mask

60 pax10 minCO2 Level689 ppm(average)/1038 ppm(max)
810 ppm(average)/1520 ppm(max)

Variantp

Attack Rate

Omicron

0.01% *

0.04% *

Omicron

0.01% *

0.06% ** Considering a 78% vaccination with a proportionate immunity of 70%; a cumulative incidence (CI) of 1150 to
14 days/100,000 hab.

4. Discussion
SARS-CoV-2 bioaerosols dissemination in infected individuals’ exhalation is widely
demonstrated [69–71]. In addition, the virus’s presence and persistence in the environmental air have also been extensively studied [10,11,72–78]. Given the apparent predominanceof the airborne route of transmission of COVID-19, various strategies have been investigated to mitigate the risk of contagion. Public transport environments represent the second
sector with the highest transmission of SARS-CoV-2, only behind the health sector [57].
However, computational studies point to a 1.5–1.6% attack rate [79,80]. Even though numerous works have been aimed at evaluating the behavior of bioaerosols in collective
transport by computational fluid dynamics [81–85], extrapolation to actual conditions is
an enormous limitation. One of the strategies that allow the indoor ventilation rate to bequantified in situ is the measurement of CO2 , which has positioned itself as a standard for
air control [22,86,87].
In this work, CO2 measurements were collected in 88 round trips, which is equivalent
to more than 79,200 records obtained from eight sensors strategically distributed in the Tram.
The distribution of CO2 throughout the vehicle follows a similar trend (RSD 0.09 ± 0.02),
so the location of the HVAC systems and natural air intakes seem to favor all points of
the Tram equally. The average absolute CO2 of all the routes studied was 685 ± 59 ppm(572 ± 75 ppm–835 ± 232 ppm). This value suggests that the percentage of air alreadybreathed is <0.7%. Considering the virus emission rates in exhaled breath [71,88], averagetime spent in the Tram (~7 min), and the mandatory use of facemasks, the interior of thevehicle does not represent a risk space of contagion by aerosols (probability of infection [22],p = 0.01%; attack rate < 0.1%) in the most unfavorable scenario (844 ppm average; 1571 ppm
maximum). In this sense, Moreno et al. [78] reported attack rates between 0.00–0.72% inbuses depending on the respiratory activity, bus air conditions, and the infected individual
without a mask. Thus, the environment of the bus at that time was more dangerous.
178

Int. J. Environ. Res. Public Health 2022, 19, 6605

Before the pandemic, some reports pointed to metabolic CO2 concentrations of up to3700 ppm in buses [58–61]. However, a study on the bus in Barcelona (Spain) points toconcentrations close to 1000 ppm that can be easily reduced to <800 ppm by implementing
simple ventilation measures (i.e., opening windows) [45]. The operating subways conditions require a reinforcement of artificial ventilation, for which values close to 1000 ppm
have been found [62,63].
In this paper, we propose using ppm/person indicator as a measure that allows ∆CO2
levels comparison on different days and circumstances. A key aspect and an obvious one
is the increase in ∆CO2 as the number of passengers increases. Analyzing the ∆CO2 datameasurements, a gradual increase in CO2 concentration could be misinterpreted as anaccumulation. However, looking at the ppm/person ratio, it can be seen that the increasein ∆CO2 comes from an increase in capacity.
Trams are typically similar to buses since they circulate outside and substantially favor
measures to reinforce natural ventilation. In this work, it was found that the speed of theexternal wind reduced the ppm/person rates to around 2.3 ± 3.3 ppm. Although it seemsa slight benefit, it represents a reduction of between 31 and 44% compared to days withless wind. Closing the air return (total external air intake) favored ventilation, reducing the∆CO2 level between 9 and 36%. Tram speed did not affect ventilation rate, at least in two
data sets with different speed ranges (2–20 km/h versus 20–40 km/h). However, the data
could not be compared with the stopped Tram since the conditions were different at thatmoment. There are no sources of CO2 generation (there are no passengers), and the doors
open entirely, so the air is wholly recirculated in a few minutes.
Favoring natural ventilation (opening windows), the HVAC system, and the use ofmasks have been shown to significantly reduce the risk of transmission [30,79,84,89,90].
Masks reduce the bioaerosols emission variably, depending on the type of mask and theaerodynamics of the scattered particles [91–94]. In addition, HVAC systems should consider
the filter, but it is also possible to optimize it to maintain adverse thermodynamic conditions
for the virus [95,96].
One of the most significant limitations of CO2 measurement is that its interpretation
cannot be generalized but must be individualized. Aerosol generation fluctuates substantially depending on the individual’s respiratory event [2,33,97–99]. In addition, environmental conditions directly influence the spread and persistence of the virus [2,100,101].
Therefore, it is not easy to define an effective viral load dependent on CO2 , at least in
absolute terms. However, this and other studies demonstrate the effectiveness of CO2 measurement to implement effective air renewal patterns and reduce the risk of transmissionof infectious diseases.
5. Conclusions and Recommendations
This work suggests that the measurement of the ∆CO2 concentration inside collective
transport constitutes a cost-efficiency strategy that can reduce the rates of spread of therespiratory virus by aerosols, as is the case of the SARS-CoV-2 virus. In this work, theinterpretation of the exhaled CO2 levels per person (ppm/person) has made it possible
to analyze the behavior of the air inside the Zaragoza Tram. Maintaining the typicalparameters of the HVAC units and implementing the partial opening of the windows,
the maximum CO2 level was 1249 ppm. On average, 835 ± 232 ppm have not been
exceeded on any of the routes studied, which indicates that air recirculation is adequate
for vehicle occupancy. In addition, the absolute CO2 in all the routes studied was 685 and690 ± 59 ppm, on average and median, respectively. However, it must be considered thatcapacity was reduced on the studied days due to the COVID-19 pandemic restrictions. It
presents a limitation when extrapolating the data to post-pandemic operating conditions.
The passengers’ exposure to the Tram air must also be considered since the average route
usually lasts around 7 min, and passengers wear a mask and keep their distance whenpossible. Under the conditions studied, the following recommendations are suggested toreduce the risk of infection by aerosols and/or improve ventilation performance:

179Int. J. Environ. Res. Public Health 2022, 19, 6605

•
•
•

•

Maximize outside air intake: by opening windows, increasing door openings instations, and minimizing the rate of return air in HVAC units;
Completely recirculate the air between outbound and return routes to avoid exposing
new passengers to the air breathed by previous passengers;
Consider implementing efficient filtration systems against particles (0.1–100 µm) instead of coarse-type filters, efficient against pollen or dust. Additionally to filtration
systems, other air purification technologies can be beneficial in improving air quality.
Even so, its performance needs to be demonstrated under operating conditions andnot just in the laboratory or theoretically;
Limit the respiratory activity of passengers to calm breathing and speech and the use ofmasks and other personal protection equipment and promote interpersonal distance.

In addition, from experience gathered during the CO2 measurement experiments inpublic transport, the following recommendations can be drawn:

•
•

•
•

•
•
•

Initially, characterizing the distribution of CO2 inside the vehicle is essential so that thelocation of the sensors allows representative measurements of the space to be taken;
Analyzing the increase in CO2 instead of absolute CO2 allows for quantifying only
the CO2 generated by passengers, discriminating external pollution. Additionally, we
propose to use the ppm/person ratio as the main indicator to compare the exhaled CO2
measurements on different scenarios. This ratio can be easily calculated by dividing
the increase by the number of people. For example, if the increase in CO2 is 500
and there are 50 people, the ratio will be 10 ppm/person. In case of studying two
separate days, for example with different weather, we can find that one day the ratiois 10 ppm/person and another day it is 30 ppm/person. With this information, we
can determine how the change of variables affects independently of the occupation.
Place the gauges at a sufficient height to avoid the direct exhalation of the passengers.
For example, they were placed 2.25 m above ground level for this work. Moreover,locating meters near doors and windows should avoid underestimating CO2 levels.
Evaluate weather conditions, especially airspeed, to interpret the measurement results
on measurement days correctly. For example, in our study, the weather substantiallyaffected the ventilation ratio inside the Tram. On the days with the greatest wind,
ppm/person rates of up to 44% lower were recorded with respect to the days with theleast wind.
Recording occupancy levels (number of passengers) is essential to estimate the ventilation rate and to be able to compare data in different samples.
Deduct the minimum number of meters to obtain representative measurements of thespace. The heterogeneity in vehicle occupancy requires a consistent distribution ofmeters. For example, a meter was placed for every 35 m3 of air in this work.
Considering the respiratory activity of the vehicle occupants is desirable when normalizing the ppm/person rates. In addition, the CO2 records must be individuallyinterpreted depending on variables such as interpersonal distance, the use of masks orother PPE, and the implemented filtration systems (or other air purification devices).

Under the conditions studied, the Tram does not present itself as a space with ahigh risk of infection by aerosols (by using Aireamos Covid Risk Airborne tool [31]; see
Section 3.7). Air quality monitoring began to gain popularity due to the COVID-19 pandemic. However, once the focus is on the air [2], a post-pandemic scenario presents
uncertainty when the windows are completely closed and the capacity increases. Consequently, it will be necessary to implement a standard that allows air quality to be regulated
in these post-pandemic conditions. The poor filtration performance against the submicronicmatter of the typically implemented filters is a significant limitation. It is necessary to find
new air control and purification strategies that reduce the risk of disease transmission in
the future.

180

Int. J. Environ. Res. Public Health 2022, 19, 6605

Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph19116605/s1, Table S1: Weekend journeys data analysis(from Friday evening to Sunday), and Table S2: Weekday journeys data analysis (from Monday to
Friday evening).
Author Contributions: Conceptualization, M.B., J.J.A. and A.J.S.; Data curation, M.B.; Formal analysis, M.B., J.J.A. and A.J.S.; Investigation, M.B. and J.J.A.; Methodology, M.B. and A.J.S.; Project
administration, J.J.A. and A.J.S.; Resources, J.J.A.; Supervision, A.J.S.; Writing—original draft, M.B.,
J.J.A. and A.J.S. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the Instituto de Investigación Sanitaria Aragón: Campaña
Investiga COVID-19 (CoviBlock).
Acknowledgments: We would like to express our gratitude to “Los Tranvías de Zaragoza S.E.M.”
and “CAF Spain S.A.” for supporting this research and its invaluable collaboration. We also would
like to thank “Ayuntamiento de Zaragoza” and “Institute for Health Research Aragón” for thereceived support.
Conflicts of Interest: The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported in this paper.

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Xie, X.; et al. Characterization of Expiration Air Jets and Droplet Size Distributions Immediately at the Mouth Opening. J. Aerosol
Sci. 2009, 40, 122–133. [CrossRef]
98. Shao, S.; Zhou, D.; He, R.; Li, J.; Zou, S.; Mallery, K.; Kumar, S.; Yang, S.; Hong, J. Risk Assessment of Airborne Transmission of
COVID-19 by Asymptomatic Individuals under Different Practical Settings. J. Aerosol Sci. 2021, 151, 105661. [CrossRef]
99. Fabian, P.; Brain, J.; Houseman, E.; Gern, J.; Milton, D. Origin of Exhaled Breath Particles from Healthy and Human RhinovirusInfected Subjects. J. Aerosol Med. Pulm. Drug Deliv. 2011, 24, 137–147. [CrossRef]
100. Hinds, W. Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles; John Wiley & Sons, Inc.: Hoboken, NJ,
USA, 1999.
101. Van Doremalen, N.; Bushmaker, T.; Morris, D.; Holbrook, M.; Gamble, A.; Williamson, B.; Munster, V. Aerosol and Surface
Stability of SARS-CoV-2 as Compared with SARS-CoV-1. N. Engl. J. Med. 2020, 382, 1177–1179. [CrossRef]

185

International Journal ofEnvironmental Researchand Public Health
ArticleEnvironmental Effects of the COVID-19 Pandemic:
The Experience of Bogotá, 2020
Jeadran Malagón-Rojas 1,2, * , Daniela Mendez-Molano 1 , Julia Almentero 1 , Yesith G. Toloza-Pérez 1 ,Eliana L. Parra-Barrera 1 and Claudia P. Gómez-Rendón 2
1
2*

Instituto Nacional de Salud, Bogotá 111321, Colombia; damemo27@gmail.com (D.M.-M.);
jalmentero@ins.gov.co (J.A.); ytoloza@ins.gov.co (Y.G.T.-P.); elipabarrera@yahoo.es (E.L.P.-B.)
Doctorado en Salud Pública, El Bosque University, Bogotá 110121, Colombia;
gomezclaudiap@unbosque.edu.co
Correspondence: jnmalagon@unbosque.edu.co; Tel.: +57-1-2207700

Abstract: During the novel coronavirus disease (COVID-19) pandemic, several environmental factors
have influenced activities and protection policy measures in cities. This has had a major effect on
climate change and global environmental catastrophe. In many countries, the strategy of closing
various activities such as tourism and industrial production stopped normal life, transportation, etc.
This closure has a positive impact on the environment. However, the massive use of masks andpersonal protection could significantly increase pollution worldwide. The impact on the environment
needs to be calculated to have information for public health actions. In this study, we present a first
overview of the potential impacts of COVID-19 on some environmental matrices in Bogotá, Colombia.
Citation: Malagón-Rojas, J.;

Keywords: COVID-19; SARS-CoV-2; global environment; solid waste; air quality; transport

Mendez-Molano, D.; Almentero, J.;
Toloza-Pérez, Y.G.; Parra-Barrera,
E.L.; Gómez-Rendón, C.P.
Environmental Effects of the

1. Introduction

COVID-19 Pandemic: The Experience

The worldwide spread of severe acute respiratory syndrome (SARS-CoV-2) has had a
relevant impact on economies, societies, road traffic, tourism, and human interaction [1].
Since COVID-19 was declared a pandemic disease by the WHO on 11 March 2020, strategies
have been focused on individual lifestyles protection, suggesting individual isolation,
confinement, and introducing behavioral practices such as regular hand washing, and theuse of a face mask to help prevent viral transmission, particularly in public spaces [2].
The adoption of measures for preventing COVID-19 transmission has impacted theenvironment in many ways. Some authors have postulated that the COVID-19 pandemic
is an opportunity to “reset” the current practices that are putting in check the planetary
sustainability [3–5]. In this vein, a better understanding of pro-environmental behaviors
and practices that took part during the global lockdown may contribute to improving
health globally [6].
In addition, it seems that the imposed restrictions on mobility have drastically modified patterns and personal behaviors toward energy consumption [7–9]. Additionally, the
COVID-19 pandemic decimated tourism and business travel, as well as labor migration [10],and modified the patterns of city mobility, increasing the usage of individual transportmodes and behaviors toward bike and car usage [11–13]. In addition, pandemic restrictions
have positively impacted indicators of air and water quality and noise pollution [14], andenvironmental noise has been reduced by 75%. This may be associated with a notabledecrease in vehicle traffic, social mobility, international trips, and the temporary closureof factories [15]. In addition, some authors have stated that countries in which a social
confinement strategy was applied to stop the spread of coronavirus infection showed anotable decrease in pollution and the emission of greenhouse gases [16–18]. This coincides with reports of a significant reduction in carbon emissions in highly industrializedcountries [19]. Nevertheless, other authors have claimed that the potential benefits ofof Bogotá, 2020. Int. J. Environ. Res.
Public Health 2022, 19, 6350.
https://doi.org/10.3390/
ijerph19106350
Academic Editors: Sachiko Kodera
and Essam A. Rashed
Received: 24 March 2022
Accepted: 4 May 2022
Published: 23 May 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

187

Int. J. Environ. Res. Public Health 2022, 19, 6350

the quarantine period have been eclipsed by lifestyles based on the increase in the useof single-use products, such as personal protective equipment, thwarting efforts toward
reducing plastic pollution [20–22].
Some reports have indicated that the production of solid waste has increased fourtimes [14,23]. Nevertheless, there is still not enough evidence of the effects on the generationof solid waste and the generation of environmental and air pollutants during the current
pandemic. It is necessary to consider the negative impacts of the pandemic in terms ofglobal warming and hospital and general waste to formulate policies regarding waste
management and environmental pollution [24]. Understanding how the lockdown andsocial distancing measures have impacted the environment may inspire ideas supporting
the goals of achieving more sustainable cities and communities in 2030 [25]. Therefore,
this study is intended to expose and evaluate the environmental impact of the COVID-19
pandemic in 2020 in Bogotá, Colombia.
2. Materials and Methods
2.1. Study Area
Bogotá, the capital of Colombia, was selected as the study area. By May 2021, the city
had more than 800 thousand infections since the beginning of the pandemic [26]. Bogotá
has approximately 7.2 million inhabitants. The city is located at an average of 2625 m above
sea level and is in the center of Colombia. It has a length of 33 km from south to northand 16 km from east to west (Figure 1). The economy is mainly based on the service sector,
commerce, manufacturing, and construction. Additionally, it is one of the most polluted
cities in Latin America. Pollution is mainly derived from diesel fuel, natural gas, industrial
pollution, and the destruction of forests [27]. By 2019, 2.4 million vehicles were circulatingin Bogotá, among which 50% were automobiles, 20% were motorcycles, 14% were vans,and 5% were public transport vehicles [28].
During the first year of the pandemic, Colombia reported almost 1.6 million confirmed
cases and roughly 42,000 deaths related to COVID-19 [29]. The national lockdown wasestablished between 25 March and 15 April, followed by a slow increase in mobility until
August 2020 [29]. Since September 2020, COVID-19 mitigation strategies were fundamentally based on promoting remote work, the usage of personal protective equipment,
and preventing social contacts, such as closing schools and universities. Additionally, theMinistry of health’s guidance included social distancing, case isolation, and shielding tolimit community-level transmission of SARS-CoV-2 and protect vulnerable groups.
In the specific case of Bogotá, community mobility was restricted based on the rates
of occupancy of intensive care units (ICUs), through the strategy “pico y cédula”, whichallowed mobility in the city and access to banks, supermarkets, and public transport basedon the ending number of ID cards [30].
Similar to what has been observed in other scenarios, the SARS-CoV-2 epidemic in
Colombia and Bogotá has been highly heterogeneous spatially. While some municipalities
experienced explosive early spikes, followed by periods of very low transmission despite
the near absence of NPI, many others experienced several moderate spikes interspersedwith plateaus of sustained transmission [29,31].
2.2. Study Design
A retrospective ecological study was designed. The observed period was January 2019
to June 2021. To select the sources of information, we considered a matrix of environmental
factors that may be affected by human activities [32]. In Supplementary Table S1, theenvironmental factors included in the study are listed, which were considered based on
the availability and quality of the data and the relevance and strategic importance of theprovided information.

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Int. J. Environ. Res. Public Health 2022, 19, 6350

Int. J. Environ. Res. Public Health 2022, 19, x

3 of 12Figure 1. (a) SARS-CoV-2 cases in Colombia from 6 March 2020 until 18 May 2021; (b) study area,

Figure1. (a)COVID-19
SARS-CoV-2
in Colombia
from
6 March2020
until
18 May2021;related(b) study
showingcasescases
in Bogotá
until 18 May
2021.
The sizeof thecirclesis directly
to area,showing
COVID-19
cases insince
Bogotá
2021. TheThesizegeographicalof the circlesinformation
is directly system
related to thethe numberof infections
theuntilfirst 18
dayMayof reporting.
ArcGISfor Desktop
10.5.0.6491
was used toThegenerate
Figure 1. The
Colombiansystemadmin-ArcGIS
numberof 10.5
infections
sinceversion
the first
day of reporting.
geographicalinformation
istrative
divisionsandothergeographic
layerswere
downloadedfromhttps://www.da10.5 for Desktop version 10.5.0.6491 was used to generate Figure 1. The Colombian administrative
tos.gov.co/browse?sortBy=newest&utf8=%E2%9C%93 (accessed on 15 March 2022). COVID-19 data
divisionsand othergeographic
layersdownloaded
from2020
https://www.datos.gov.co/browse?
were obtained
from
Health Ministerin wereColombia,from 6 March
to 18 May 2021.
sortBy=newest&utf8=%E2%9C%93 (accessed on 15 March 2022). COVID-19 data were obtained from
Healthin Colombia, from 6 March 2020 to 18 May 2021.
2.2. Minister
Study Design
A retrospective ecological study was designed. The observed period was January
2019 to June 2021. To select the sources of information, we considered a matrix of enviTo analyzechangesin thegenerationof solid
waste [32].
in Bogotá,
two sets ofTable
data wereronmental
factorsthat may
be affectedby humanactivities
In Supplementary
S1, the environmental
factors included
in solid
the study
aredisposed
listed, which
consideredanalyzed.
The first set corresponds
to thewasteof inwerethe capital’ssanitary
basedForon thisthe analysis,
availability
and qualityof thedatacomparing
and the relevanceand strategic
landfill.
a timeseries was
made
its generationin 2019imwith thatportance
provided
information.
in 2020.
For ofthetheseconddataset,the hospital waste in the biosanitary category obtained from

2.3. Solid Waste Data Analysis

the Special Administrative Unit of Public Services (UAESPs) was analyzed. In this study,
2.3. Solid Waste Data Analysis
the generation of solid waste in kg/month by large producers (LPs), medium producers
To analyzechangesin theandgenerationof solid waste
in A
Bogotá,setsB ofdata andweretype C(MPs), small
producers(PPs),
micro-producers
type(MA),twotype(MB),
analyzed. The first set corresponds to the solid waste disposed of in the capital’s sanitary(MC) was determined. We included information from all companies (n = 12) who are inlandfill. For this analysis, a time series was made comparing its generation in 2019 withcharge of solid waste collection in the city (Supplementary Table S2).
that in 2020. For the second dataset, the hospital waste in the biosanitary category obtained from the Special Administrative Unit of Public Services (UAESPs) was analyzed.
2.4. Air Quality
In this study, the generation of solid waste in kg/month by large producers (LPs), medium
Secondary
data
from
the Bogotá
Monitoring(RMCAB)
producers(MPs),
small
producers(PPs),Air
andQuality
micro-producers
type Network
A (MA), type
B (MB), wereusedtotypeanalyzechangesin the concentration
criteria pollutants
during theandC (MC)
was determined.
We includedof
informationfrom all companies(n =pandemic
12)
who arein charge of asolid
waste collectionin the(Supplementary
Table pollutants
S2).
period.
Additionally,time-series
analysis
wascity
performed
for criteria
carbonmonoxide (CO), nitrogen dioxide (NO2 ), and particulate matter less than 10 microns indiameter (PM10 ) and less than 2.5 microns in diameter (PM2.5 ). Information from 11 stations
operating during the 2 years was used.
Additionally, we included data from the Air Contamination And Health Effects in Microenvironments in Bogotá (ITHACA) study related to questions regarding the perceptionof air quality of people on their way to work or study and the personal protective elements
they use to travel [33,34]. In this study, we used the data from 1821 citizens (more details

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Int. J. Environ. Res. Public Health 2022, 19, 6350

are provided in Supplementary Table S3). The data were collected between February 2019
and June 2021.
2.5. Water Resources
Information on physical and chemical profiles of water for human consumption
reported by Bogotá Aqueduct Company was included. Values are reported for turbidity in
UNT, the concentration of manganese, organic matter, ammonium, dissolved oxygen in
mg/L, and conductivity in µS/cm. The values are averages and maximums recorded for2019 and 2020.
2.6. Transport
To analyze changes in transportation dynamics, the survey conducted in the ITHACA
project was used. The survey records participants’ answers to questions associated with
their transportation and air quality during the COVID-19 pandemic (n = 1821).
In addition, information on the number of trips in the city’s BRT system (Transmilenio
S.A.) was collected. These values report the number of entries in three modes of transportthat are part of the system. The first refers to BRT buses that run on the main lines of thecity, the second mode of transport is zonal buses that run in mixed lanes, and the thirdmode is dual buses.
2.7. Statistical Analysis
For quantitative variables, averages and standard deviations were estimated. For
categorical variables, frequencies were obtained. Location measures, such as quartiles,
were used for some variables.
A time-series visual analysis was performed for air quality and transport (urban
trips and air trips) by month and year. To establish differences between months andyears, a bivariate analysis was performed using a t-test or Mann–Whitney U test forcontinuous variables and Chi2 test for discrete variables, where p > 0.05 indicated asignificant difference. In addition, the data from 2020 were divided into four phases:(1) baseline (1 February 2019 to 24 March 2020); (2) strict national lockdown (25 March
to 26 April); (3) first relaxation (27 April to 31 May); and (4) gradual economic opening(from 1 June onwards) (Supplementary Figures).
The analysis was performed using R version 4.1 (Vienna, Austria) and Wolfram Mathematica version 12.0 (Champaign, IL, USA).
3. Results
3.1. Solid Waste
A decrease (33%) in disposed waste was observed in April 2020, compared with thatin 2019. Nevertheless, after performing a temporal series analysis, it was observed that
disposal quantities returned to their recurrent trend after the first measures of the pandemic
strategy were removed (Supplementary Figure S1).
It was found that large producers of biosanitary waste and micro-producers A and B
significantly increased their waste generation (Figure 2), with large producers presenting
an increase from 67,000 kg in March to 89,000 kg in August 2020 and micro-producers A
and B presenting a 100% increase in solid waste from March to July 2020.
For LPs, an increase in biosanitary waste generation was observed between April andAugust 2020. For MPs and PPs, a small increase was also observed, but this coincidedwith the economic reopening of the sites where these types of waste are produced, which
presented the largest figures for 2019. Concerning MA, MB, and MC generators, increases
were observed between April and August 2020, corresponding to normalization in thegeneration of biosanitary waste.
The Mann–Whitney U test showed significant differences between total waste production in 2019 and 2020 (p-value < 0.05). Nevertheless, the median amounts of biosanitary190

Int. J. Environ. Res. Public Health 2022, 19, x

5 of 12Int. J. Environ. Res. Public Health 2022, 19, 6350

It was found that large producers of biosanitary waste and micro-producers A and B
significantly increased their waste generation (Figure 2), with large producers presenting
waste
in 2019fromand 67,000
2020 werenotdifferent(p-value> August
0.05) (Supplementary
Table S4). Whenan increase
kg in
March
to 89,000
kg in2020 and micro-producers
A
comparing
only theLPs, large
differenceswere found(p value< 0.05).
and B presenting
a 100%
increasein solid wastefrom March
to July
2020.

Figure 2. Bio-sanitary waste disposal series at the Bogotá sanitary landfill. The first panel shows
biosanitaryresidues forwaste
the year2020 series
for large
producers(LPs), thesecondThepanel
Figure 2. Bio-sanitary
disposalat theBogotá sanitary
landfill.
firstshows
panel medium
shows
producers(MPs)
and smallandproducers
the third panel
micro-producers
B, and C
biosanitaryresidues
for theproducers
year 2020(PPs),
for large(LPs),shows
the second
panel showsA,
mediuminproducers
red, orange
andandyellow,
respectively.
(MPs)
small
producers (PPs), and the third panel shows micro-producers A, B, andC in red, orange and yellow, respectively.

3.2. Air Quality and Citizens’ Perception
For LPs, antheincreasein biosanitary
waste
generation
was observed
between
April andRegardingdata provided
by theBogotá
Air Quality
Monitoring
Network,
there
August2020. For
MPs andPPs, a small
increase
was monoxide
also observed,
but
this coincided
wasa decreasein criteria
pollutants,such
as carbon(CO),nitrogenoxides with
(NO2 ),
the economic
reopeningof thewheretypes (PMof wasteare produced,
which matter
preparticulatematterless than
10sitesmicrons
inthesediameterfine particulate10 ), andsentedthelargestfiguresfor2019.
Concerning
MA,
MB,
and
MC
generators,
increases
(PM
).
For
the
strictquarantine
stage,
therewas
anaveragereduction
of
~51%
for
CO
and
2.5
wereforobserved
betweenApril
and reductions
August 2020,
corresponding
to normalizationin the~61%
NO2 (Figure3a,b).
Thesewere
due to the decreasein transportation
generationof biosanitary
waste. fixed emissions.
andhalt of activities
that produce
The
Mann–Whitney
test showedsignificant
differences
between total
waste
Regarding particulateUmatter
concentrations,
there
was a reductionof ~36%
forproPM10
duction
in
2019
and
2020
(p-value<0.05).
Nevertheless,themedianamountsofbiosaniand ~19% for PM2.5 (Figure 3c,d). It is important to highlight that the strict lockdown began
waste
in 2019
were notdifferent(p-value >conditions
0.05) (Supplementary
Table S4).
attarya timewhen
the andcity 2020
presented
criticalair pollution
due to the atmosphericWhen
comparing
only
the
LPs,
large
differences
were
found
(p
value
<
0.05).
conditions of the capital. Between February and March, there are usually air pollution alertsthat exceed the maximum values allowed by the WHO, as shown in the timeline for 2019.
3.2. TheAir Quality
andMann–Whitney
Citizens’ Perception
t-test andU test showed significant differences in CO, NO2 , andRegarding the data
providedtheBogotá
Air Quality
Network,
PM10 concentrations
between2019 byand2020(p-value< 0.05) Monitoring(Supplementary
Tablethere
S5).
wasIna addition,
decrease in
criteria
pollutants,suchascarbonmonoxide(CO),
nitrogenoxidesit was found that the perception of air quality improved significantly
2 = 25.73;(NO2),theparticulate
than 10with
microns
diameter(PM), and fineparticulate
duringpandemicmatter
periodlesscompared2019 in(Chi
p =100.00001)(Supplementary
matter
(PM
2.5
).
For
the
strictquarantine
stage,
therewas
anaveragereductionofforFigure S2). Air quality perceptions are related to the intention to use personal ~51%
protective
CO
and
~61%
for
NO
2
(Figure3a,b).
These
reductions
were
due
to
the
decreaseintransequipment (PPE) (p-value = <0.001, X-squared = 20.0). This relationship is evident only inportationand halt ofactivities
produceWe
fixedemissions.
surveysconducted
duringthe thatpandemic.
foundthat before the pandemic, the use ofRegardingparticulatematterconcentrations,
theretrend
was aandreductionof ~36%for of
PMPPE,
10
PPE was not significant. The pandemic modified thispromoted
the use
and
~19%
for
PM
2.5 (Figure 3c,d). It is important to highlight that the strict lockdown beganleading to the emergence of similar trends in the perception of the relationship between air
quality and health effects (Supplementary Figure S2).

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Int. J. Environ. Res. Public Health 2022, 19, x

6 of 12at a time when the city presented critical air pollution conditions due to the atmosphericconditionsof the capital. Between February and March, there are usually air pollution
Int. J. Environ. Res. Public Health 2022, 19,6350
alerts that exceed the maximum values allowed by the WHO, as shown in the timeline for2019.

Figure3.3.Air
Airquality
qualitytimeline:
timeline:(a)(a)daily
averageconcentrationsof carbonmonoxide(CO);(b) nitroFigure
daily
averageconcentrationsof carbonmonoxide(CO);(b) nitrogengen dioxide (NO2) concentrations; (c) the first panel corresponds to concentrations of particulate
dioxide (NO2 ) concentrations; (c) the first panel corresponds to concentrations of particulate mattermatter below 10 microns (PM10); (d) particulate matter below 2.5 microns (PM2.5). The light blue
below 10 microns (PM10 ); (d) particulate matter below 2.5 microns (PM2.5 ). The light blue series
series shows moving concentrations in 2019 and the dark blue series shows concentrations in 2020.
shows moving concentrations in 2019 and the dark blue series shows concentrations in 2020.

The t-test and Mann–Whitney U test showed significant differences in CO, NO2, and3.3. Transport
PM10 concentrations between 2019 and 2020 (p-value < 0.05) (Supplementary Table S5).
From the time-series analysis of the data provided by Transmilenio S.A., declines
In addition, it was found that the perception of air quality improved significantlyof 70–80% in revenues to the city’s BRT system were observed
between April and June,
during the pandemic period compared with 2019 (Chi2 = 25.73; p = 0.00001) (Supplemencompared with the data of 2019 (Supplementary Figure S3). This decline had not returnedtary Figure S2). Air quality perceptions are related to the intention to use personal protecto habitual levels by December 2020, showing 37% fewer revenues than the previous year.
tive equipment (PPE) (p-value = <0.001, X-squared = 20.0). This relationship is evident only
The figures show the effect of the strict quarantine and are consistent with the reduction inin surveys conducted during the pandemic. We found that before the pandemic, the use
air pollutant emissions.
of PPEwassignificant.
The pandemic
modified thisoftrend
and promoted
use ofFromthenotresponses
obtainedfrom the participants
the ITHACA
survey,thechangesPPE,
leadingtotheemergenceofsimilartrendsintheperceptionoftherelationshipin the choice of a preferred mode of transportation were found (Figure 4). There wasbea
tween airtoquality
andS2).conventional buses, alongtendency
decrease
thehealthuse ofeffects
public(Supplementarytransport, such asFigure
BRT andwith eight-point and one-point increases in the choice of private transportation by car and3.3.
Transport respectively. There was also an increase in the use of active transport modes,bymotorcycle,
From
the time-seriesof thedata
provided by fiveTransmilenio
declines ofreaching
almostfour pointsanalysis
for walking
andapproximately
points for S.A.,
biking.
70–80% in revenues to the city’s BRT system were observed between April and June, com3.4.
Water
Resources
pared
with
the data of 2019 (Supplementary Figure S3). This decline had not returned tohabitualby December
2020,according
showing to37%
revenues
thanyear.
Whenlevels
analyzingwater quality
thefewer
reports
generatedbythe
the previous
Bogotá Water
TheSewerage
figures show
the effectof the strict
quarantine
are consistent
with thepollutants
reduction
andCompany,
significant
reductions
wereandobservedin the different
in air pollutant
emissions.
analyzed.
In the caseof organic matter, a reduction of approximately 7 mg/L was observedin 2020 compared with 2019. Additionally, ammonium was reduced by 38%, manganese
was reduced by 75.8%, turbidity was reduced by 46%, and conductivity was reduced by
49%. These values refer to the maximum concentrations observed for the two years ofanalysis and represent a significant decrease and, thus, favorable conditions in the city for

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Int. J. Environ. Res. Public Health 2022, 19, x

7 of 12From the responses obtained from the participants of the ITHACA survey, changes
in the choice of a preferred mode of transportation were found (Figure 4). There was aInt. J. Environ. Res. Public Health 2022, 19, 6350
tendency to decrease the use of public transport, such as BRT and conventional buses,
along with eight-point and one-point increases in the choice of private transportation by
car and by motorcycle, respectively. There was also an increase in the use of active
transportmodes,
four withpointsthefordecrease
walking andapproximately
points located2020. Thiseffectreaching
may be almost
associatedin discharges
fromfiveindustries
forbiking. of the plant’s catchment point, i.e., in the upper basin (Supplementary Figure S4).
upstream

Figure 4. Transport modes in 2019 compared to 2020 or pandemic times according to the ITHACA

Figure 4. Transport modes in 2019 compared to 2020 or pandemic times according to the ITHACA
perception survey.
perception survey.

4. Discussion

3.4. Water Resources

To our knowledge, this is the first study carried out about a city in Latin America
When analyzing water quality according to the reports generated by the Bogotá Waexploring the effects of the COVID-19 on environmental matrices, including effects on water,
ter and Sewerage Company, significant reductions were observed in the different polluair quality,
mobility
patterns,
and wasteSome
studies exploreddescriptively
tants
analyzed.
In the caseof organic
matter,production.
a reduction of
approximately
7 mg/L wastheeffectsofthepandemic
onairqualityinArgentina,
Chile,
Colombia,
andMexicoobserved in 2020 compared with 2019. Additionally, ammonium was reduced by 38%, [35–38],
while others
reportedthedeforestation
patterns
BrazilPeru [39]. was
Nevertheless,manganesewasreducedbyeffects
75.8%,ofturbidity
was reduced
byin46%,andandconductivity
information
relatedsolid waste
andconcentrations
water qualityobservedis scarce.for the two
reducedby 49%.
Thesetovalues
refer toproduction
the maximum
restrictions
establishedby the government
generateda changein the dynamics
ofyears Theof analysis
and represent
a significant
decrease and,thus, favorable
conditions
in
the
2020.
Thisquarantine
effect may bewith thein discharges
from in- activities
thecitycity.forThe
strictledassociatedto the closureof decrease
many places
with economic
dustries
locatedwaste,
upstreamof the plant’s
catchment
point,
i.e.,pollution
in the upperthat produce
atmosphericemissions,
and
water
andbasin
to an(Suppleincrease in solid
mentary
FigureS4). The decrease in different environmental effects was reflected in several
biosanitary
waste.
scenarios, generally having positive effects on the environment.
4. Discussion
First, there was a 32% reduction in solid waste in comparison with the normal amountTo
our inknowledge,
is thefirst study
carriedthis
out reduction
about a citywas
in Latin
Americaproducedthe most thiscriticalmonth.
However,
evident
only until the
exploring the effects of the COVID-19 on environmental matrices, including effects on
gradual economic opening, at which time the tons of solid waste arriving at the landfill
water, air quality, mobility patterns, and waste production. Some studies explored decorresponded to the average values of previous years. In addition, there was a significant
scriptively the effects of the pandemic on air quality in Argentina, Chile, Colombia, andincreasein thewhile
generationof biosanitary
wastefrom large producers,from and
600,000 tons
Mexico[35–38],others reported
the effects
of deforestation
patterns in Brazil
per
month
to
almost900,000
tons/year.
Someauthors
considerthat
waste
production
Peru [39]. Nevertheless, information related to solid waste production and water quality
has
risen
as
a
resultof
COVID-19,
althoughthesechangesdonotfollowthesame
pattern
is scarce.
in different
areas
[40].
The
increasein
waste
productionis
explained
in
two
ways:
first,The restrictions established by the government generated a change in the dynamics
diseaseprevention
or treatment(hospitalandlab PPE)
and, second,of
the city.
The strict quarantine
ledactivities
to the closureof many
placeswith [41]economic
activitiesthe effects
that
producewaste,onatmospheric
and waterin-homepollutioncooking
and to anincreaseof thepandemic
lifestyles, emissions,such as increased
andonlineinshopping.
Additionally, as a result of the massive usage of PPE, this waste category substantially
increased, between 18% and 425% [42,43].
On the other hand, atmospheric pollution greatly benefited from the city’s mobility
restrictions. The Colombian capital tends to present atmospheric conditions that are notcharacterized exclusively by local emissions, as the influence of regional pollution has been
demonstrated [44]. Despite this influence, mobility and industrial activities that are usually
critical for the city slowed down for months. Authors have reported similar results indifferent regions. A study carried out in 10 countries in North America, Europe, and Asia(n = 9394) observed that a reduction in pollutant concentration was perceived, although todifferent extents, by all populations [45]. According to the authors, except for participantsfrom China and Norway, participants from all countries perceived a drop in the air pollution
193Int. J. Environ. Res. Public Health 2022, 19, 6350

concentration during the quarantine period. However, the large decline observed in muchof the world is projected to be exceeded again by the end of 2021 [46]. The “rebound effect”
of the pandemic is estimated to produce an increase of 36.4 billion tons of carbon emissions
from burning fossil fuels, an increase of 4.9% in 2021 compared with the previous year,
reaffirming the continued reliance of the planet on carbon-based technologies.
Air quality is directly reflected in the third matrix evaluated, which refers to transportation. Clear decreases were observed in the use of the city’s BRT system, and the transitionof transport modes must be evaluated. A notable proportion of people shifted from public
to private transport, which is logical given the exposure to COVID-19 in mass transit
modes. These findings are similar to those reported in a study carried out in Pakistan [47].
Travelers seem to prefer not to use public transport during the pandemic situation. Survey
participants stated that they feared being infected with the virus while traveling on public
transport, as there are chances of interacting with a person who is a carrier of the COVID-19
virus. Another study carried out in more than 15 countries in Asia, Europe, and North
America found that most commuters have changed their trip habits. Shopping became theprimary purpose for travel during COVID-19 [48].
This change might not be very beneficial if the percentage of people who start using
vehicles or motorcycles increases to the extent that the vehicle fleet starts to have a greater
impact on air pollution [49]. This situation may be prevented by the increase in the choiceof active modes of transport, which generate enormous benefits given sustainable mobility.
We similarly observed an improvement in sewage water quality parameters. Reports
from different regions around the world likewise show a reduction in water pollutants
during the quarantine period [24]. This may be explained by the decrease in industrial water
consumption. Although the report analyzed in this paper refers to the conditions in only
one period of the two years of study, the decrease in parameters such as organic matter andturbidity represents a great relief for the city’s water bodies and their treatment logistics.
This study has many limitations. The first concerns the design of the study. Ecologicalstudies look for associations between the occurrence of disease and exposure to known orsuspected causes. However, because it is not possible to control different variables, biases
can interfere with the analysis regarding the association between exposure and outcomes.
Second, we did not include variables related to the effect of the pandemic on biodiversity
due to the lack of data. Third, the data on changes in transport modes and perceptionof air quality are not representative of all cities. Finally, data were not available in thesame format for all variables, i.e., the frequency of collection differed depending on theenvironmental matrix and source of the query, so data between matrices could not becompared to more accurately estimate the interrelationships among matrices.
5. Conclusions
This study showed the COVID-19 pandemic impact on the environment, offering
an overview of changes in pollutants during 2019 and 2020 in Bogotá. The COVID-19
pandemic was a unique chance to analyze environmental impacts due to the fact that
activities in large cities, such as Bogotá, were stopped over lockdown. The effects wereboth positive and negative. During the pandemic, Bogotá, like most cities around theworld, adopted a strict lockdown to contain the spread of the virus. The months of strict
restrictions generated significant changes associated with environmental and ecologicalconditions. The main air pollutants decreased markedly, attributed to travel restrictions byprivate cars or public transport.
This is relevant, considering that there is a long-term—potentially permanent—downward
impact on the levels of environmental pressure, with stronger effects for pressure related tocapital-intensive economic activities [50]. The promotion of telework as a result of lessons
learned from the pandemic may positively impact some environmental matrices. Social
experiments such as “a day without a car” have been undertaken in cities such as Paris,
Berlin, Bogotá, and Toledo [51–53]. These experiences offer interesting results for improving

194

Int. J. Environ. Res. Public Health 2022, 19, 6350

mobility and air quality. More detailed studies are urged to understand and apply thelessons learned from the COVID-19 pandemic and its effects on the environment.
Supplementary Materials: The following supporting information can be downloaded at: https://www.
mdpi.com/article/10.3390/ijerph19106350/s1, Table S1: Variables to be analyzed and information
sources; Table S2: Types of generators according to their production in kilograms per day and thefrequency of waste collection; Table S3: Sociodemographic characteristics of the survey participants
ITHACA; Table S4: U-Mann Whitney test to total solid waste and biosanitary waste; Table S5: UMann Whitney and t-test to Air Quality data; Figure S1: Solid waste disposal series at the Bogota
sanitary landfill. The gray line with circular markers indicates the tons per month for 2019, theblue line with square markers indicates the tons disposed of in 2020, the year of the pandemic;
Figure S2: Air quality perception: (a) the air quality on house from job. (b) Only pandemic answers
about the existence of a relationship between air quality and usage of personal protection; Figure S3:
BRT system admissions timeline. The light blue series shows moving concentrations in 2019 and thedark blue series shows concentrations in 2020; Figure S4: Water Quality reported Bogota Water andSewer Service 2019–2020.
Author Contributions: Conceptualization, J.M.-R. and C.P.G.-R.; methodology, J.M.-R., D.M.-M. and
Y.G.T.-P.; software, Y.G.T.-P. and D.M.-M.; formal analysis, D.M.-M., Y.G.T.-P., J.A. and
E.L.P.-B.; investigation, D.M.-M., Y.G.T.-P., J.A. and E.L.P.-B.; data curation, D.M.-M., Y.G.T.-P. and
J.A., writing—original draft preparation D.M.-M., Y.G.T.-P., J.A. and E.L.P.-B.; writing—review andediting, J.M.-R., J.A., E.L.P.-B. and C.P.G.-R. All authors have read and agreed to the published versionof the manuscript.
Funding: The project was partially funded by MinCiencias Colombia, Project Number 2010484467564(call number 844 of 2019).
Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics and Methodologies Committee (CEMIN)
at the National Institute of Health of Colombia (Protocol Code 014/2019, March 2019 for studies
involving humans.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Not applicable.
Acknowledgments: The authors want to thank Hugo Saenz and Sergio Salazar-Sánchez from Secretaria Distrital de Ambiente de Bogotá for their collaboration in providing the public data on air
quality and water. Additionally, the authors want to thank the Special Administrative Unit of PublicServices in Bogotá for providing us with the data on solid waste. Finally, J.M.R. would like to express
his sincere gratitude to Fernando Gutierrez at El Bosque University, for their recommendations in the
final version of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the designof the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; orin the decision to publish the results.

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197

International Journal ofEnvironmental Researchand Public Health
ArticleEthnopharmacology for Skin Diseases and Cosmetics during
the COVID-19 Pandemic in Lithuania
Zivile Pranskuniene 1,2, * , Rugile Grisiute 2 , Andrius Pranskunas 3
1
2
3

*

Citation: Pranskuniene, Z.; Grisiute,
R.; Pranskunas, A.; Bernatoniene, J.
Ethnopharmacology for Skin
Diseases and Cosmetics during the
COVID-19 Pandemic in Lithuania.
Int. J. Environ. Res. Public Health 2022,

and Jurga Bernatoniene 1,2

Department of Drug Technology and Social Pharmacy, Lithuanian University of Health Sciences,LT-50162 Kaunas, Lithuania; jurga.bernatoniene@lsmuni.lt
Institute of Pharmaceutical Technologies, Lithuanian University of Health Sciences,LT-50162 Kaunas, Lithuania; rugile.grisiute@stud.lsmu.lt
Department of Intensive Care Medicine, Lithuanian University of Health Sciences,LT-50161 Kaunas, Lithuania; andrius.pranskunas@lsmuni.lt
Correspondence: zivile.pranskuniene@lsmuni.lt

Abstract: The documentation of ethnopharmaceutical knowledge has always been important for thepreservation of countries’ cultural, social, and economic identity. The COVID-19 pandemic with thecollapse of healthcare, which has left the individual health to self-care, has also forced us to look backat ethnopharmacology from a practical point of view. This is the first study in Lithuania, dedicatedentirely to ethnopharmaceuticals used for skin diseases and cosmetics, and the first study to analyse
ethnopharmacology as a Lithuanian phenomenon during the ongoing COVID-19 pandemic. The
main purpose of this study was to collect and evaluate ethnopharmaceutical knowledge regarding
skin diseases and cosmetics in Šiauliai District, Lithuania during the COVID-19 pandemic from July
2020 to October 2021. This study surveyed 50 respondents; the survey was conducted using thedeep interview method. The respondents mentioned 67 species of medicinal plants from 37 different
families used for skin diseases (64.18%), cosmetics (13.44%) and cosmeceuticals (22.38%). Of the
67 plant species, 43 (64%) were not included in the European Medicines Agency monographs andonly 14 species (21%) of all included species were used with European Medicines Agency approved
medical indications for skin diseases. In terms of public health, the safety of “self-treatment” andrecovery rituals for skin diseases are no less important than ethnopharmacological knowledge and its
application, this being especially relevant during the COVID-19 pandemic.

19, 4054. https://doi.org/10.3390/
ijerph19074054

Keywords: ethnopharmacology; skin diseases; cosmetics; COVID-19; Lithuania

Academic Editors: Sachiko Kodera
and Essam A. Rashed
Received: 12 February 2022
Accepted: 25 March 2022
Published: 29 March 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
Lithuanian scientists first viewed ethnopharmacology as a cultural phenomenon.
It was analyzed in historical, ethnocultural and geographical contexts. Traditional folk
medicine is an area of folk culture which includes medicinal knowledge, beliefs, andtreatments that existed in traditional rural communities [1]. It is necessary to document traditional knowledge since many communities are losing their cultural, social, and economic
characteristics [2].
Lithuanian medics, interested in ethnopharmacology, are viewing it not only as a partof the Lithuanian history of medicine, but also as a significant opportunity for medical
science and practice. The use of ethnopharmaceuticals, analyzed from both ethnologicaland ethnomedical aspects, has forced us to view ethnopharmacology as a whole entity.
An opinion that ethnopharmacology is not only a way of treatment but also the means ofhealth care and disease prevention has formed [3,4].
Currently, the interest in ethnopharmacological research is not only noticeable indeveloping countries but in the developed world as well. The COVID-19 pandemic withthe collapse of healthcare, which has left the health of individuals to self-care, has forced us
to look back at ethnopharmacology from a practical point of view [5].

199Int. J. Environ. Res. Public Health 2022, 19, 4054

The World Health Organization has estimated that more than 6 billion people dependon plant-based and animal-based medicine. Various populations have natural, widely usedpharmacopoeia, where animal and plant derived ingredients are used in the preparationof modern medicines, herbal, and traditional medicines [6]. In the 21st century, medicinal
plants have not lost their significance in human lives. Their application value for prophylactic and treatment purposes keeps increasing. The inhabitants of various countries actively
make use of local and imported plants and their parts. As many as 80% of the world’s
medicinal products are made from medicinal plants, consequently the research of plants
is still relevant. Today, as in the past, there are still specialists in phytotherapy since theknowledge and analysis of plants to this day remains a relevant issue.
One of the areas of research and application of ethnopharmacology is dedicated tovery important human organ, the skin. The European Medicines Agency, as well as theWorld Health Organization and the European Scientific Cooperative on Phytotherapy, haveconfirmed that one of the most frequent indications for which many medicinal plants areused in the European Community and in the rest of the world, is the treatment of skindisorders and minor wounds [7]. Researchers around the world are researching plants andlooking for natural means to treat skin diseases and create cosmetics [8–11].
Skin care and preventative treatments have a strong impact on the condition of skin,consequently scientists pay a lot of attention to cosmetics and cosmeceuticals (the active andscience-based cosmetics). Natural agents are gaining popularity nowadays as most women
prefer natural products for their personal care. These products supply the body withnutrients and enhance health and at the same time are free from synthetic chemicals andhave relatively less side-effects compared to synthetic cosmetics. Following this tendency,
more medicinal plants are used for the development of new drugs, cosmeceuticals, andpharmaceutical applications [12]. The investment in ethnobotanical studies can minimize
the loss of knowledge and, with specialized analytics and more complex in vitro skinmodels, can develop and help better understand the effects that plants have on humanskin [7]. In addition, preventative cosmetic procedures can reduce psychological disordersthat may increase in patients during the COVID-19 pandemic situation [13].
Studies have shown that during the COVID-19 pandemic, patients were concernedwith their appearance and continued to undergo cosmetic procedures and pay attention
to their skincare [14]. The emotional health and well-being of a person heavily depends
on aesthetic appearance, therefore the disrupted availability of cosmetology services dueto the pandemic was a stressful occurrence in Lithuania as well. This is the first study
in Lithuania dedicated entirely to skin diseases and cosmetic ethnopharmaceuticals, andthe first study to analyze ethnopharmacology as a phenomenon in Lithuania during theongoing COVID-19 pandemic. The main purpose of this study was to collect and evaluate
ethnopharmaceutical knowledge regarding skin diseases and cosmetics in the Šiauliai
District during the COVID-19 pandemic in Lithuania.
2. Materials and Methods
2.1. Study Area
The climate of Lithuania is transitional between the continental type in the East andthe oceanic type of Western Europe. Therefore, here predominate air masses influenced by
the Atlantic alternating with continental Eurasian or Arctic air masses. The coldest monthin Lithuania is January with an average temperature of −5 ◦ C; the warmest month is July
with an average temperature of 17 ◦ C. The average annual rainfall is about 800 mm [15].
Climate change also affects Lithuanian vegetation, which inhabits several different regions.
The maritime region is dominated by pine forests, the sand dunes by shrubby plants, spruce
dominate in the hilly eastern part, oaks in the central part, birches, black alder, aspen in thenorth, while pine forests are prevalent in the south. About 1/3 of the vegetation consistsof forests, 1/5 is meadows, and a small part is wetlands. Dzūkija is the most forested
ethnographic area in Lithuania, while the least forested is Suvalkija. Coniferous trees make200

Int. J. Environ. Res. Public Health 2022, 19, 4054
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW

3 of 14

up 56% of Lithuania’sforests,
while1/5
deciduoustrees
and hardwoods
4%. Thevegetation consistsof forests,
is meadows,
and makea small up
part39%is wetlands.
Dzūkija is theforested in
ethnographic
in Lithuania,
while
the least
average agemostof forests
Lithuaniaarea
is from
50 to 69
years
[16].forested is Suvalkija. Coniferous trees
make up 56%
of Lithuania’sforests,
while deciduoustrees makeup 39% and
The regionsin Lithuania
thathave formed
throughout
the courseof history
are called
hardwoods 4%. The average age of forests in Lithuania is from 50 to 69 years [16].
ethnographic regions. Their boundaries roughly coincide with the boundaries of Lithuanian
The regions in Lithuania that have formed throughout the course of history are called
dialects. The
regions regions.
differ in
theirinternalstructure,
traditions,architecture,ethnographic
Theirboundaries
roughly
coincidecultural
with the boundaries
of Lithuadialects, andactivities[17].
Samogitia
is a internal
uniquestructure,ethnographic
region of
Lithuania, firstnian
dialects. The
regions
differ in theircultural traditions,architecture,
activities [17].
Samogitia
a uniqueethnographic
Lithuania,
firstmentioneddialects,
in the andVolhynian
Chronicle
inis1219.
It stands
out region
from ofother
homesteads
inmentionedlandscaping,in the Volhynian
Chronicle
1219. It stands
out fromother homesteads
interms of planning,
work
tools, inhousehold
utensils,
landscape,traditions,andterms of planning, landscaping, work tools, household utensils, landscape, traditions, anddialect. The
traditional dishes of Samogitia are cibulynė, or blood sausage with groats,dialect. The traditional dishes of Samogitia are cibulynė, or blood sausage with groats,
and boiled andpotatoeswith hemp.
Samogitians
have
being
stubborn,boiled potatoes
with hemp.
Samogitians
have aa reputation
reputation forfor
being
very very
stubborn,sincere,unhurried,andhospitablepeople.
Samogitia
ischaracterizedby
thesecraftsmen:
sincere, unhurried, and hospitable people. Samogitia is characterized by these craftsmen:
shoemakers,
weavers,wheelers,andand blacksmiths.
Pottery
is especiallycarpenters,carpenters,
shoemakers,
tailors,tailors,
weavers,wheelers,
blacksmiths.
Pottery
is especially
popular. Samogitia used to be famous for its wooden chapels, chapel columns, crosses,
popular. Samogitia used to be famous for its wooden chapels, chapel columns, crosses, andand figurines of saints [18].
figurines of saints[18].
Šiauliai
district is in the Northwest of Lithuania, located directly on the border beŠiauliaitween
district
is in theof Lithuania,
locateddirectlyon theTheborderbetween
Aukštaitija
andNorthwest
Samogitia, although
it is considereda partof Samogitia.
district
1807 square althoughkilometres, itconsistsof 11 elderships,
a populationThedensityof 26 covers
Aukštaitija covers
and Samogitia,
is considereda partwithof Samogitia.
district
km. The district has 1 city, 7 towns and 521 villages within its territory. The
1807 squarepeople/sq.
kilometres,
consists of 11 elderships, with a population density of 26 people/sq.
highest point is Grinikai hill (183.4 m). Šiauliai district is in the middle plain of the Venta
km. The district
city, 7oftowns
andNemunėlis
521 villages
its territory.
highestriver, inhas
the 1lowland
the Mūša
and inwithin
the Eastern
SamogitianTheplateau.
For- point
is Grinikai ests
hillcover(183.4m).ofŠiauliai
districtis in
the middle
Venta river,
34.8%
the district’s
territory.
Spruceand birch plain
forests ofarethepredominant.
The in theimportantextractablemineralsin Eastern
the districtSamogitian
are gravel andsand [17,18].
Most coverof the 34.8%
lowland of mostthe Mūša Nemunėlis andin theplateau.
Forests
respondents
livedSprucecloser andto thebirch
borderof Samogitia(Kuršėnai, Daugėliai,Kužiai,of the district’s
territory.
forestsare predominant.
The most
important
Gruzdžiai, Lukšiai, etc.) (Figure 1). Although the Samogitian dialect is quite prevalent in
extractableSamogitia,
mineralspeople
in thedistrictaregravelandsand[17,18].
Most
of
the
respondents
who can speak the dialect usually use it only with people they are close
lived closertoto
theinborder
Samogitia(Kuršėnai,in DaugKužiai,Gruzdžiai,Lukšiai,with,a privateof
environment
andless often
public.ėliai,
Althoughthe respondents
wereetc.) (Figure1). Although
the Samogitian
quite
prevalentin Samogitia,
mostlyfrom the Samogitianside of thedialectborder, isthey
did not
use the dialectduring thepeopleinterviews.
who can speak
the dialect usually use it only with people they are close to with, in a private
environment and less often in public. Although the respondents were mostly from the
Samogitian side of the border, they did not use the dialect during the interviews.

Figure 1. Study area.

2.2. Methods
The study was conducted from July 2020 to October 2021 in Šiauliai district, in the
territory of the Samogitia ethnographic region (Figure 1). The purpose of this study was
FigureStudy area.
explained toeach1. interviewee
and an informed consent form was signed prior to the study.
The study was conducted in accordance with the Code of Ethics of the International Societyof Ethnobiology [19]. The research was approved by the Bioethics Centre of the Lithuanian
University of Health Sciences (No. BEC-FF-30). The study surveyed 50 respondents, 9 men(18%) and 41 women (82%). The respondents were mostly farmers, housewives and evenmedical professionals, practically all of whom were interested in the use of medicinal plants
for medicinal purposes (herbalists). Permission to conduct the study was obtained fromlocal community leaders. A study guide identified the members of the target group as
respondents who used ethnopharmacology for skin diseases and cosmetics. The study
group formation employed the “snowball” technique. All safety measures were taken

201

Int. J. Environ. Res. Public Health 2022, 19, 4054

according to requirements of the pandemic situation (i.e., wearing face masks and gloves
and maintaining a safe distance).
The research method was a structured interview. It was carried out in two stages.
During the first stage, using the prepared questionnaire File S1 (Supplementary Materials),
the researcher wrote down the respondent’s answers. The prepared questionnaire consistedof 17 questions. The first stage started with the main questions for demographic data andclosed questions to assess the source of ethnopharmaceutical information and to evaluate
how many respondents chose to consult a healthcare professional (pharmacist or doctor)
as a qualified consultant regarding ethnopharmacology for skin diseases and cosmetics.
Much attention was paid to the sources of ethnobotanical knowledge obtained by the
respondents. The aim of the second stage was to gather as much information as possible
about products of natural origin, to identify medicinal raw materials used for medicinaland cosmetic purposes, their preparation methods, indications for use, doses, durationof use, and storage conditions (Figure 2). This information was obtained in the form of afree interview, and informants were allowed to speak spontaneously and without pressure.
Interviews were voice recorded with permission from the respondents, and field notes
were also taken and encoded. It was attempted to capture information about collected
medicinal substances: where they were collected and how and under what conditions they
were dried and stored. Respondents were visited a second time to supplement information
if needed. The indications for skin diseases identified in the study were compared with
the European Union herbal monographs by the Committee on Herbal Medicinal Products
published by the European Medicines Agency (EMA) [20]. In this way, an attempt was
Int.made
J. Environ.to
Res.determine
Public Health 2022,the19, x extent
FOR PEER to
REVIEW
5 of 14which the indications for use in this study matchedtheindications approved in the EMA studies.

(b)

(a)

(c)

2. Preparationand storageof medicinal
raw materials
usedskin diseases
diseases
Figure 2. Preparation Figureand storage
conditions
ofconditions
medicinal
raw materials
usedforforskin
and/or cosmetics: (a) drying of various herbs; (b) prepared Sea buckthorn oil; (c) storage of dried
and/or cosmetics: (a) dryingof various
plants in canvas
bags. herbs; (b) prepared Sea buckthorn oil; (c) storage of driedplants in canvas bags.

3. Results and Discussion

3.1. Characteristics of Informants and Sources of Ethnopharmaceutical Knowledge
50 respondents were interviewed: 9 men (18%) and 41 (82%) women. The age of the202
respondents
ranged from 23 to 94 years. 30% of the respondents were over the age of 65.
When interviewing the respondents, it was important to determine whether the respondents were permanent residents of Šiauliai district and to determine if they were born there.

Int. J. Environ. Res. Public Health 2022, 19, 4054

Taxonomic identification, botanical nomenclature and plant family assignment were
performed based on validated databases, namely World Flora Online [21] and the Angiosperm Phylogeny Group IV [22]. Plant species were identified using writings on traditional Lithuanian flora [23–25].
The research data is stored in the Lithuanian Museum of the History of Medicine andPharmacy of the Lithuanian University of Health Sciences.
3. Results and Discussion
3.1. Characteristics of Informants and Sources of Ethnopharmaceutical Knowledge
50 respondents were interviewed: 9 men (18%) and 41 (82%) women. The age ofthe respondents ranged from 23 to 94 years. 30% of the respondents were over the ageof 65. When interviewing the respondents, it was important to determine whether the
respondents were permanent residents of Šiauliai district and to determine if they wereborn there. Four respondents have changed their place of residence (moved to another
district), but all of them named Šiauliai district as their place of residence. Therefore, we
can assume that part of the ethnopharmaceutical knowledge collected in the study area
is of local use. Respondents from 18 different residential areas of Šiauliai district wereinterviewed in this study. Most respondents lived in villages-Kužiai 15%, Voveriškiai 13%,
Gruzdžiai 11% (Figure 1).
The respondents’ education was also an important indicator in the survey. The oldest
respondents in the survey, who were over 80 years old, had primary education 7 respondens(14%), secondary education 4 respondents (8%), vocational education 8 respondents (16%),higher education 7 (14%), and university education 24 (48%) respondents. This shows thatin the first decades of the 21st century, 22% the of respondents did not have a profession
and were primarily agricultural workers. A total of 20 (40%) respondents with vocational,
higher and university education were engaged in agricultural activities. The other 19 (38%)
respondents were not involved with agricultural work (studying or working in the city).
Knowledge about the use of ethnopharmaceuticals for the treatment of skin diseases
and for cosmetic purposes was obtained mainly from parents, grandparents, and relatives
45 (90%) of all respondents. Other sources of information were neighbors 27 (54%), theinternet and television 26 (52%), doctors 9 (18%), pharmacists 25 (50%), and books 13 (26%).
Every second respondent consulted a pharmacist on the use of ethnopharmaceuticals,
which is a high percentage, considering that in our previous studies in Lithuania thispercentage was incredibly low—0% [3], 8% [26] and 28% [27] of all respondents. A study
on the attitude of the Lithuanian population towards phytotherapy revealed that only
1% of patients in all age groups turned to a pharmacist as a source of information on
herbal medicine [28]. The reasons for not referring to a pharmacist were mainly distrustof pharmacists and their lack of knowledge about ethnopharmacology, and sometimes
even the pharmacist’s ridicule of the patient for using such measures. In this study, due
to the increase in the number of patients turning to a pharmacist for information on
ethnopharmacology, the COVID-19 pandemic can be identified as the cause, due to it
causing difficulties in reaching a physician or cosmetologist, leaving pharmacists as themost widely available healthcare professionals. On the other hand, the question arises as towhether the consultation and the knowledge of the pharmacist were sufficient and met the
expectations of patients.
3.2. Skin Diseases and Ethnopharmacology
We documented 67 species of medicinal plants from 37 families used for skin diseases
and cosmetics. All of the collected data are summarized in Table S1 (Supplementary
Materials). According to our results, the most common indication of skin diseases were
wounds (39%) (Figure 3).

2033.2. Skin Diseases and Ethnopharmacology
We documented 67 species of medicinal plants from 37 families used for skin diseases
and cosmetics. All of the collected data are summarized in Table S1 (Supplementary MaInt. J. Environ. Res. Public Health 2022, 19, 4054
terials). According to our results, the most common indication of skin diseases were
wounds (39%) (Figure 3).

Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW

7 of 14

Figure 3.
3. Ethnopharmaceutical
Ethnopharmaceutical preparations
preparations forfor skinskin disorders.
disorders.
FigureAccording to studies by other researchers, this is the most common indication of all
According to studies by other researchers, this is the most common indication of all
skin diseases for which ethnobotanical measures are used [7]. For thousands of years, meskin diseases for which ethnobotanical measures are used [7]. For thousands of years,
dicinal plants have represented the only remedy for wound care, and they still maintainmedicinal plants have represented the only remedy for wound care, and they still maintainan important therapeutic role. Of course, the main properties of plants used for wound
an important therapeutic role. Of course, the main properties of plants used for woundcare are antimicrobial and anti-inflammatory activities [29]. Although homemade herbal
care are antimicrobial and anti-inflammatory activities [29]. Although homemade herbal
products in general are less expensive than modern treatments, they can lead to unexproducts in general are less expensive than modern treatments, they can lead to unexpected
pected allergic reactions and side effects [30]. This is especially true when applied to theallergic reactions and side effects [30]. This is especially true when applied to the affectedaffected area of skin (excluding wounds, the main indications were eczema, burns, abarea of skin (excluding wounds, the main indications were eczema, burns, abscesses). In
scesses).
In this study, all ethnopharmaceuticals were used externally, mostly as decocthis study, all ethnopharmaceuticals were used externally, mostly as decoction, compress,
tion,
compress,juice, and oil
applications(Figure 4).
juice, and oil applications(Figure4).

Figureof ethnopharmaceuticalethnopharmaceuticalpreparation
preparation
treatmentof skindiseasesand cosFigure 4.
4. Methods
Methods offorforthethetreatmentof skindiseasesand cosmetics.
metics.

Although the most common method of administration is decoction usually usedinternally,in thisa decoction
is preparedfrom plant-based
raw materials
andplacedAlthoughthecasemostcommon methodof administration
is decoction
usuallyused
inexternallythecaseareaaaffected
by is
skin
diseases.
Only
two types ofplant
raw materials
were
ternally,inon
thisdecoction
preparedfromplant-basedrawmaterials
and placed
used internally,
the decoction
Viola tricolor
aerialparts
for thetreatmentofexternallyon thenamelyarea affectedby skinof
diseases.
Only L.
twotypes
of plantrawmaterials
furuncles,
eczema, andrashes,thethedecoction
other internallysubstance
decoction
made
wereused internally,
namelyof Viola usedtricolor
L. aerialbeing
parts afor
the treatment
withroots of eczema,
Elytrigia repens
L., usedtreat internallyskin lesions,furuncles,
andbeing
rashes.
Decoctionoffuruncles,
and rashes,
thetootherusedsubstance
a decoction
made with roots of Elytrigia repens L., used to treat skin lesions, furuncles, and rashes.
Decoction was the most common method of preparation in this study, using a soft above204
ground part of the plant, so it is not surprising that the most used parts of the plant wereaerial parts, leaves and flowers (Figure 5). The hard parts of plants (i.e., the seeds, stem,

Int. J. Environ. Res. Public Health 2022, 19, 4054

was the most common method of preparation in this study, using a soft above-ground partof the plant, so it is not surprising that the most used parts of the plant were aerial parts,
Int. J. Environ. Res. Public Health 2022, leaves
19, x FORPEER
REVIEW
8 ofthe14
andflowers(Figure 5). The hard parts of plants (i.e., the seeds, stem, bark, andfruits) were used less often, and less common preparation methods were used to prepare
them, such as extraction with oil and tinctures.

Figureof thethe plants
plantsused
usedforforethnopharmaceuticalethnopharmaceuticalpreparations
for skindiseasesand cosFigure 5.
5. Parts
Parts ofpreparations
for skindiseasesand cosmetics.
metics.

Jarič S and colleagues in their study on traditional wound-healing plants used in the
Jarič
S and(Southeast
colleagues Europe)in their study
on traditional
plantsin theBalkanregion[31] mentioned
thatwound-healing
the applications
usedused
for wound
Balkan(Southeast
Europe)mentioned
that theapplications
used
wound
healingregion
were external,
in theform of[31]infusions,decoctions,tinctures,syrups,oils,forointments,
and balms,
andwere applied
the skin,with Plantago
majorsyrups,
L. beingoils,
the ointmost
healing
were
external,
in the directly
form of onto
infusions,decoctions,tinctures,
popularand
medicinal
plant.
ments,
balms, andwere applied directly onto the skin, with Plantago major L. being
In ourstudy,medicinal
the most citedthe most
popularplant.plants for the care of skin diseases were Plantago major L.
(80%)
Chelidonium
majus
L. plants(70%).forPlantago
L. diseases
leaves haveusedmajor
to treatInand
our study,
the most
cited
the caremajorof skinwerebeen
PlantagoL.
woundstraumas for
centuries
in almostall major
parts ofEuropeAccordingto(80%)
andandChelidonium
majus
L. (70%).
Plantago
L. theleaveshave[7,32].
been used
to treatour
study,
itwasusually
applieddirectly
onto
the
skin.
Chelidonium
majus
L.
(70%)
is
wounds and traumas for centuries in almost all parts of the Europe [7,32]. According toa
well-known
in Lithuania,
the freshjuiceChelidoniumof this plantmajus
is usedto destroy
our
study, it plant
was usuallyappliedfrequently
directly onto
the skin.
L. (70%)is awarts.
The
plant
accumulates
alkaloids,soingestion
without
medicalsupervision
can bewell-known plant in Lithuania, frequently the fresh juice of this plant is used to destroy
dangerous.
In ourstudy, the alkaloids,
plant wasso
usedonly externallyto treatsupervision
warts or psoriasis.
warts.
The plant
accumulates
ingestion
without medical
can beChelidoniumIn
majus
has traditionally
been
used
for externallythe treatmentof various
dangerous.
our L.
study,
the plant was
used
only
to treatwartsinflammatory
or psoriasis.
diseases andmajus
recently
there
were studies
conducted
use for atopic
dermatitis[33] andChelidonium
L. hastraditionallybeenused for on
theitstreatmentof various
inflammatory
antibacterial
wound
dressing
applications[34].
diseases and recently there were studies conducted on its use for atopic dermatitis [33]In our studywound
not only
the frequency
of usewas identified, but also a comparative
and antibacterial
dressing
applications[34].
safetyanalysiswith
EMA
monographs
wascarriedout (Table
S1). Modern
In our study not only the frequency of use was
identified,
but alsopharmacologicala comparative
investigations
have
shown
that
many
medicinal
plantsusedinethnobotanicaltraditions
safety analysis with EMA monographs was carried out (Table S1). Modern pharmacologworldwide
are being
currently
usedmanyin a very
rationalplants
way, mainly
new
and
ical
investigations
have
shown that
medicinal
used in synthesizing
ethnobotanicaltradiold
information.
Mostclinicalusesofmedicinal
plantshave
beensimilartotheirtraditionaltions worldwide are being currently used in a very rational way, mainly synthesizing newuses.old
However,
thereMost
are some
plantshave clinicalthatdifferfromtotraditional
andinformation.
clinicaluseswhichof medicinal
plantsuses
have
been
similartheir trauses [7,35]. Despite their clinical effectiveness, a guarantee of the safety and quality ofditional uses. However, there are some plants which have clinical uses that differ frommedicinal plants in developed countries is challenging as people increasingly return totraditional uses [7,35]. Despite their clinical effectiveness, a guarantee of the safety andherbal remedies refusing chemical drugs [27]. According to our study, respondents inquality of medicinal plants in developed countries is challenging as people increasinglyŠiauliai district mentioned 67 species of medicinal plants from 37 different families used
return to herbal remedies refusing chemical drugs [27]. According to our study, respondfor the treatment of skin diseases and/or production of cosmetics. Only 24 species areents in Šiauliai district mentioned 67 species of medicinal plants from 37 different families
described in the official herbal monographs of the European Medicines Agency (EMA) [20].
used for the treatment of skin diseases and/or production of cosmetics. Only 24 species
Sile I and colleagues from Latvia [36] have analyzed archives and found out that one ofare described in the official herbal monographs of the European Medicines Agency (EMA)
the most common health conditions were skin disorders. Analysis of EMA monographs[20]. Sile I and colleagues from Latvia [36] have analyzed archives and found out that one
showed that only 59 out of 211 taxa mentioned in this study are included in the officialof the most common health conditions were skin disorders. Analysis of EMA monographs
herbal monographs. EMA herbal monographs provide scientific information on safety
showed that only 59 out of 211 taxa mentioned in this study are included in the official
herbal monographs. EMA herbal monographs provide scientific information on safety
and efficacy and deserve further
205 exploration as traditional herbal medicines. In our study,
43 of 67 plant species were not included in the EMA monographs and only 14 species(21%) of all included species were used with EMA approved medical indications for theInt. J. Environ. Res. Public Health 2022, 19, 4054

and efficacy and deserve further exploration as traditional herbal medicines. In our study,
43 of 67 plant species were not included in the EMA monographs and only 14 species(21%)
allPEER
includedInt. J. Environ. Res. Public Health 2022, 19, xof
FOR
REVIEWspecies were used with EMA approved medical indications
9 of 14for thetreatment of skin diseases. Other medicinal plants were used without EMA approved
medical indications and were based solely on folk knowledge and experience in medicine.
In the treatmentof skinOther
diseases,
phytotherapy
importantand othertreatmentof skin diseases.
medicinal
plants wereplays
used anwithout
EMA role,
approvedmaterials
also usedasweremainbasedor additional
remedybases. andIn ourprevious
regarding
medicalareindications
andsolely on folk
knowledge
experience
instudy
medicine.
historicalusestreatmentof the bee
accordingto archivalhoneyandandpropolis
In theof products,
skin diseases,
phytotherapy
playssources,
an importantrole,other werematerials
also used
as abscesses
main or additionalbases.
In ourprevious
study
regard- (forusedto treatarewoundsand[37]. In remedy
this study,
honey
was
used asan excipienting historicaluses of thebee products,ointment,
according toarchivalsources,
andpropolisexampleby preparing
a home-madewhich
is madeby honeymixingpine
shoots and
were
used
to
treat
woundsandabscesses
[37].
In
this
study,
honey
was
used
as
an
exciphoney) to treat psoriasis. For acne-prone skin, a facial mask is made by mixing a tablespoon(forwith
exampleby ofpreparinghome-madeointment,
is madeby10
mixingpine tar isof ient
honey
10 mL
almonda oil
and one egg
yolk. which
To treatburns,
g of birch
shootsandhoney)
totreat
psoriasis.
Foracne-proneskin,afacial
mask
is
made
by
mixingmixed with 0.5 kg of honey and then the mixture is spread on the skin area affected bya tablespoon of honey with 10 mL of almond oil and one egg yolk. To treat burns, 10 g ofburns. Lard is often used as a base for the ointment. Melted hare fat is a popular remedy
birch tar is mixed with 0.5 kg of honey and then the mixture is spread on the skin area
for treating splinters, and it is used as a poultice on the opposite side of where the splinteraffected by burns. Lard is often used as a base for the ointment. Melted hare fat is a popis. ular
Cases
where
whole
animaland
is usedinsteadof its products
are usually
isolated
remedyforthetreating
splinters,
it is used
as a poulticeon the opposite
side ofonly
whereoccurrences(forexample,thetreatment
of
cold
sores
by
rubbingasmall
frog’sabdomenthe splinter is. Cases where the whole animal is used instead of its products are usuallyon only
the affectedarea).
isolated occurrences(for example, the treatment of cold sores by rubbing a small
frog’s abdomen on the affected area).

3.3. Ethnopharmacology and Cosmetics

3.3.Data
Ethnopharmacology
and Cosmetics applications for cosmetic use made up 13.44% of ouron ethnopharmaceuticalDataon ethnopharmaceuticalfor cosmetic
madeanalyses
up 13.44%
our haveresearch(TableS1). Ethnobotanical applications
studies combinedwith use
modernofof
plantsresearch (Table
S1).modern
Ethnobotanicalcombined
analysesof plants
a potential
to enrichcosmeticstudies
products.
Plantswith
withmodern
cosmetic
uses haveoften been
have a potential
to enrich modern
cosmetic
products.
withuses haveoften andneglected
in ethnobotanicalsurveyswhich
focus Plants
mainlyoncosmetic
plants withmedicinal
been
neglected
in
ethnobotanicalsurveyswhich
focus
mainlyonplantswith
medicinal
culinary uses [38–40]. Often, the target of researchers is the measures for treatment of skinand culinary uses [38–40]. Often, the target of researchers is the measures for treatment ofconditions,but during the course of the study, it has been revealed that many of the plantskin conditions, but during the course of the study, it has been revealed that many of the
preparations for therapeutic purposes were also used for cosmetic purposes, and making aplant preparations for therapeutic purposes were also used for cosmetic purposes, andclear distinction among the recorded preparations between cosmetics, cosmeceuticals andmaking a clear distinction among the recorded preparations between cosmetics, cospharmaceuticals
the treatmentforof theskintreatment
diseasesofisskinverydiseases
problematic
Althoughmeceuticals andfor
pharmaceuticals
is very[41–43].
problematic
it may
seem
that
the
treatmentofskindiseasesismoreimportantthanskincare for[41–43]. Although it may seem that the treatment of skin diseases is more important thancleanliness,prevention
andrestoration,
beautification
has
also
playedanimportantskin care for cleanliness, prevention and restoration, beautification has also played an im-role inruralcommunities.
show Studies
that theshow
needthat
forthebeautyincludingin-natural
portantrole in ruralStudies
communities.
need treatments,
for beauty treatments,
cluding
naturalones, has during
not diminishedduring the
COVID-19
pandemic[44]. Induring
our
ones,
has not
diminished
the COVID-19
pandemic[44].
In our study
thestudy during
the COVID-19
pandemicin Lithuania,
theusesmain
cosmetic
uses wereskin and
COVID-19
pandemicin Lithuania,
the maincosmetic
wereskin (20%of reports)(20%of reports)
and andhair hydration(17%) andsweat
reduction
hair
hydration(17%)
sweat reduction(17%)(Figure6). (17%) (Figure 6).

Figure6. 6.
Ethnopharmaceuticalusageforforcosmetics.
cosmetics.
FigureEthnopharmaceutical usage206

Int. J. Environ. Res. Public Health 2022, 19, 4054

According to our knowledge, there are only some ethnobotanical studies in the worldthat research the medicinal properties of plants and their use for cosmetic purposes. Xenia
J and colleagues [39] have conducted an ethnobotanical survey of cosmetic plants used in
the Marquesas Islands (French Polynesia). The most referred application areas were theskin, the hair, and the genitalia, whereas the main cosmetic uses were perfume, hydration,
medicinal care, and healing. In our study the most common areas of application were theface (40% of reports), the body (38%), the feet (16%), and the hair (7%). 15.5% of facial
treatments were used to reduce eye swelling.
If in Marquesas Islands the perfumed coconut oil, also known as monoi, was the main
Marquesan cosmetic preparation used on skin and hair, in our study the main cosmetic
preparation of Aloe vera L. (70%) juice was also used on skin and hair (Table S1). Our
previous study on plants cultivated in Lithuania and the archival sources have revealed
that Aloe vera L., usually called “the plant of elders”, was very popular to grow at home andwas “always at hand when needed” [26,45]. Due to its special composition of amino acids,
lectin, lipids, minerals, lactates, phenols, etc. and its soothing and cooling properties, aloe
was not only used to improve the condition of the skin but also to treat it. It reduces itchingand swelling, treats light cuts, bruises, eczema, has antibacterial, antifungal properties,
and improves blood flow in the affected areas [46]. In our study, several cosmetic recipes
are presented. For example, for acne, one tablespoon of crushed aloe mixed with threetablespoons of calendula oil. The mixture is applied on the face and left there for 20 min;an aloe mask used for application under the eyes, is made with 1 tablespoon of aloe juice,
1 tablespoon almond oil and half a grated cucumber, everything is mixed and applied
under the eyes. The mixture is left there for 15–20 min. Burns are treated by applying theflesh of a peeled aloe leaf to the affected area. This plant is popular in today’s cosmetic
products [47] and the modern consumer is usually aware of its beneficial properties forskin and hair.
As in treatment of skin diseases, honey, pig fat, eggs, and buttermilk represented themost reported ingredients of animal origin in cosmetic applications. The face was washedwith whey to remove freckles so that the skin was “clear”. The acid of ants was also used
to remove freckles. This acid was acquired by throwing a scarf on an anthill, and when it
dampened, the freckles were rubbed with it. Decoction, juice, and tincture with oil weremost popular ways of preparation (Figure 4). As pointed out by Svanberg I [10], birch sap
was the second most valuable product after wood to be found in the forest. It is renowned
for antioxidative nutrients and high content of minerals. In northern Europe, birch sap haslong been used not only as a food source but also for healing and cosmetics. The cosmetic
use of birch sap was widespread in Estonia, where in the 19th century it was believedthat washing the face with the first drops of birch sap will get rid of freckles and that theface will stay clear all summer. It was also used to treat skin diseases. In Lithuania, it
was popular to rinse hair with birch sap. In this study, birch sap was also used to remove
freckles and treat acne, blackheads, dry skin etc. Among the respondents, it was mentionedthat the juice is frozen to ice and used to treat swollen eyes and to smoothen small wrinkles.
To treat an oily scalp, two tablespoons of honey are mixed with four tablespoons of juice
and a pinch of heated salt. The mixture is then diluted in half a glass of vodka, and itis then kept in a dark place for 10 days. The mixture is then rubbed into the oily scalp
before washing.
The data from our study shows that the same plants are often used for cosmetic
purposes and for the treatment of skin diseases. This distinguishes a separate group of
cosmeceuticals. The term “cosmeceuticals” was first used by Raymond Reed in 1961 andthe word and concept were further popularized by Dr Albert Kligman in the late 1970s [12].
Cosmeceuticals are both cosmetic and pharmaceutical preparations, intended to enhance
health and beauty through ingredients that influence the skin’s biological function. These
are cosmetic products that are not just used for beautification but also for different skinailments, from acne-control and anti-wrinkle effects to sun protection. Medicinal plants
as ingredients act not only as cosmetics, but also as pharmaceuticals [12]. In this study

207

Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW

11 of 14Int. J. Environ. Res. Public Health 2022, 19, 4054

highlighted medicinal plants used for cosmetics and for the treatment of skin diseases(Table S1). According to the results of our study, medicinal plants used for the treatmentof medical disorders and for cosmetic purposes can be singled out as potential cosmeceuwe highlighted
ticals(Figure 7). medicinal plants used for cosmetics and for the treatment of skin diseases(Table S1). According to the results of our study, medicinal plants used for the treatment of
medical disorders and for cosmetic purposes can be singled out as potential cosmeceuticals(Figure 7).

Figure 7. Use categories of medicinal plants.

4. Conclusions
Figure The
7. Usecategories of medicinal
plants.
documentationof ethnopharmacologicalknowledge is important for the preser-

vation of country’s cultural, social, and economic identity. Also, the application of modern
4.methods
Conclusionsof analysis enables the development of pharmaceutical and cosmetic products
The documentationof consumer.
ethnopharmacologicalknowledge
important
for the preseracceptable
to the modern
In terms of publichealth,isnot
only ethnopharmaceutivationof country’s
cultural,social, andidentity.
Also,
the applicationof moderncal knowledge
and
its application
areeconomic
relevant, butalso thesafetyof “self-treatment”
andmethods
analysisthe developmentof pharmaceuticaland cosmetic
recoveryofrituals
for enables
the treatmentof skin diseases(which is especiallyrelevant products
during theacceptable
topandemic).
the modernOur
consumer.
In termsthatof public
health,notindications
only ethnopharmaceuCOVID-19
study showed
only 21%of thefor the use ofticalknowledge and its application
are relevant,also thesafetywithof “self-treatment”
andethnopharmaceuticals
for the treatmentof skin but
diseases
coincide
the EMA assessment.
The useritualsof naturalcosmetics
cosmeceuticals
duringthe COVID-19
pandemicwasrecoveryfor thetreatmentandof skindiseases (whichis especiallyrelevant
during the
popular inpandemic).
Lithuania. Our
Improperof ethnopharmaceuticals
cosmetic purposes
and
COVID-19
studyuse
showedthat only 21% of thefor
indications
for the use
ofin
the treatment of skin for
diseasescan causeofnot
only
aesthetic
but also
healthproblems,
andethnopharmaceuticals
the treatment
skindiseases
coincidewith
the EMA
assesstherefore
ethnopharmacologicalknowledge
must be criticallyand basedon scienment.
The useof natural cosmetics
and cosmeceuticals
duringassessed
the COVID-19
pandemictificresearch.
The
ethnopharmacologicalknowledgeofhealthcare
professionals(especiallywas popular in Lithuania. Improper use of ethnopharmaceuticals for cosmetic purposes
pharmacists)must ofbeskinsufficientfor canthe causepatient
consultation
andbutan alsoadequateandin the treatment
diseasesnotonly aesthetic
healthcare.
problems,
and therefore ethnopharmacological knowledge must be critically assessed and based on
Supplementary
Materials:
The following supporting
information
can be downloadedat: (eshttps:
scientificresearch.
The ethnopharmacologicalknowledgeof healthcare
professionals//www.mdpi.com/article/10.3390/ijerph19074054/s1,
File
S1:
Questionnaire,
Table
S1:
Ethnopharpecially pharmacists) must be sufficient for the patient consultation and an adequate care.
maceuticals for skin diseases and cosmetics in Siauliai district, Lithuania.

Supplementary
Materials:
The
following
information
can be downloadedat:Data
curation,
A.P.;supporting
formal analysis,
R.G.; investigation,
Z.P. and R.G.;
Author Contributions:www.mdpi.com/xxx/s1,
File
S1:
Questionnaire,
Table
S1:
Ethnopharmaceuticals
forskin
diseasesmethodology, Z.P., A.P. and J.B.; supervision, Z.P. and J.B.; writing—original draft, Z.P.; writing—andcosmetics
in Siauliai
district,
Lithuania.
reviewand editing,
A.P.
and J.B.
All authors have read and agreed to the published version of
the manuscript.
AuthorContributions: Data curation, A.P.; formal analysis, R.G.; investigation, Z.P. and R.G.; methodology,
Z.P.,
A.P.,
and J.B.;
supervision,
Z.P.funding.
and J.B.; writing—original draft, Z.P.; writing—reFunding:This
research
received
no externalview and editing, A.P. and J.B. All authors have read and agreed to the published version of theInstitutional Review Board Statement: The study was conducted according to the guidelines of themanuscript.
Declaration of Helsinki, and approved by the Bioethics Center of the LITHUANIAN UNIVERSITY
Funding:
This researchreceived
no externaldata
funding.
OF HEALTHSCIENCES(No. BEC-FF-30,
of approval 16 June 2020).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

208

Int. J. Environ. Res. Public Health 2022, 19, 4054

Data Availability Statement: The data generated for this study are available from the authors
upon request.
Acknowledgments: We thank all the local people who kindly shared their knowledge and lifestyle.
Conflicts of Interest: The authors declare no conflict of interest.

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International Journal ofEnvironmental Researchand Public Health
ArticleCOVID-19 Pandemic Lockdown: An Excellent Opportunity to
Study the Effects of Trawling Disturbance on Macrobenthic
Fauna in the Shallow Waters of the Gulf of Gabès (Tunisia,
Central Mediterranean Sea)
Nawfel Mosbahi 1, *, Jean-Philippe Pezy 2 , Jean-Claude Dauvin 2
1
2*

Citation: Mosbahi, N.; Pezy, J.-P.;
Dauvin, J.-C.; Neifar, L. COVID-19
Pandemic Lockdown: An Excellent
Opportunity to Study the Effects ofTrawling Disturbance on
Macrobenthic Fauna in the Shallow
Waters of the Gulf of Gabès (Tunisia,
Central Mediterranean Sea). Int. J.

and Lassad Neifar 1

Laboratoire de Biodiversité Marine et Environnement, Faculté des Sciences de Sfax, Université de Sfax,
BP 1171, Sfax 3038, Tunisia; lassad.naifar@fss.rnu.tn
Laboratoire Morphodynamique Continentale et Côtière, Normandie University, UNICAEN, CNRS,UMR 6143 M2C, 24 Rue des Tilleuls, 14000 Caen, France; jean-philippe.pezy@unicaen.fr (J.-P.P.);
jean-claude.dauvin@unicaen.fr (J.-C.D.)
Correspondence: nawfelmosbahi@hotmail.fr or nawfel.mosbahi.etud@fss.usf.tn

Abstract: This study describes for the first time in the central Mediterranean Sea the effects of bottom
trawling on macrobenthic fauna in tidal channels of the Kneiss Islands in the Gulf of Gabès, Tunisia.
Following a BACI protocol, two control stations (protected by artificial reefs) and two trawled stations(impacted stations) were sampled during a period with the absence of bottom trawling activity(the COVID-19 pandemic lockdown period from March to May 2020) and during a trawled period.
Although bottom trawling had no impact on sediment composition, this anthropogenic activity
reduced the concentration of dissolved oxygen and had a noticeable effect on water column turbidity.
The absence of trawling led to a significant increase in biomass, number of species, and abundanceof total macrofauna. This illustrated the negative effect of trawling activity in shallow waters andthe high resilience of macrobenthic communities of the tidal ecosystem of the Kneiss Islands. In
the future, it would be very important to control the use of this destructive fishing gear due to its
negative impact on the marine habitat and macrofauna, which represents essential prey for fishes
and birds living in this protected area.

Environ. Res. Public Health 2022, 19,1282. https://doi.org/10.3390/
ijerph19031282

Keywords: bottom trawling; COVID-19 pandemic lockdown; environmental impacts; macrobenthic
fauna; tidal channels; central Mediterranean Sea

Academic Editors: Sachiko Kodera
and Essam A. Rashed
Received: 9 December 2021
Accepted: 17 January 2022
Published: 24 January 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
Bottom trawling is one of the most harmful anthropogenic activities on both shallow
and deep marine ecosystems [1–3]. It is a relatively non-selective fishing method with
global negative impacts on benthic communities and habitats [4–6]. Bottom trawling
provokes sediment re-suspension; decreases macrofaunal bioturbation processes; andremoves, injures, or kills a wide range of sedentary organisms. It also induces changes in
the population demography and can have dire consequences on ecosystem structures andfunctions [7–10].
The Gulf of Gabès, located in the central part of the Mediterranean, covers the secondwidest continental shelf area (35,900 km2 ) and is characterized by unique geomorphological,
climatic, and oceanographic conditions. This gulf has an extensive network of tidal channels and very gentle slopes [11]. The tidal channel environments are of major ecologicalimportance, being considered among the main pathways of passage and migration forseveral commercial marine species (fish and shrimp) and providing important habitats
for the juveniles of many inshore fish species [12,13]. Therefore, the Gulf of Gabès is an
important nursery for several fish species and represents one of the main target areas forfishing activities in Tunisia; moreover, it is among the most highly productive zones in the211

Int. J. Environ. Res. Public Health 2022, 19, 1282

Mediterranean Sea [14]. The favourable geomorphologic and climatic conditions are combined to support one of the most productive ecosystems around the small tidal channels in
the Gulf of Gabès [14,15]. These tidal features are very attractive for many kinds of fishingactivity, especially bottom trawling [16]. Around the Kneiss Islands, tidal channels arevisible only at low tide; this unique system in the Mediterranean Sea represents the highest
energy environment of the Gulf of Gabès and is sensitive to anthropogenic activities andclimate change [17,18]. These conditions favour the circulation of seawater, sediments,organic matter, and nutrients between terrestrial and coastal marine environments [19].
Due to their diversity of birds, the Kneiss Islands have been designated as a ‘SpeciallyProtected Area of Mediterranean Importance’ (SPAMI) in 2001, an ‘Important Bird Area’(IBA) in 2003, and as a ‘RAMSAR site’ since 2007. Nevertheless, despite international
protection, intensive human activities occur in this Marine Protected Area [20]. In the tidal
channels of the Kneiss Islands, fishers use traditional and impacting fishing gear such as
the small bottom trawl known locally as “Kiss”. The Kiss trawlers usually display all of
the characteristics and gears that can be found on regular trawlers: a net with a lead line;
a float line; and a cod end, tied on both sides to otter doors that are usually wooden on
Kiss trawlers. The main differences between a Kiss trawler and a regular one are the size ofthe vessel and those of the net. The mesh size of the Kiss nets is much smaller (18 versus
more than 28 mm) than on a regular trawler, which makes them less selective and liable toproducing large amounts of by-catch. Kiss trawlers are small versions of the regular ones;
they rarely reach 10 m in length, are mainly manufactured with wood, and are equippedwith a power winch and an iron bar on the stern of the boat to tow the net. Illegal vessels
could operate in areas of shallow water (5 m depth or less), destroying sensitive habitats
and spawning grounds, by tearing out the seagrass meadows caused by bottom scraping
metal panels towed by the trawler [21].
Such gear unfavourably affects the juveniles of fish and shrimps as well as sensitive
benthic habitats such as Posidonia oceanica meadows [7,22]. In October 2018, a team ofinspectors counted the vessels equipped with unauthorized bottom trawls to estimate thetotal number of illegal trawlers operating over the whole Gulf of Gabès. Between 400 and
500 of such trawlers were identified in the main ports around the Gulf of Gabès [21]. It isvery difficult to recognize and separate illegal Kiss trawlers from other trawlers, as mostof them do not display their identification numbers or VMS (vessel monitoring system)
used to monitor the location and the activities of commercial fishing vessels required under
Tunisian legislation, making legal pursuit and law enforcement harder. We estimatedthat, around the Kneiss Islands, in the central part of the Gulf of Gabès, approximately
100 vessels carry out trawling activities every day in tidal channels. These vessels camefrom the ports of Khawala, Skhira, and Mahres. They were small vessels (7 to 10 m oflength) equipped with mini trawls to catch target species such as the shrimp Metapenaeus
monoceros (Fabricius, 1798), several fish species, including Sparus aurata Linnaeus, 1758;
Diplodus spp.; Solea spp.; cephalopods Sepia officinalis Linnaeus, 1758; and recently, theexotic invasive blue crab Portunis segnis (Forskål, 1775).
To study anthropogenic pressures and to assess their effects, ecologists and stakeholders have stressed the key role of the macrofauna in establishing the ecological quality
status of benthic communities [23,24] and in assessing the biological integrity of marine
systems [25,26]. In fact, benthic invertebrates are considered potentially powerful indicatorsof marine ecosystem health [24]. Due to their life at the sediment–water interface and theirshort life spans, these organisms can record both accidental and chronic perturbations inmarine environments [27–29].
Coronavirus Disease 2019 (COVID-19) is the official name of a respiratory infectious
disease caused by a new coronavirus that was first reported in Wuhan, China, and thenspread unexpectedly fast across the world on 11 March 2020, when the World HealthOrganization (WHO) stated the COVID-19 outbreak as a “pandemic public health menace” [30,31] due to its worldwide spread. Since February 2020, affected countries havelocked down their cities and industries to restrict the movement of their citizens and to212

Int. J. Environ. Res. Public Health 2022, 19, 1282

minimize the spread of the virus [30,32]. Despite the negative aspects of coronavirus onhuman health, this crisis had a positive impact on the natural environment: improvements
in air and water quality, and reductions in industrial effluents and in the discharge of otherwastes [33,34]. There had also been a decline in the exploitation of natural resources anda strong rise in the successful recruitment of several marine species observed in certain
perturbed ecosystems (harbours, industrial coastal zones, etc.), which was attributed tolowered anthropogenic pressures [35,36]. These might be short-term improvements, butthey highlighted the severity of anthropogenic impacts worldwide.
In the Gulf of Gabès, a major part of this scientific work was carried out to assess theinteraction between fishing with bottom trawling, and vulnerable and protected animals
such as sharks, rays, and marine turtles. Data on the impacts of bottom trawling on
macrobenthic fauna in the Gulf of Gabès are few and even rare, because a reference period isrequired to study anthropogenic activities to understand their environmental effects. Many
human activities have developed around the Kneiss Islands, including bait digging [20]and clam harvesting [37], and several research protocols have been implemented to study
the effects of each activity (see [20,37]).
This work highlights the response of macrobenthic communities when faced withthe cessation of destructive fishing activities during the COVID-19 pandemic lockdown
period. Therefore, this study was carried out using a BACI (Before After Control Impact)
approach, exploiting the COVID-19 pandemic lockdown period to evaluate the impactsof bottom trawling on soft-bottom macrobenthic communities of the tidal channels of theKneiss Islands in order to set up an effective management plan for artisanal fishing activity
in this subtidal ecosystem of the central Mediterranean Sea.
2. Materials and Methods
2.1. Study Area
Located in the northwestern part of the Gulf of Gabès, between latitudes 34◦ 100 N and34◦ 300 N and between longitudes 10◦ E and 10◦ 300 E, the Kneiss islands are characterized

by the highest tidal range in the western Mediterranean Sea; the tide is semi-diurnal with
an amplitude varying from 0.8 to 2.3 m [11]. Tidal currents have created shallow channels
in the Gulf of Gabès that are extensively developed around the Kneiss islands. In theseecosystems, sediments are mainly composed of sand and coarse sand; the sedimentary
filling of the tidal channels shows decreasing grain size from downstream to upstream,
indicating that the action of tidal currents is stronger during a flood tide than during an ebb
tide [19]. Moreover, sediments in the shallowest waters are mainly characterized by sand,whilst gravel is found at intermediate-depth stations and deeper stations are dominatedby fine sediment including silt [16]. Intensive human activities take place in this protectedarea of the Kneiss Islands [16,20,37].
2.2. Sampling and Laboratory Procedures
2.2.1. Macrobenthic Sampling and Measurement of Physicochemical Variables
Benthic macrofauna sampling was performed between March and June 2020, andcarried out in the tidal channels of the Kneiss Islands at two trawled stations (i.e., stations
I1 and I2) in zones where the fishers used bottom trawling and at two other control
stations (stations C1 and C2: located in a protected zone preserved by artificial reefs
since 2014) (Figures 1 and 2). Five sampling campaigns were organized according to
a BACI strategy: one during March before the lockdown period, another at the end of
the lockdown period (total cessation of trawler fishing activity controlled by the coast
guard and the national navy), and three after the lockdown period (Figure 2). Benthic
macrofauna sampling was carried out using a Van Veen grab covering an area of about
0.05 m2 , which penetrated approximately 0.1 m into the sediment. The station positions
were accurately determined using a GPS (Global Positioning System, WGS84). For eachsampling campaign, five replicates were carried out at each station: four for biologicalanalysis covering a total surface-area of 0.2 m2 and one for sediment analysis. Each213

lockdown period (total cessation of trawler fishing activity controlled by the coast guard
and the national navy), and three after the lockdown period (Figure 2). Benthic
macrofauna sampling was carried out using a Van Veen grab covering an area of about
0.05 m2, which penetrated approximately 0.1 m into the sediment. The station positions
were
accurately determined using a GPS (Global Positioning System, WGS84). For eachInt. J. Environ. Res. Public Health 2022,
19, 1282
sampling campaign, five replicates were carried out at each station: four for biologicalanalysis covering a total surface-area of 0.2 m2 and one for sediment analysis. Each biological sample was sieved through a 1 mm mesh, fixed with buffered 10% formaldebiological sample was sieved through a 1 mm mesh, fixed with buffered 10% formaldehyde,
hyde, and stained with Rose Bengal to facilitate sorting. In the laboratory, prior to idenand stained with Rose Bengal to facilitate sorting. In the laboratory, prior to identification,tification, the samples were washed and the organisms were hand-sorted into major
the samples were washed and the organisms were hand-sorted into major taxonomic
taxonomic groups, identified to the lowest practical taxonomic level (usually species
groups, identified to the lowest practical taxonomic level (usually species level), and thenlevel), and then counted. Biomass was obtained as ash-free dry weight (g AFDW) after
counted. Biomass was obtained as ash-free dry weight (g AFDW) after drying (60 ◦ C for
drying (60 °C for 48 h) and◦ calcination (500 °C for 5 h). Species names were checked us48 h) and calcination (500 C for 5 h). Species names were checked using the World Register
ing
World
Registerof Marine Species list (http://www.marinespecies.org
accessedonInof theMarineSpecieslist (http://www.marinespecies.org
accessed on 20 November2021).
20addition,
November2021).
In
addition,severalenvironmental
factorsweremeasuredinsitu
several environmental factors were measured in situ such as depth (m), turbidity
such
as depthturbidity(measuredby Suspended
Solidsmg◦ C), salinity(measuredby (m),
Suspended
SolidsConcentration“SSC”; mgL−1 ),Concentration
temperature (T“SSC”;
L−1),
temperature(T°C),
salinity(measured
in
situ
by
WTW
multi-meter)anddissolved(measured in situ by WTW multi-meter) and dissolved oxygen concentration (by WTW−1oxygenconcentration
oximeter;mg L−1 ). (by WTW oximeter; mg L ).

Figure1.1.Map
Mapofofthe
thestudy
studyarea
areashowing
showingthe
thelocations
locationsofofthe
thesampling
samplingstations
stations(I1(I1andandI2:I2:trawled
trawledFigurezones;C1C1andandC2:
C2:control
controlstations).
stations).
zones;

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5 of 18

Figureused inin thisthisstudy
studyduring
duringthe
thefive
fivesampling
samplingperiods,
peFigure2.2.Representation
Representationofof thethe sampling strategy usedriods,
withbefore
P1: before
lockdown;
P2: during
lockdown;
after
days;
after
two
weeks;
P5:onewith P1:
lockdown;
P2: during
lockdown;
P3: P3:after
twotwodays;
P4:P4:
after
two
weeks;
P5:
one
month
after
the
lockdown;andBT:bottomtrawling.
month after the lockdown; and BT: bottom trawling.

2.2.2.
Content
2.2.2.Sediment
SedimentGrain-Size
Grain-SizeAnalysis
AnalysisandandOrganic
OrganicMatter
MatterContent
The
fromeacheachsampling
sampling
periodhomogenized
and wet-sieved
The sediment fromperiodwaswashomogenized
and wet-sieved
throughthrough
63 μm
to separate(including
andgravely,clay), gravely,
and fractions
sandy
a 63 µmamesh
tomeshseparate
muddy muddy(including
silt andsiltclay),and sandy
fractionson theAfter
sieve).being
Afteroven-dried
being oven-dried
to constantat sediment
60 °C,(retained(retainedon the sieve).
to constantweight weightat 60 ◦ C,
sediment
were separated
a mechanical(columnof sieves
five sieves
fractionsfractions
were separatedusing ausing
mechanicalshakershaker(columnof five
withwithmesh
meshof 1000,
500, 125,
250, and
125, 63andµm)
63 μm)
formin.
10 min.
All fractions(including
μm)
sizes sizesof 1000,
500, 250,
for 10All fractions(including<63<63
µm)
were
were
weighedto determine
percentage
relative
abundance.
Fororganic
the organic
then thenweighedto determine
their theirpercentage
relative
abundance.
For themattermatteranalyses,sediment
were°C to weight
constantand
weightandto acontentcontent
analyses,sediment
samplessamples
were driedat dried
60 ◦ C at
to 60
constant
groundgroundto a fine
powder.
Organic
matterwas determinedon thepowdersam-onfine powder.
Organic
mattercontent
wascontent
determinedon the powder
samplesby ‘loss◦ C for 4 atplesby ‘losson ignition’ignition’at 450h. 450 °C for 4 h.

2.3.Statistical
StatisticalAnalyses
Analyses
2.3.
Univariate andand multivariate
usedto evaluate
the changes
in the structure
Univariate
multivariateanalyses
analyseswere
were
used
to evaluate
the changes
in theand compositionof macrobenthic
assemblages
due to bottom
Wetrawling.
consideredstructureand compositionof macrobenthic
assemblages
duetrawling.
to bottom
Wethe
numberoftaxa,
abundance,andbiomass
perstation.
These
biological
data
were
used
considered the number of taxa, abundance, and biomass per station. These biological to2 ) and the most common
calculate,eachtostation,
the atabundance(ind.them−abundance
data
were atusedcalculate,each station,(ind. m−2) and biodiversity
the most
2 ), biomass (g AFDW m−2 ),indices,i.e.,taxonomicrichness(number
of
taxa
per
0.2
m
common biodiversity indices, i.e., taxonomic richness (number of taxa per 0.2 m²), bio−2), Shannon
Shannonindexm(H’)[38], and index
Pielou’sevenness[39]. evenness (J’) [39].
mass(g AFDW(H’)[38], and(J’)
Pielou’sLog (x(x ++ 1)to tothetheabundance
matrix(data
for eachstationAALog1)transformationtransformationwas
wasapplied
appliedabundance
matrix(data
for eachwere pooledprior topriorundertaking
furtherfurther
analyses)
to minimize
the influenceof the moststationwere pooled
to undertaking
analyses)
to minimize
the influenceofdominant
taxa, before
calculating
the Bray–Curtis
similarities
using the using
statisticalpackagethemost dominanttaxa,
before calculating
the Bray–Curtis
similarities
the statisPRIMER[40].PRIMER
A dendrogram
was created waswithcreatedgroup withaverages
expressed
the cluster
tical
package[40]. A dendrogram
group
averagesinexpressed
mode.
Then,
a
non-parametricmulti-dimensional
scaling(n-MDS)
ordination
wasappliedin the cluster mode. Then, a non-parametric multi-dimensional scaling (n-MDS) ordinato
theabundance
matrixusing
theBray–Curtis
similarity
measure,withtheobjective
tion was applied to the abundance matrix using the Bray–Curtis similarity measure, of
examining
the structureof thethemacrobenthic
Theassemblages.
SIMilarity PERcentages
withthe objectiveof examining
structure of assemblages.
the macrobenthic
The SIM(SIMPER)
routine wasappliedroutine
to establish
which species
contributed
to theobserved
ilarity
PERcentages(SIMPER)
was applied
to establish
which most
species
contribut® version
differencesin thedata. differences
Analyses ofinthe
were
performed
PRIMERed
most to theobserved
thedatadata.
Analysesof theusing
data were
performed
6 (Plymouth Routines in Multivariate Ecological Research) package [40]. Permutational
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multivariate analysis of variance (PERMANOVA) was also conducted to test the significant
differences in macrobenthic fauna composition in response to bottom trawling (one-way
analysis) [41].
For the biological parameters, a Shapiro–Wilk normality test and a Bartlett’s testfor homogeneity of variance were performed prior to each ANOVA to check whether
the assumptions of ANOVA were met and if data transformation was necessary. Then,
three-way ANOVAs were used to assess the spatiotemporal effect of bottom trawling on
taxonomic, abundance, and biomass of macrofauna benthic between sample stations andduring each sampling campaign (before lockdown; during lockdown; and after two days,
two weeks, and one month of deconfinement). A Tukey Honestly Significant Difference
test was employed to determine differences before and after trawling period as well as
between the different sediment types. These statistical procedures were performed using
the R software vegan package.
3. Results
3.1. Environmental Variables
There were only a very small proportion of fine particles (<63 µm) in the sediment,
representing between 2 and 3.4% at stations I2, C1, and C2 and reaching 16% at I1. The
sediment was dominated by coarse sand (500–2000 µm) (68 to 82%), except at I1, which was
dominated by medium sand (74%). The organic matter content varied between 2.6% (C2)and 4.1% (I1) (Table 1). No significant statistical changes could be identified in sedimenttype and organic matter content between sampling stations and between dates (in bothcases; p > 0.05), showing that the sediment variables were stable throughout the BACI
study period from March to June 2020.
Table 1. Mean environmental characteristics of the four sampling stations during the five campaignsof the March–June period.
Coordinates
Latitude (N)Longitude (E)

Depth
(m)

OM
(%)

Q50
(mm)

Sandy (%)

Silt-Clay(%)

I1

38.00974

32.614844

5.60

4.1

0.79

I2
C1
C2

37.98597
37.96937
37.95854

32.615700
32.615471
32.614429

8.30
7.40
6.50

3.8
3.6
2.6

1.11
1.21
1.14

83.5 ± 3.62

16.0 ± 5.18

Stations

97.3 ± 4.10
96.8 ± 3.12
95.8 ± 2.64

2.6 ± 6.22
3.2 ± 4.60
3.4 ± 1.42

SedimentTypemediumsandcoarse sandcoarse sandcoarse sandFigure 3 showed the variation in physicochemical variables measured during thesampling period. The dissolved oxygen and the turbidity were significantly different(ANOVA; F = 1.38; p < 0.01; F = 2.12; p < 0.01, respectively) between the control and impacted
stations during the five trawling periods (P1, P3, P4, and P5; illustrated in Figure 2), withhigher turbidity values in the trawling stations (I1 and I2) and higher dissolved oxygen
values at the control stations (C1 and C2). The impacted stations exhibited lower values ofdissolved oxygen and higher values of SSC compared with the control stations during the
trawling period and particularly at the last date of sampling. Conversely, no significant
differences were identified for temperature and salinity between the stations during thefive sampling periods for the both environmental variables (p < 0.05) (Table 1).

216

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7 of 18

Figure3. Physicochemicalparameters(± SD)
duringthe fivesampling
periods,
P1:March;
15 March;
Figure3. Physicochemicalparameters(± SD)
duringthe fivesampling
periods,
withwith
P1: 15
P2: 21 April; P3: 6 May; P4: 18 May; and P5: 3 June 2020, were (a) temperature ◦(°C), (b) salinity, (c)P2: 21 April; P3: 6 May; P4: 18 May; and P5: 3 June 2020, were (a) temperature ( C), (b) salinity,
dissolved oxygen (mg L−1), and (d) turbidity (Suspended solid concentrations; mg L−1).
(c) dissolved oxygen (mg L− 1 ), and (d) turbidity (Suspended solid concentrations; mg L− 1 ).

3.2. 3.2.
GeneralCompositionof theofBenthic
Macrofauna
GeneralComposition
the Benthic
Macrofauna
In total,
48464846
macrofaunal
individuals
wereidentified
belonging
to to
7272
taxa
and
In total,
macrofaunal
individuals
wereidentified
belonging
taxa
and 6
6 zoologicalgroupsunequally
distributed
among
the thesampling
stations.
Annelidpolyzoologicalgroupsunequally
distributed
amongsampling
stations.
Annelidpolychaetes
wereweredominant(40%(40%of thenumberof taxa),
followedby crustaceans(34%),
chaetesdominantof total
the total
numberof taxa),
followedby crustaceans(34%),
mainlyamphipods,
decapods,
andandisopods,along
withwithmolluscs(21%),
mainlybivalves
mainlyamphipods,
decapods,
isopods,alongmolluscs(21%),
mainlybivalves
and andgastropods.
The othercnidarians,
and tunicates)
gastropods.
The threeotherphyla
three(echinoderms,
phyla (echinoderms,
cnidarians,
and accounted
tunicates) acfor only
5% oftheonly
total5%
numbertaxa.number
Similarly,
the polychaetes
dominatenumericallycountedforof theoftotalof taxa.
Similarly, thepolychaetes
dominate(42%numericallyof the total (42%abundance)withthespeciesCirratulus
cirratus(O.
F.
Müller,1776),(O.
Euclyof the total abundance) with the species Cirratulus cirratus
F. Mülmeneler,
lumbricoides(Quatrefages,
1866),(Quatrefages,
Amphitritides 1866),
gracilisAmphitritides(Grube, 1860),
and Nereis
spp.
1776), Euclymene
lumbricoides
gracilis(Grube,1860),
showing
the highest
number theof individuals.
and Nereis
spp. showing
highest number of individuals.
3.3. Impact of Bottom Trawling on the Macrofauna
3.3. Impact of Bottom Trawling on the Macrofauna
Before the lockdown period (15 March 2020), the number of taxa and abundances in
Before the lockdown period (15 March 2020), the number of taxa and abundances in
the four sampled stations were significantly different, with higher values recorded in thethe four sampled stations were significantly different, with higher values recorded in thecontrol stations than in the impacted stations (F = 3.9; p < 0.01; F = 2.1; p < 0.01, respectively).
control stations than in the impacted stations (F = 3.9; p < 0.01; F = 2.1; p < 0.01, respecDuring the lockdown period (21 April 2020), when there were no trawling activities, thetively). During the lockdown period (21 April 2020), when there were no trawling activinumber of taxa and abundance increased at both impacted stations. After lockdown,ties, the number of taxa and abundance increased at both impacted stations. After lockabundances and numbers of taxa at both control stations were higher after two days, twodown, abundances and numbers of taxa at both control stations were higher after two
weeks, and one month of the period after confinement compared with the impacted stations
days, two weeks, and one month of the period after confinement compared with the(Figure 4; Table 2).
impacted stations (Figure 4; Table 2).
Table 2. Proportion of zoological groups (A: % total abundance; B: % total biomass), the mean taxa
number, mean abundance, and mean biomass recorded at the two impacted (I) and two control stations (C) during the five sampling periods: The mean taxa number (number of taxa per 0.2 m2 ± SD);the mean abundance (number of individuals per m2 ± SD); the mean biomass (g AFDW per m2 ± SD);
BT: bottom trawling and others groups (echinoderms, cnidarians, and tunicates).

Polychaetes

Presence of BT
Activity
Before Lockdown

Absence of BT
Activity
Lockdown

After Two Days

After Two Weeks

C

C

C

C

I

Deconfinement; Presence of BT Activity

I

I

After One Month

I

C

I

A

B

A

B

A

B

A

B

A

B

A

B

A

B

A

B

A

B

A

B

47

35.1

27.0

14.4

42

34.3

37

24.9

36

35.4

30

21.8

43

31.7

26

16.2

49

31.3

19

13.6

217

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Table 2. Cont.

Crustaceans
Molluscs
Others groups

A
27
22
4

Presence of BT
Activity
Before Lockdown

Absence of BT
Activity
Lockdown

After Two Days

After Two Weeks

C

C

C

C

I
B
34.3
27.2
3.4

A
44
27
2

B
51.3
32.2
2.1

A
33
21
4

I
B
35.1
28.1
2.4

A
35
25
3

Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW
Mean taxa number
Mean abundance
Mean biomass

35.0 ± 3.02762 ± 202
113 ± 20

11.0 ± 2.1
1256 ± 112
64 ± 9Deconfinement; Presence of BT Activity

41.5 ± 3.6
3187 ± 348
186 ± 22

B
42.8
28.8
3.5

33.0 ± 4.2
2807 ± 211
124 ± 12

A
38
24
2

I
B
34.5
28.3
1.8

36.0 ± 2.42856 ± 262
194 ± 24

A
39
30
1

B
42.4
34.6
1.2

25.5 ± 2.6
1923 ± 104
102 ± 13

A
31
23
3

After One Month

I
B
36.0
30.3
2.0

36.5 ± 3.2
2821 ± 301
198 ± 14

A
42
32
0

C
B
50.4
33.4
0

17.0 ± 1.81112 ± 96
68 ± 8

A
26
22
3

I
B
38.3
27.2
3.2

34.5 ± 3.42634 ± 182
211 ± 14

A
42
39
0

B
54.2
32.2
0

8 of 189.5 ± 2.2
958 ± 62
49 ± 6

Figure 4. Benthic community parameters: taxonomic richness (a), abundance (b), Shannon–Wiener
Figure4. Benthic
community
parameters:
taxonomicrichness
abundance(b), Shannon–Wiener
index (c),and Pielou’s
evenness(d) with standard
deviationfor (a),all stations
sampledbefore lockdown;
index
(c),
and
Pielou’sevenness(d)withstandard
deviationforallstations
sampledduring lockdown; and after two days, two weeks, and one month after confinement. before lockdown; during lockdown; and after two days, two weeks, and one month after confinement.

H’ and J’ varied significantly between stations (F = 2.18; p < 0.01; F = 3.11; p < 0.01,

Polychaetes
Crustaceans
Molluscs
Others groups
Mean taxa number
Mean abundance
Mean biomass

Table
2. Proportion
zoologicalgroups(A: %oftotalabundance;
B: % total biomass),
the meantaxa
respectively)(Tableof3),with higher
valuesthese
indices corresponding
to control
stations−1 ; J’= 0.95
number,mean
abundance,and (H’=mean3.6
biomassrecordedat theintwo(I) and two control
during the
lockdown
periodbits.ind
C2)impacted(Figure 4).
stations (C) during the five sampling periods: The mean taxa number (number of taxa per 0.2 m² ±2SD);(number
individuals
per time
m² ± (5SD);
the mean
Tablethe3. meanResultsabundanceof the ANOVAs
withofthe
three factors:
levels;df = 4),biomass
trawling(g(2AFDW
levels; per
df =m
1),
± SD); BT: bottom trawling and others groups (echinoderms, cnidarians, and tunicates).
and site (4 levels; df = 3) for the five biological variables (*: significant variation).
Presence of BT
Absence of BT
Deconfinement;
FactorF Presence of BT ActivitypActivity
ActivityTime
3.9Two Weeks<0.01
Before Lockdown
Lockdown
After Two Days
After
After
One* Month
Richness
species
SiteC
1.14
C
I
C
I
I
C
I
C<0.01 *I
Trawling
0.23
0.01
*
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
Time
2.9
<0.01 *
47
35.1 27.0 14.4 42 34.3 37 24.9 36 35.4 30 21.8 43 31.7 26 16.2 49 31.3 19 13.6
AbundanceSite
1.21<0.01 *
27
34.3 44 51.3 33 35.1 35 42.8 Trawling
38 34.5 39 42.4 31 0.84
36.0 42 50.4 26 <0.01
38.3 *42 54.2
22
27.2 27 32.2 21 28.1 25 28.8 24
30.3 32 33.4 22 27.2
Time28.3 30 34.6 23 21.4
0.01 * 39 32.2
4
3.4
2
2.1
4
2.4 3
3.5
2Site 1.8
1
1.2
3 0.862.0
00
3 <0.01
3.2 * 00
Biomass
35.0 ± 3.0
11.0 ± 2.1 41.5 ± 3.6 33.0 ± 4.2 Trawling
36.0 ± 2.4 25.5 ± 2.6 36.512.2± 3.2 17.0 ± 1.8 34.5 ±0.01
3.4 * 9.5 ± 2.2
Time± 262 1923 ± 104 28212.11
2762 ± 202 1256 ± 112 3187 ± 348 2807 ± 211 2856
± 301 1112 ± 96 2634 <0.01± 182 * 958 ± 62
113 ± 2064 ± 9186 ± 22 124 ± 12 194 ± 24 102 ± 13 198 ± 14
68 ± 8211 ± 1449 ± 6

H’ and J’ varied significantly between stations (F = 2.18; p < 0.01; F = 3.11; p < 0.01,
respectively) (Table 3), with higher values of these indices corresponding to control stations during the lockdown period (H’= 3.6 bits.ind−1; J’= 0.95 in C2) (Figure 4).
Table 3. Results of the ANOVAs218
with the three factors: time (5 levels; df = 4), trawling (2 levels; df =1), and site (4 levels; df = 3) for the five biological variables (*: significant variation).

Int. J. Environ. Res. Public Health 2022, 19, 1282

Biomass
Table 3. Cont.

H’
H’

J’
J’

Trawling
Time
Site
Trawling
Time
Site
Factor
Trawling
Site
Time
Trawling
Site
Time
Site
Trawling
Trawling

0.84
21.4
0.86
12.2
2.11
2.18
F
1.14
2.18
2.41
1.14
3.11
2.41
3.11
1.04
1.04

<0.01 *
0.01 *<0.01 *
0.01 *<0.01 *
<0.01
p *0.01 *<0.01 *
0.01
0.01 **<0.01
0.01 **<0.01
0.01 **
0.01 *

During the lockdown period, the four stations were dominated by polychaetes(~40%),
followedby crustaceansmolluscs.
the trawling
period, the (~40%),
faunalDuringthe lockdown
period, andthe fourstationsDuring
were dominatedby polychaetes
composition
varied between
the control
and thestations,the
with
lower
values infollowed by crustaceans
and molluscs.
Duringtheimpacted
trawling period,
faunalcomposition
theimpacted
stations.
The andabundanceof dominant
suchvalues
as theininfaunal
polyvaried
betweenthe control
the impacted
stations,species
with lower
the impacted
chaetescirratusof(O.F.
Müller,1776),such
Euclymene
oerstedii (Claparède,thestations.Cirratulus
The abundance
dominant
speciesas the infaunal
polychaetes 1863),
Cirratulus
cirratus (O.F.
Müller, 1776),anomalus
Euclymene
oerstedii1843),(Claparède,the Scrobicularia
amphipod Microdeutoamphipod
Microdeutopus(Rathke,and the1863),
bivalve
plana (dapus anomalus(Rathke, 1843),
andthetrawling
bivalve periods
Scrobicularia
plana (daCosta,
1778) decreased
Costa,
1778) decreased
duringtheat trawledsites
comparedwith theduring the
trawling
periods atthe
trawledsites comparedwithspecies
the control
In contrast,
controlstations.
In contrast,
abundancesof the latest
werestations.
higher duringthethe abundances
the even
latesttwo
speciesduringlockdown
and eventwo
lockdown
periodofanddayswere
afterhigher
the endof thethe
lockdown
in period
all sampled
stations
days after(Figure5). the end of the lockdown in all sampled stations (Figure 5).

Figure 5.
5. MeanMean abundance± SD;
species
at control
andandimpactstations
Figureabundance((±
SD; nn==2)2)forforsomesomerepresentative
representative
species
at control
impactstaduring
the fivesampling
periodsperiods(before lockdown;
during lockdown;
and after two
tions
duringthefive sampling(before lockdown;
during lockdown;
and days,
after two
twoweeks,days,
and weeks,
one monthdeconfinement).
White column:
controlstations;
blackstations;column:black
impacted
stations.
two
andof
one
month of deconfinement).
White
column:controlcolumn:impacted stations.

Several sedentary organisms such as the porifera; echinodermata; cnidaria; and the
tubicolous(including such
the Sabellidae
Sabellaechinodermata;pavonina Roule,1896 and
Severalpolychaeta
sedentary organisms
as the porifera;
cnidaria;
andBranthe
chiomma bombyx(Dalyell,1853); theTerebella
lapidaria
Linnaeus,
1767Branandtubicolous
polychaeta(including
the Terebellidae
Sabellidae Sabellapavonina
Roule,
1896 andAmphitrite
rubra (Risso,1826);
andthetheTerebellidae
Serpulidae Serpula
vermicularis
Linnaeus,1767
1767 andandchiommabombyx(Dalyell,1853);Terebella
lapidaria Linnaeus,
Hydroides
dianthus(Verrill,
1873))
only
appearedattrawledstations
duringthelockdown
Amphitrite rubra (Risso, 1826); and the Serpulidae Serpula vermicularis Linnaeus, 1767 andperiod andafter twodays1873))
at the only
end appearedof the lockdown,
andthen disappeared
after two
Hydroides
dianthus(Verrill,
at trawled
stations
during the lockdown
weeks
later
(18
May
2020).
The
ANOVA
test
showed
that
there
were
significant
period and after two days at the end of the lockdown, and then disappeared afterdiffertwo
ence between
recordedthetestfiveshowedsampling
periods
at all stations(ANOVA;
weeks
later (18biomass
May 2020).
The during
ANOVA
that
there weresignificant
differdf = 4;
F = 21.4;biomass
p < 0.05)recorded(Table 3).during
The control
stations
showedhigherbiomass
comparedence
betweenthe fivesampling
periods
at allstations(ANOwith both trawled stations during the five sampling campaigns (ANOVA; df = 1; F = 12.2;
p < 0.01). The biomass of macrobenthic communities was higher at the control stations
during the four fishing periods compared with the trawled stations (I1 and I2). During
Int. J. Environ. Res. Public Health 2022, 19, x. https://doi.org/10.3390/xxxxx
the lockdown period (P2), the biomass increased at the impactedwww.mdpi.com/journal/ijerph
stations simultaneouslywith the increase in abundance. Equally, the biomass of the main zoological group showed219

compared with both trawled stations during the five sampling campaigns (ANOVA; df =1; F = 12.2; p < 0.01). The biomass of macrobenthic communities was higher at the control
stations during the four fishing periods compared with the trawled stations (I1 and I2).
During the lockdown period (P2), the biomass increased at the impacted stations simultaneously
Int. J. Environ. Res. Public Health 2022,
19, 1282 with the increase in abundance. Equally, the biomass of the main zoologicalgroup showed significant variation during the five sampling periods (ANOVA; df = 4; F =112.4; p < 0.05). The biomass of polychaetes recorded at trawled stations gradually decreased
withvariation
the increasein theof trawlingdays(see Tablesignificant
duringthenumberfive sampling
periods(ANOVA;df 2).
= 4; F = 112.4; p < 0.05).
dendrogramand n-MDS(Figure6) showed
a remarkableseparation
TheThebiomassof polychaetes
recordedat trawled
stations
gradually spatial
decreasedwith thebetweensampledstationsin all days
three(seegroups.
increasethein thenumberof trawling
TableThe2). first group (GI) corresponded toboth control
stations sampledduring(Figure
the fiveplus both impacted
stations
The dendrogramand n-MDS6) campaigns
showed a remarkablespatial separation
sampledduringthelockdown
period.
Thesecondgroup(GII)corresponded
tothetotwo
between the sampled stations in all three groups. The first group (GI) corresponded
both
impacted
stations
sampled
after
only
two
days
of
deconfinement(resumptionofcontrol stations sampled during the five campaigns plus both impacted stations bottom
sampledtrawling
activity)(I1d period.
and I2d).
Thelast groupgathered the to
impacted
stations
during the
lockdown
Thesecondgroup (GIII)(GII) corresponded
the two impacted
sampledlockdown,
after
two
weeks
and after one monthof deconfinement.
stationsbefore
sampledafter only
twodays
of deconfinement(resumptionof bottom trawling
activity) (I1d and I2d). The last group (GIII) gathered the impacted stations sampled before
lockdown, after two weeks and after one month of deconfinement.

Figure6.6.Dendrogram
Dendrogram(a)(a)andand n-MDS
n-MDS ordination
similarities
fromabundance
Figureordination plot
plot(b)(b)ofofBray–Curtis
Bray–Curtis
similarities
fromabundata
(Log
(x
+
1)
transformation)for
impacted
(I1
and
I2)
and
control(C1
andC2)
sampling
beforedance data (Log (x + 1) transformation) for impacted (I1 and I2) and control (C1 and C2) sampling
lockdown(b);duringlockdown
(l);
and
after
two
days
(d),
two
weeks
(w),
and
one
month
(m) ofbefore lockdown (b); during lockdown (l); and after two days (d), two weeks (w), and one month(m)ofdeconfinement.
Threegroupsofstations
areidentifiedbyclusteranalysis
ata50%
similarity
deconfinement. Three groups of stations are identified by cluster analysis at a 50% similarity level.
level.

SIMPER was used here to illustrate the biological reasons for the clustering of stations
with(before wasand used
after lockdown)
and withoutbottom trawling
activity(during lockdown
SIMPER
here to illustrate
the biologicalreasons forthe clusteringof staperiod)4) by
the groupandsimilarity
identifying
species contributing
most
tions
with(Table(beforeandshowing
after lockdown)
withoutbottom trawling
activity (during
to the similarity between groups. The first group (GI; 84.54% contribution to total similarity) was strongly represented by the polychaetes Cirratulus cirratus, Euclymene oerstedii,
Hediste diversicolor, Amphitritides gracilis, and Euclymene lumbricoides and the amphipods
Int. J. Environ. Res. Public Health 2022, 19,x. https://doi.org/10.3390/xxxxx
www.mdpi.com/journal/ijerph
Monocorophiumacherusicum and Microdeutopus anomalus. The second
group (GII; 59.43%)
was represented by the polychaetes Cirratulus cirratus, Hilbigneris gracilis, and Perinereis
cultrifera; the molluscs Cerithium scabridum, Scrobicularia plana, and Tricolia speciosa; and theamphipods Monocorophium insidiosum and Dexamine spinosa. The last group (GIII; 27.82%)
corresponded to the impacted stations sampled before lockdown and after two weeks
and then one month of deconfinement. This group was represented by the amphipods

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Int. J. Environ. Res. Public Health 2022, 19, 1282

Microdeutopus gryllotalpa, Cymadusa filosa, and Dexamine spinosa and the molluscs Gibbula
ardens, Loripes orbiculatus, and Calliostoma zizyphinum (Table 4). The PERMANOVA analysis
showed that the three groups are statistically highly separated (PERMANOVA; F = 23.2;
p < 0.01).
Table 4. Groups formed by n-MDS ordination, with indication of similarities between each group(%) and the most representative species (% of contribution for the similarity within the group)
contributing to the similarity within the group, determined with SIMPER analysis.

Similarity (%)

Main species

GI

GII

GIII

64.54
Cirratulus cirratus—52.3
Euclymene oerstedii—46.0
Hediste diversicolor—38.1
Amphitritides gracilis—34.2
Leucothoe incisa—28.2
Eulymene lumbricoides—26.6
Monocorophium acherusicum—22.7
Microdeutopus anomalus—18.6
Scrobicularia plana—11.3
Melinna palmata—7.0
Heteromastus filiformis—4.2

49.43
Cirratulus cirratus—48.6
Hilbigneris gracilis—44.9
Perinereis cultrifera—42.0
Cerithium scabridum—37.0
Euclymene oerstedii—26.7
Scrobicularia plana—21.0
Melita palmata—18.2
Monocorophium insidiosum—16.1
Marphysa sanguinea—10.2
Tricolia speciosa—8.6
Dexamine spinosa—3.2

27.82
Microdeutopus gryllotalpa—51.6
Cymadusa filosa—48.2
Dexamine spinosa—41.0
Gibbula ardens—32.2
Tellina tenius—28.1
Tritia cuvierii—21.4
Gammarus insensibilis—20.8
Euclymene oerstedii—19.4
Loripes orbiculatus—17.3
Calliostoma zizyphinum—15.2
Pinctada (imbricata) radiata—7.1

4. Discussion
Bottom trawling has global negative impacts on marine biodiversity and habitats andis already considered among the anthropogenic pressures that threaten marine environments [5,6,8,10,42]. This destructive fishing activity is very widespread in the Mediterranean and practiced intensively in the Gulf of Gabès region. In the Mediterranean Sea,
many studies have demonstrated the negative environmental impacts of bottom trawlingon Posidonia oceanica meadows, marine fish productivity, and macroinvertebrate fauna
diversity [43–45]. According to different observations in several Mediterranean fisheries,
trawling activities have been responsible for the bulk of discards and the decline in fishstocks during the last few decades in the Mediterranean region [5,44].
To study the impacts of bottom trawling on macrobenthic communities in tidal channels of the Kneiss Islands, we use a BACI approach applied before and after the COVID-19
pandemic lockdown in the Gulf of Gabès. In this study, we evaluate for the first time theeffects of trawling on the benthic fauna of soft bottom substrates. Many recent studies haveindicated that the COVID-19 lockdown had important positive impacts in reducing several
human activities and improving environmental quality [35,36]. Similarly, the before/after
COVID-19 pandemic lockdown approach is an excellent opportunity used to assess theimpacts of trawling on benthic communities in the central part of the Mediterranean Sea.
The macrofauna of the tidal channels of Kneiss Islands is mainly composed of polychaetes belonging to the families Cirratulidae, Maldanidae, Nereididae, and Lumbrineridae,
followed by the amphipods represented by the Gammaridae, Dexaminidae, Corophiidae, andAoridae. During the five sampling periods (Figure 2), the control stations showed higher
diversity indices (taxonomic richness, abundance, and biomass) compared with the trawled
stations. The values of abundance and biomass recorded at the control stations in tidal
channels of Kneiss islands (3187 ± 348 ind.m−2 ; 186 ± 22 g AFDW.m−2 , respectively)
appear to be very high compared with other Mediterranean ecosystems [46], showingthat the tidal channels ecosystems form suitable and highly productive habitats for themacrobenthic fauna [16,47].
The seafloor morphologies in the tidal channels are of crucial importance for thefunctioning of marine ecosystems since they allow for the exchange of water, sediments,
nutrients, biota, and pollutants with the open sea [16,48]. The low biodiversity of the macrozoobenthic fauna recorded at trawled stations reflects the strong impact of destructive

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Int. J. Environ. Res. Public Health 2022, 19, 1282

fishing gear, potentially affecting entire habitats and leading to the damage and/or killingof benthic organisms. As a result, this reduces species taxonomic richness, abundance, andbiomass as reported in several other international studies [49–51]. The notable increase
in benthic community indices during the COVID-2019 pandemic lockdown period andafter two days of deconfinement (5 June 2020) at trawled stations highlights the negative
effects of illegal fishing gear on the subtidal benthic soft-bottom communities of the Kneiss
Islands. However, in the absence of trawling activities, we observe an increase in the abundance of many species such as the tubicolous polychaetes Euclymene oerstedii and Melinna
palmata; the infaunal polychaetes Cirratulus cirratus and Scolemata impatiens; the amphipod
Microdeutopus anomalus; and the bivalves Scrobicularia plana, Loripes orbiculatus, and Pinctada
imbricata radiata. Likewise, other numerous sedentary fauna such as the cnidaria, echinodermata, and tubicolous polychaeta species (e.g., Sabellidae, Terebellidae, and Serpulidae)
appeared only at impacted stations during the lockdown period and after two days ofdeconfinement. In fact, the continuous fishing disturbance contributes to a decline in theresistance of the macrobenthic assemblages of the Kneiss Islands. The high resilience of
these shallow benthic communities during the cessation of fishing activities is indicated by
the reappearance of smaller and some sensitive species such as infaunal polychaetes (e.g.,
tubicolous species) and mobile crustaceans that are unable to withstand environmental
changes due to their biological traits (life history, morphology, behaviour of each taxon,
inter-specific interactions, and connections between species and their environment); thisalso proves that the stability of sediment and nutrients of the tidal channels in the absenceof bottom trawling promotes the successful recruitment and colonization of several benthic
species and increases the trophic availability, which also attracts other mobile organisms
from neighbouring ecosystems (intertidal zones). The short-term increase in the number
abundance, and biomass of many sensitive and small-sized benthic organisms during the
COVID-19 lockdown period in the tidal channels of Kneiss could be explained by the factthat this period coincides with the recruitment period of the majority of the macrobenthic
species that generally takes place during the spring and summer season [52]. Previous
studies showed that opportunistic species, such as the polychaete families Spionidae andCirratulidae, are characterized by high growth rates, a short life span, a low reproductive
age, and a large reproductive output [53,54]; these are more resistant to change and impose their opportunistic strategy of life to overcome environmental changes caused byanthropogenic pressures including fishing disturbances [45,55].
Trawling stress leads to the selection of species that are adapted to living in softbottom habitats. Combined with a strong bottom trawling activity, this implies that thebenthic community is able to recolonize this environment rapidly after the cessation ofanthropogenic pressures [56]. In fact, mobile species (e.g., amphipods and small species)
have the ability to tolerate environmental changes and are able to escape during fishing
disturbances [57]. Moreover, the smaller and sensitive macrobenthic organisms are also
vulnerable to trawling effects [45,58], often displaying relatively high growth capacity andturnover rates, resulting in shorter recovery times [42,59,60].
The reappearance of some macrobenthic species and the increases in abundance andbiomass of macrofauna at the trawled stations during the COVID-19 lockdown suggestthat one month without trawling activities is necessary for the restoration of biodiversity
and for the recovery of the subtidal ecosystem of the Kneiss Islands. Many authors haveshown the strong resilience capacity of benthic communities following the short impactsof fishing disturbances including bottom trawling effects and that the speed and natureof recovery can vary greatly, depending upon the type and duration of impact and upon
the physical and life-history characteristics of the species making up the macrobenthic
communities [6,60–63].
The depletion in diversity and biomass of macrobenthic communities (especiallypolychaetes; Cirratulidae, Nereididae, and Maldanidae) observed at trawled stations during
the fishing period (almost −70% after a month of fishing) is due to direct and indirectmortality (destruction of tubes, exposure to predators, and habitat destruction) [63–66].

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Generally, the diversity and abundance of tubicolous polychaetes decreases after any
destructive fishing activity [20,37,65]. Environmental changes caused by destructive fishinggear lead to effects that modify the habitat structure, disrupt food web processes, andeliminate the more vulnerable organisms susceptible to environmental damage. This isbecause organisms are displaced by the fluidized sediments generated by the pressure wave
in front of a moving trawl [67,68]. Usually, macrobenthos respond negatively to fishingpressures, since they are highly vulnerable and can be considerably reduced in diversity
and abundance or even eliminated, being extremely fragile and particularly susceptible
to damage [45,69]. The increase in macrobenthic fauna biomass at the trawled stations
during the COVID-19 lockdown compared with the fishing periods confirms that chronic
trawling has a strong negative impact on the biomass and the functional capacity of subtidal
benthic macrofaunal communities. These results are in agreement with other previous
studies reporting the negative effects of trawling activity on the diversity, size composition,and biomass of benthic communities [50,62]. Equally, biomass is an effective indicator of
trawling disturbance. Recently, Sciberras et al. [50] demonstrated that the effects of trawling
are most pronounced in long-lived benthic organisms, as these typically take longer torecover after a trawling event. In the tidal channels of the Kneiss Islands, the effects oftrawling influence not only the sensitive taxa (infauna polychaetes) but also the larger
macrobenthic communities such as the crustacean decapods Carcinus spp. and Metapenaeus
Monoceros (Fabricius, 1798) and the molluscs Pinctada imbricata radiata (Leach, 1814) andCerastoderma glaucum (Bruguière, 1789). Evenly, many experimental studies have revealed
that large macrofauna are also disproportionately sensitive to trawling disturbance [70–72].
This vulnerability has been linked to a relationship between body size and several key lifehistory traits [73], whereby larger macrobenthic species tend to grow and reach maturity ata slower rate. Such species have comparatively lower mortality and population growth
rates and are therefore more vulnerable to trawling-induced mortality. Repeated andintense trawling would typically result in shifts away from communities dominated bya high biomass of long-lived organisms towards those dominated by highly abundantsmall macrofauna including opportunistic species [42,71,74]. The frequently trawled tidal
channels of the Kneiss islands show increased water turbidity caused by the scraping of theseabed during trawling and the injection of sediments into the water column [75] associatedwith a decrease in dissolved oxygen concentration [52]. The impact of oxygen deficiency
results in various behavioural changes for macrofauna communities. Mobile organisms (i.e.,
amphipods and decapods) are able to move away from or avoid hypoxic locations, while
the behavioural responses of sessile species (i.e., some molluscs, infauna, and tubicolous
polychaetes) might express modifications of feeding and bioturbation rates [76]. However,complete faunal depletion is observed at low oxygen concentrations and under anoxic
conditions in trawled zones [77]. Generally, various co-varying and interacting factors
have been proposed to account for the environmental effects of bottom trawling. Thesemay include the sensitivity of specific marine habitats [7,62], the impact of different gear
types [1,78], the magnitude of background human and natural disturbances [52,79], and thecapacity of benthic communities to adapt (resistance) to environmental disturbances [62,74].
Either alone or in combination, such factors could obscure the measurable effects of trawlingon benthic habitats and organisms and may also explain why indicators sometimes display
various different performances [12,80].
5. Conclusions and Future Perspectives
The present study shows the negative effects of bottom trawling on macrobenthic
fauna in the tidal channels of the Kneiss Islands. This destructive fishing gear causes a
notable reduction in the taxonomic richness, abundance, and biomass of the macrobenthic
communities. It is well known that the macrofauna plays a crucial role in the food web
either by feeding on detritus or as food for aquatic birds and economically important
demersal fish [81]. Thus, it would be very important to ban this illegal destructive fishingactivity in the Kneiss Islands, which represents a site of international interest in terms

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of marine biodiversity (Important Bird Area, SPAMI, and RAMSAR Site). This study
shows that just one month of non-fishing is sufficient to allow for the recovery of benthic
biodiversity. Finally, this exceptional lockdown period due to the COVID-19 pandemic
points to the high resilience of the macrobenthic community after the cessation of trawling
disturbances, which affects the interface between the sediment and the bottom layer of thewater column and has widespread negative impacts on benthic communities and marine
habitat. It could be a significant and important reason to prevent and prohibit the use ofthis illegal and destructive fishing gear to better protect the marine biodiversity of the tidal
channels of Gulf of Gabès.
Several aims might be considered for future work. The first would be to produce aneffective management plan integrating stakeholder communities to conserve the fisheriesof the Kneiss Islands. Second, it would be interesting to complete an evaluation of the
impacts of bottom trawling on macro- and megabenthic fauna in other tidal channels of
the Gulf of Gabès (studied recently by Dauvin et al. [16]). Finally, it might be worthwhile
carrying out seasonal and annual monitoring at selected stations along these tidal channels
to follow the long-term response of macrofauna communities to illegal fishing pressures.
Author Contributions: Conceptualization, N.M. and L.N.; methodology, N.M. and L.N.; software,
N.M. and J.-P.P.; writing—original draft preparation, N.M.; writing—review and editing, N.M., J.-P.P.;
J.-C.D. and L.N., supervision, J.-C.D. and L.N. All authors have read and agreed to the publishedversion of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: All data used are in the manuscript and are available from the corresponding author.
Acknowledgments: The authors thank the fishers of the Kneiss Islands and General Directorate ofFisheries and Aquaculture of Skhira for their support during the five sampling campaigns, especiallyduring the COVID-19 lockdown period in the Gulf of Gabès (Mach–May 2020), and M. Carpenter for
the English revision.
Conflicts of Interest: The authors declare no conflict of interest.

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International Journal ofEnvironmental Researchand Public Health
ArticleEvidence of Air and Surface Contamination with SARS-CoV-2
in a Major Hospital in Portugal
Priscilla Gomes da Silva 1,2,3 , José Gonçalves 4,5 , Ariana Isabel Brito Lopes 6 , Nury Alves Esteves 6 ,Gustavo Emanuel Enes Bamba 6 , Maria São José Nascimento 7 , Pedro T. B. S. Branco 3 , Ruben R. G. Soares 8 ,Sofia I. V. Sousa 3 and João R. Mesquita 1,2, *
1
2
3

4
5
6

7
8

*

ICBAS–School of Medicine and Biomedical Sciences, Porto University, 4050-313 Porto, Portugal;
up202002072@edu.icbas.up.pt
Epidemiology Research Unit (EPIunit), Institute of Public Health, University of Porto,
4050-600 Porto, Portugal
LEPABE–Laboratory for Process Engineering, Environment, Biotechnology and Energy,
Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal; up200606911@fe.up.pt (P.T.B.S.B.);
sofia.sousa@fe.up.pt (S.I.V.S.)
Institute of Sustainable Processes, University of Valladolid, 47011 Valladolid, Spain; zemcg5@gmail.com
Department of Chemical Engineering, University of Valladolid, 47011 Valladolid, Spain
Unidade Local de Saúde do Alto Minho E.P.E., 4904-858 Viana do Castelo, Portugal;
ariana.lopes@ulsam.min-saude.pt (A.I.B.L.); nury.esteves@ulsam.min-saude.pt (N.A.E.);
gustavo.bamba@ulsam.min-saude.pt (G.E.E.B.)
Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; saojose@ff.up.pt
Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University,
SE-106 91 Stockholm, Sweden; ruben.soares@scilifelab.se
Correspondence: jrmesquita@icbas.up.pt

Citation: Silva, P.G.d.; Gonçalves, J.;
Lopes, A.I.B.; Esteves, N.A.; Bamba,
G.E.E.; Nascimento, M.S.J.; Branco,
P.T.B.S.; Soares, R.R.G.; Sousa, S.I.V.;
Mesquita, J.R. Evidence of Air andSurface Contamination with
SARS-CoV-2 in a Major Hospital in
Portugal. Int. J. Environ. Res. Public
Health 2022, 19, 525. https://doi.org/10.3390/ijerph19010525
Academic Editors: Sachiko Kodera
and Essam A. Rashed
Received: 3 December 2021
Accepted: 30 December 2021
Published: 4 January 2022
Publisher’s Note: MDPI stays neutral

Abstract: As the third wave of the COVID-19 pandemic hit Portugal, it forced the country to reintroduce lockdown measures due to hospitals reaching their full capacities. Under these circumstances,environmental contamination by SARS-CoV-2 in different areas of one of Portugal’s major Hospitals
was assessed between 21 January and 11 February 2021. Air samples (n = 44) were collected fromeleven different areas of the Hospital (four COVID-19 and seven non-COVID-19 areas) using Coriolis®
µ and Coriolis® Compact cyclone air sampling devices. Surface sampling was also performed (n = 17)
on four areas (one COVID-19 and three non-COVID-19 areas). RNA extraction followed by a one-step
RT-qPCR adapted for quantitative purposes were performed. Of the 44 air samples, two were positive
for SARS-CoV-2 RNA (6575 copies/m3 and 6662.5 copies/m3 , respectively). Of the 17 surface samples,
three were positive for SARS-CoV-2 RNA (200.6 copies/cm2 , 179.2 copies/cm2 , and 201.7 copies/cm2 ,respectively). SARS-CoV-2 environmental contamination was found both in air and on surfaces
in both COVID-19 and non-COVID-19 areas. Moreover, our results suggest that longer collection
sessions are needed to detect point contaminations. This reinforces the need to remain cautious at all
times, not only when in close contact with infected individuals. Hand hygiene and other standardtransmission-prevention guidelines should be continuously followed to avoid nosocomial COVID-19.

with regard to jurisdictional claims inpublished maps and institutional affil-

Keywords: SARS-CoV-2; environmental contamination; air samples; surface samples

iations.

1. Introduction
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

SARS-CoV-2 infection causes respiratory illness ranging from mild to severe disease
and death, with some infected people being asymptomatic [1]. According to the World
Health Organization (WHO), evidence shows that SARS-CoV-2 spreads mainly between
people who are standing near one another. In these situations, aerosols or droplets produced
by an infected person and which contain the virus are inhaled or come directly into contact
with the nose or mouth of a susceptible person, particularly in poorly-ventilated andcrowded indoor environments [2].

229Int. J. Environ. Res. Public Health 2022, 19, 525

Respiratory droplets formed from respiratory secretions and saliva are emitted throughtalking, coughing, sneezing and even breathing, and have a diameter ranging from <1µm to >100 µm [3]. Respiratory droplets are generally defined as particles that fall to theground (or any surface) more quickly under the influence of gravity due to their larger
size; typically, these are particles > 5–10 µm that fall within 2 m of the source. When theseparticles settle on surfaces, the contaminated surfaces are then called fomites. Once emitted
by humans, these respiratory droplets tend to reduce in size due to evaporation, after
which they are termed as droplet nuclei or aerosols, which can be defined as particles
that remain suspended due to size and/or environmental conditions; typically, these areparticles ≤ 5 µm that stay suspended in air for longer, eventually falling to the ground if
the air is motionless for long enough (at least 30 min) [3]. Considering that aerosols are
small enough to remain suspended in air, they can accumulate in poorly-ventilated spaces
and in turn be inhaled at both short and long ranges by a susceptible person, indicating theimportance of improving and ensuring good indoor ventilation in the context of COVID19 [4]. Moreover, the produced aerosols contain much higher viral loads when comparedto viral loads in droplets [5–8]. Hence, proper ventilation can reduce surface contamination
by removing virus particles before they can land on surfaces [9].
Despite droplet and fomite transmissions being considered the probable main modes oftransmission for SARS-CoV-2, these alone cannot account for superspreading events [10–12],
or for differences in transmission between indoor and outdoor environments [8]. SARSCoV-2 modes of transmission are now distinguished as inhalation of virus, depositionof virus on exposed mucous membranes, and touching mucous membranes with soiled
hands contaminated with virus [13]. However, individuals who come into contact withpotentially infectious surfaces or aerosols containing viral particles often have close contact
with an infected person, making it difficult to distinguish the source of infection as being
airborne or through fomites [14], particularly in healthcare institutions where infected individuals continuously excrete high viral loads into the environment, potentially adding tothe occupational risk of healthcare professionals [15]. In fact, while the risk of transmission
via environmental contamination of SARS-CoV-2 is considered to be generally low [16], anumber of factors may increase this risk, particularly considering hospital environments
where a high number of symptomatic patients with active infection and increased viral
shedding might be present when compared to the situation in the community outside thehospital environment [17].
At the beginning of this study, 21 January 2021, there were 151,226 active cases of
COVID-19 in Portugal and 702 people admitted in intensive care units (ICUs) all overthe country [18]. To the best of the authors’ knowledge, no study on environmental
contamination with SARS-CoV-2 in hospital settings has been performed in Portugal;
hence, this study aimed to assess air and surface contamination in different areas of a major
Hospital in Portugal during the peak of the third wave of COVID-19 in the country (lateDecember 2020 to mid-February 2021). The study also aimed at assessing the performanceand suitability of two air samplers (Coriolis® µ and Coriolis® Compact) for SARS-CoV-2 airmonitoring. The results may be relevant in establishing interventions to prevent healthcare
workers’ exposure to SARS-CoV-2 and to optimize and better understand the extent ofenvironmental viral contamination of surfaces in healthcare settings.
2. Materials and Methods
Sampling sites
Environmental sampling took place in a Hospital in Portugal that serves around 2.2%of the Portuguese population, between 21 January and 11 February 2021. Air samples(n = 44) were collected from four COVID-19 and seven non-COVID-19 areas. COVID-19
areas included the COVID-19 ICU, intermediate COVID-19 ICU, COVID-19 nursing area
and the COVID-19 testing room. Non-COVID-19 areas included the respiratory diseases
observation room, respiratory diseases waiting room, clinical decision unit, non-respiratory

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diseases patients’ waiting room, urgent care (recovery area), the Hospital’s outside entrance
atrium and the Hospital’s staff cafeteria.
Surface sampling (n = 17) was performed on four areas, of which three were nonCOVID-19 areas (non-respiratory disease waiting room, staff cafeteria, and outside entrance
atrium) and one COVID-19 area (the COVID-19 testing room). Further details about eachsampling site are summarized in Tables 1 and 2.

Non-COVID-19 areas

COVID-19 areas

Table 1. Air ventilation details about the sampling sites.
Hospital Area

Type of Ventilation
and Pressure

People with Access toThis Area

COVID-19 ICU

Mechanic ventilation,negative pressure

Patients and Hospital staff

Intermediate
COVID-19 ICU

Mechanic ventilation,negative pressure

Patients and Hospital staff

COVID-19 nursing area

Mechanic ventilation,negative pressure

Patients and Hospital staff

COVID-19 testing room

Mechanic ventilation,negative pressure

Patients and Hospital staff

Respiratory diseases
observation room

Natural ventilation,neutral pressure

Patients and Hospital staff

Respiratory diseases
waiting room

Natural ventilation,neutral pressure

Patients, Hospital staff andpatients’ companions

Non-respiratory diseases
waiting room

Natural ventilation,neutral pressure

Patients, Hospital staff andpatients’ companions

Clinical decision unit

Natural ventilation,neutral pressure

Patients and Hospital staff

Urgency care(recovery area)

Natural ventilation,neutral pressure

Patients and Hospital staff

Hospital’s outside
entrance atrium

Natural ventilation,neutral pressure

Open to the general public

Hospital staff’s cafeteria

Natural ventilation,neutral pressure

Hospital staff

Table 2. Details of the air samples’ collections for SARS-CoV-2 RNA detection in the Hospital.

Coriolis® Compact

DeviceSample ID

C1

C2

Date of Collection

21 January 2021

21 January 2021

Hospital Area

Sampler Location

Sampling
Parameters

ICU COVID-19

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

50 L/min, 60 min

ICU intermediate
COVID-19

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

50 L/min, 60 min

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Table 2. Cont.
DeviceSample ID

C3

C4

C5

C6

C7

C8

C9

C10

C11

Coriolis® µM1

M2

Hospital Area

Sampler Location

Sampling
Parameters

21 January 2021

Nursing area
COVID-19

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

50 L/min, 60 min

27 January 2021

Respiratory
diseases
observation room

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

50 L/min, 60 min

27 January 2021

Respiratory
diseases waitingroom

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

50 L/min, 60 min

27 January 2021

Clinical decisionunit

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

50 L/min, 60 min

2 February 2021

COVID-19 testingroom

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

50 L/min, 60 min

2 February 2021

Non-respiratory
diseases waitingroom

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

50 L/min, 60 min

2 February 2021

Urgency Care
(Recovery area)

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

50 L/min, 60 min

11 February 2021

Hospital’s outside
entrance atrium

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

50 L/min, 60 min

11 February 2021

Hospital
employe’s cafeteria

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

50 L/min, 60 min

ICU COVID-19

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

100 L/min, 10 min

ICU COVID-19

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

200 L/min, 10 min

Date of Collection

21 January 2021

21 January 2021

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Table 2. Cont.
DeviceSample ID

M3

M4

M5

M6

M7

M8

M9

M10

M11

M12

Hospital Area

Sampler Location

Sampling
Parameters

ICU COVID-19

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

300 L/min, 10 min

ICU intermediate
COVID-19

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

100 L/min, 10 min

ICU intermediate
COVID-19

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

200 L/min, 10 min

ICU intermediate
COVID-19

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

300 L/min, 10 min

Nursing area
COVID-19

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

100 L/min, 10 min

Nursing area
COVID-19

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

200 L/min, 10 min

21 January 2021

Nursing area
COVID-19

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

300 L/min, 10 min

27 January 2021

Respiratory
diseases
observation room

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

100 L/min, 10 min

27 January 2021

Respiratory
diseases
observation room

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

200 L/min, 10 min

27 January 2021

Respiratory
diseases
observation room

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

300 L/min, 10 min

Date of Collection

21 January 2021

21 January 2021

21 January 2021

21 January 2021

21 January 2021

21 January 2021

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Table 2. Cont.
DeviceSample ID

M13

M14

M15

M16

M17

M18

M19

M20

M21

M22

M23

Date of Collection

Hospital Area

Sampler Location

Sampling
Parameters

27 January 2021

Respiratory
diseases waitingroom

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

100 L/min, 10 min

27 January 2021

Respiratory
diseases waitingroom

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

200 L/min, 10 min

27 January 2021

Respiratory
diseases waitingroom

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

300 L/min, 10 min

Clinical decisionunit

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

100 L/min, 10 min

Clinical decisionunit

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

200 L/min, 10 min

27 January 2021

Clinical decisionunit

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

300 L/min, 10 min

2 February 2021

COVID-19 testingroom

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

100 L/min, 10 min

2 February 2021

COVID-19 testingroom

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

200 L/min, 10 min

2 February 2021

COVID-19 testingroom

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

300 L/min, 10 min

2 February 2021

Non-respiratory
diseases waitingroom

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

100 L/min, 10 min

2 February 2021

Non-respiratory
diseases waitingroom

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

200 L/min, 10 min

27 January 2021

27 January 2021

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Table 2. Cont.
DeviceSample ID

M24

M25

M26

M27

M28

M29

M30

M31

M32

M33

Date of Collection

Hospital Area

Sampler Location

Sampling
Parameters

2 February 2021

Non-respiratory
diseases waitingroom

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

300 L/min, 10 min

Urgency Care
(Recovery area)

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

100 L/min, 10 min

Urgency Care
(Recovery area)

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

200 L/min, 10 min

2 February 2021

Urgency Care
(Recovery area)

Air sampler placed
approximately 1.3 m
above the floor and 2 mfrom a patient bed, inthe center of the room

300 L/min, 10 min

11 February 2021

Hospital’s outside
entrance atrium

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

100 L/min, 10 min

11 February 2021

Hospital’s outside
entrance atrium

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

200 L/min, 10 min

11 February 2021

Hospital’s outside
entrance atrium

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

300 L/min, 10 min

11 February 2021

Hospital staff’s
cafeteria

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

100 L/min, 10 min

11 February 2021

Hospital staff’s
cafeteria

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

200 L/min, 10 min

11 February 2021

Hospital staff’s
cafeteria

Air sampler placed
approximately 1.3 m
above the floor in thecenter of the room

300 L/min, 10 min

2 February 2021

2 February 2021

Collection of air and surface samples
Air samples were collected using two cyclonic microbial air samplers, a Coriolis® µand a Coriolis® Compact (Bertin Instruments, Montigny-le-Bretonneux, France). Using the
Coriolis® µ, three consecutive air samplings were collected from each of the eleven areas of
the Hospital for 10 min each with an airflow rate of 100 L/min (total of 1 m3 ), 200 L/min(total of 2 m3 ) and 300 L/min (total of 3 m3 ), respectively. Air samples with the Coriolis® µwere collected on wet medium, with 4 mL of sterile phosphate buffered saline (PBS) added
to the collection cones before sampling. With the Coriolis® Compact, one air sampling235

Int. J. Environ. Res. Public Health 2022, 19, 525

was performed in the same eleven areas for 60 min, with an airflow rate of 50 L/min(total of 3 m3 ). Air samples with Coriolis® Compact were collected on dry medium, with
4 mL of sterile PBS added to the collection cones after sampling. Both Coriolis® samplers
were placed side by side at 1.3 m height using a portable table, and the three consecutive
Coriolis® µ samplings were performed simultaneously within the 60-min sampling periodsof the Coriolis® Compact.
Surface samples were collected on 10 cm × 10 cm surface areas (100 cm2 area per
sampling) using sterile flocked plastic swabs previously wetted on PBS and immediately
placed in PBS (4 mL).
All samples were stored at 4 ◦ C before being taken to the laboratory facilities, andwere processed within 8 h. Details about the characteristics of the air and surface samples
collected are summarized in Tables 2 and 3, respectively.
Table 3. Details of the surface samples’ collections for SARS-CoV-2 RNA detection in the Hospital.
Sample ID

Hospital Area

Collection Date

Hand sanitizer
dispenser

S1
S2
S3

Sample Location

COVID-19 testingroom

2 February 2021

Instruments’ counterGlove box

S4

Wall of a COVID-19
testing booth

S5

Paper dispenser

S6

Faucet handle

S7

Non-respiratory
disease waiting room

2 February 2021

Vending machine(buttons)

S8

Bathroom: flush
buttonS9Bathroom: inside
doorknob

S10

Bathroom: outside
doorknob

S11
S12

Hospital’s outside
outside entrance
atrium

11 February 2021

ATM (buttons)
Fire extinguisher(approx. 3 m awayfrom outside
entrance)

S13

Soap dispenser

S14
S15
S16

Statue (approx. 3 m
away from outside
entrance)

Hospital staff’s
cafeteria

11 February 2021

S17

Faucet handle
Paper dispenser
Table sign on a table ** Table sign: a sign with precautions to avoid contamination was placed on the table.

RNA extraction and detection of SARS-CoV-2
RNA extraction was performed using the GRS Viral DNA/RNA Purification Kit(GRISP, Porto, Portugal) according to the manufacturer’s instructions. RNA extraction
was performed on 200 µL of sample suspensions as previously described [19]. A one-step
RT-qPCR reaction aimed at two viral gene targets (N1 and N2) using viral target-specificprimers and Taqman probe technology based on a previously described protocol [20] was236

Int. J. Environ. Res. Public Health 2022, 19, 525

used (Xpert qDetect COVID-19, GRISP, Porto, Portugal). For the CFX Real-Time PCR(qPCR) Detection System (Bio-Rad, Hercules, CA, USA), the Bio-Rad CFX Maestro 1.0
Software version 4.0.2325.0418 was used to control the runs and remotely analyze the data.
Each RT-qPCR run included ssDNA targets for both N1 and N2 regions (positive controls)
and a no-template control. Reactions were set up and run with initial conditions of 15
min at 45 ◦ C and 2 min at 95 ◦ C, then 45 cycles of 95 ◦ C for 15 sec and 55 ◦ C for 30 sec.
A standard curve was constructed using the ssDNA targets for both N1 and N2 regions
in a 10-fold serial dilution mixture starting at 200,000 copies/µL, in order to quantify the
number of viral gene copies present in each sample from the measured Ct values; the limit
of detection (LOD) was 1.3 copies/µL for N1 and 3.2 copies/µL for N2. Air sample results
are expressed in copies/m3 , and surface sample results in copies/cm2.
3. Results
Of the 44 air samples collected in eleven different areas of the Hospital, only two (C1and M1) were positive for SARS-CoV-2 RNA (Table 4). They were both from the same place,
the COVID-19 ICU, and were collected at the same time; C1 (viral loads of 6000 and 6575
copies/m3 for N1 and N2 genes, respectively) was collected during 60 min sampling with
the Coriolis® Compact at an airflow rate of 50 L/min (total of 3 m3 ), while M1 (viral loads
of 6362.5 and 6662.5 copies/m3 for N1 and N2 genes, respectively) was collected with the
Coriolis® µ during the first 10 min of the Coriolis® Compact collection period, at an airflow
rate of 100 L/min (total of 1 m3 ). The two other Coriolis® µ consecutive samples (M2 andM3) collected within the 60-min time frame of the Coriolis® Compact (air flow rates of 200
L/min and 300 L/min, respectively) were both negative for SARS-CoV-2 RNA.
Table 4. Details of Hospital area, sampling location and viral genome copy numbers of the positive
air and surface samples.

Air samples

SampleID
C1

Surface samples

S6

S8

Copy Number(N2 Gene)

6000 copies/m3

6575 copies/m3

6362.5 copies/m3

6662.5 copies/m3

Wall of a COVID-19 testing booth

200.6 copies/cm2

No amplification
detectedFaucet handle

179.2 copies/cm2

No amplification
detected

Bathroom: flush button

No amplification
detected

201.7 copies/cm2

Sample Location

COVID-19 ICU

Air sampler placed approximately 1.3 m
above the floor and 2 m from intubatedpatients beds, in the center of the room

M1

S4

Copy Number(N1 Gene)

Hospital Area

COVID-19 testingroom
Non-respiratory
disease patients’
waiting room

Of the 17 surface samples collected in four different areas of the Hospital, three werepositive for SARS-CoV-2 RNA, with viral loads of 200.6 copies/cm2 (COVID-19 testing
room, wall of a testing booth), 179.2 copies/cm2 (non-respiratory disease waiting room,
faucet handle), and 201.7 copies/cm2 (non-respiratory disease waiting room, bathroom’s
flush button). The three samples amplified only one of the two target genes (N1, N1 andN2 respectively). Details on the Hospital area, sampling location and viral genome copy
numbers of the positive air and surface samples are summarized in Table 4.

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4. Discussion
The present study aimed to evaluate SARS-CoV-2 environmental contamination of
air and surfaces in a major Hospital in Portugal during the third wave of the COVID19 pandemic. Eleven different areas of the Hospital were selected to be assessed for air
contamination, including four COVID-19 and seven non-COVID-19 areas. SARS-CoV-2
RNA was only detected in the air of the COVID-19 ICU. The viral load of these air samples
collected with the Coriolis® Compact and Coriolis® µ ranged from 6000 to 6662.5 copies/m3 .
Interestingly, only the first sample of Coriolis® µ, collected during the first 10 min of the
60-min time frame of the Coriolis® Compact, was SARS-CoV-2 RNA positive. The twoother consecutive samplings of Coriolis® µ performed within the 60-min time frame of the
Coriolis® Compact were negative for SARS-CoV-2 RNA in spite of a higher airflow rate (airflow rates of 200 L/min and 300 L/min, respectively), suggesting a point contamination
which could be explained by the fact that, when the sampling period started, a patient had
just been intubated. This intubation can explain the presence of aerosols containing virus
during the first 10 min of Coriolis® µ, sampling and the negative results in the second andthird samples, considering that aerosols containing SARS-CoV-2 may have been removedby the rooms’ ventilation system by the time the other two samplings took place.
In this study, we aimed to assess the performance and suitability of both air samplers
for SARS-CoV-2 air monitoring. The results of this study suggest that Coriolis® µ and
Coriolis® Compact samplers seem robust for SARS-CoV-2 air sampling, as both were able
to detect SARS-CoV-2 RNA in the air of the COVID-19 ICU. However, longer collection
times are more likely to cover point contaminations, as was seen with the 60-min collection
with Coriolis® Compact. During this collection, three consecutive samplings of Coriolis® µwere performed, with only the first providing a positive air sample.
To assess contamination of surfaces, four different areas of the Hospital were selected,
one COVID-19 area and three non-COVID-19 areas. SARS-CoV-2 RNA was detected on
the wall of one of the testing booths (where patients wait for nasopharyngeal sample
collection), which was somewhat expected due to the fact that during sampling there
were possibly-infected individuals constantly coming in to collect nasopharyngeal samples
for testing, all of them reporting as symptomatic (presenting respiratory symptoms suchas coughing or sneezing). Moreover, this is the only room where patients remove theirmasks, likely increasing the viral load in indoor air. Contamination in this room might
have happened through respiratory aerosols or droplets from the infected patients that
settled on the surfaces sampled, considering that neither the patients or healthcare staff
touch the walls in this area, ruling out the possibility of direct touch contamination in thiscase. SARS-CoV-2 viral RNA was detected in the non-respiratory disease waiting room
as well. This was one of the non-COVID-19 areas sampled, and therefore the presence ofviral RNA was not expected. This was a positive pressure room, which ideally prevents
unfiltered air from outside the room from coming inside [21]. Nevertheless, viral RNA
was detected in two surface samples in this area, namely on a faucet handle located in the
middle of the waiting room and on the flush button of a bathroom in this area. As these are
frequently-touched surfaces, the most likely explanation for viral RNA presence is direct
touch contamination. Nonetheless, SARS-CoV-2 excretion in stools has been described,
and the flushing could generate contaminated aerosols that could ultimately deposit onthese surfaces [22,23]. These results highlight the importance of hand and general hygienein public toilets as well as the need for enhanced disinfection protocols in all areas of the
Hospital, not only those dedicated to COVID-19 patients.
Airborne transmission of SARS-CoV-2 is now widely accepted as a mode of transmission of SARS-CoV-2 [2,4,8,12]. This route dominates under certain environmental
conditions, particularly indoor environments without proper ventilation [24–29], with arecent study demonstrating that some fraction of the RNA-containing aerosols emitted
from infected people contain intact, replication-competent virions [30]. On the contrary, current evidence suggests that transmission through contaminated surfaces is rare; however,when it comes to healthcare settings where COVID-19 patients are being treated, espe-

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cially in ICUs, aerosol-generating medical procedures take place which could potentiallyexacerbate the contamination of air and surfaces in the surrounding area [31]. To avoid
nosocomial infection of healthcare workers, non-COVID-19 patients, and visitors by thesevirus-laden aerosols as well as to avoid contamination of medical equipment and surfaces,
it is imperative that hospital ventilation works properly [4].
COVID-19 patients that need to undergo intubation and extubation are usually placedin negative-pressure isolation rooms, which is considered to be safer [32]. Negative-pressure
rooms have a ventilation system in which air flows from the exterior to the interior [21].
This keeps aerosolized viruses from spreading through the heating, ventilation, and air
conditioning system. Under these conditions, if an opening exists, air will flow from thesurrounding areas into the negatively pressurized space [4]. In the Hospital assessed inthis study, the COVID-19 ICU, which was the only negative room sampled, had twelve airchanges per hour (ACH), in compliance with the safety rules for these types of rooms [31].
Since the beginning of the pandemic, many articles have been published on detectionof SARS-CoV-2 in air samples [24,25,33–35]; however, some inherent problems with air
sampling for the detection of viruses have come up, such as the limited diversity ofmonitored spaces (most studies are done in indoor healthcare facilities), limited number ofsamples, diversity of methodologies (there is no gold-standard protocol for air sampling of
SARS-CoV-2 and other airborne viruses), not all studies performing both surface and air
sampling simultaneously, and lastly, the fact that most of these studies were performed
during the first wave when little was known about the virus, which could have led to errors
in methods and molecular analysis.
This study faced some limitations that are worth highlighting. This was an observational study at a single hospital, which means that the results may not be generalizable toother healthcare facilities. Additionally, no assessment of virus viability was performed, as
no BSL3 facility was available to perform such experiments. As a result, the findings in thisstudy, although reflecting the real extent of environmental contamination with SARS-CoV-2
in the Hospital, do not necessarily amount to an infection risk assessment for air andsurfaces. Moreover, no surface sampling was performed in the COVID-19 ICU, and we do
not have individual data on patients, particularly those who occupied the COVID-19 ICU at
the time of sampling. Patients with severe infection influence the viral load in droplets andexhaled aerosols; therefore, it is important that in future studies these individual patient
data are acquired in order to allow better interpretation of results. Nevertheless, this study
is an important addition to the growing literature on the detection of SARS-CoV-2 RNA in
air and on surfaces.
5. Conclusions
The present study showed SARS-CoV-2 hospital environmental contamination bothin air and on surfaces in locations where both COVID-19 and non-COVID-19 patientswere present. This reinforces the need to remain cautious at all times, not only when inclose contact with infected individuals. Hand hygiene and other standard transmissionprevention guidelines should be continuously followed in order to avoid nosocomial
COVID-19. Further studies combining air and surface sampling with virus viability assays
are still needed to fully elucidate the real risk of air and environmental transmission in
healthcare facilities.
Author Contributions: Conceptualization, P.G.d.S., J.R.M., S.I.V.S.; methodology, P.G.d.S., J.R.M. and
S.I.V.S.; investigation, P.G.d.S.; resources, J.R.M. and S.I.V.S.; data curation, P.G.d.S.; writing—originaldraft preparation, P.G.d.S.; writing—review and editing, P.G.d.S., J.R.M., S.I.V.S., M.S.J.N., P.T.B.S.B.,
R.R.G.S., A.I.B.L., N.A.E., G.E.E.B. and J.G. All authors have read and agreed to the published versionof the manuscript.
Funding: This work was financially supported by Base Funding-UIDB/00511/2020 of the Laboratory
for Process Engineering, Environment, Biotechnology and Energy–LEPABE—funded by national
funds through the FCT/MCTES (PIDDAC).

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Int. J. Environ. Res. Public Health 2022, 19, 525

Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: Priscilla Gomes da Silva thanks the Portuguese Foundation for Science andTechnology–FCT for the financial support of her PhD work (2020.07806.BD, CRM: 0026504) contract
through the DOCTORATES 4 COVID-19 program. Sofia I.V. Sousa thanks the Portuguese Foundationfor Science and Technology (FCT) for the financial support of her work contract through the Scientific
Employment Stimulus-Individual Call-CEECIND/02477/2017.
Conflicts of Interest: All the authors certify that they have no affiliations with or involvement in any
organization or entity with any financial interest (such as honoraria, educational grants, participationin speakers’ bureaus, membership, employment, consultancies, stock ownership, or other equity
interest, and expert testimony or patent-licensing arrangements), or non-financial interest (such as
personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter ormaterials discussed in this manuscript.

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International Journal ofEnvironmental Researchand Public Health
ArticleHow Does Gender Moderate Customer Intention of Shopping
via Live-Streaming Apps during the COVID-19 Pandemic
Lockdown Period?
Yuyang Zhao *and Fernando Bacao
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal;
bacao@novaims.unl.pt* Correspondence: d2015046@novaims.unl.pt; Tel.: +86-156-3993-7262

Citation: Zhao, Y.; Bacao, F. How
Does Gender Moderate Customer
Intention of Shopping via
Live-Streaming Apps during the
COVID-19 Pandemic Lockdown
Period? Int. J. Environ. Res. Public
Health 2021, 18, 13004. https://

Abstract: Shopping through Live-Streaming Shopping Apps (LSSAs) as an emerging consumption
phenomenon has increased dramatically in recent years, especially during the COVID-19 lockdown
period. However, insufficient studies have focused on the psychological processes undergone indifferent customer demographics while shopping via LSSAs under pandemic conditions. This study
integrated the Unified Theory of Acceptance and Use of Technology 2 with Flow Theory into a
Stimulus-Organism-Response framework to investigate the psychological processes of different
customer demographics during the COVID-19 lockdown period. A total of 374 validated data wereanalyzed by covariance-based structural equation modelling. The statistical results demonstrated by
the proposed model showed a significant discrepancy between different gender groups, in which
Flow, as a mediator, representing users’ engagement and immersion in shopping via LSSAs, wassignificantly moderated by gender where connection between stimulus components, hedonic motivation, trust and social influence and response component perceived value are concerned. This study
contributed a theoretical development and a practical framework to the explanation of the mental
processes of different customer demographics when using an innovative e-commerce technology.
Furthermore, the results can support the relevant stakeholders in e-commerce in their comprehensive
understanding of customers’ behavior, allowing better strategical and managerial development.

doi.org/10.3390/ijerph182413004
Academic Editors: Sachiko Kodera,
Essam A. Rashed and William

Keywords: COVID-19; customer behavior; psychological process; live-streaming shopping apps;
stimulus-organism-response framework; Unified Theory of Acceptance and Use of Technology 2;
Flow Theory; gender; age

Douglas Evans
Received: 6 November 2021
Accepted: 7 December 2021
Published: 9 December 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
Live-streaming commerce, as a burgeoning e-commerce pattern with the unique features of real-time live-streaming demonstration of products and instant interactions among
sellers and viewers, provides personalized services for customers remotely [1]. Meanwhile,
based on the broad application of telecommunication networks and extensive adoptionof mobile devices, live-streaming shopping apps (LSSAs) have provided an immersive
experience for viewers [2], which has formulated a new consumption phenomenon, namely,
shopping via LSSAs, especially in the Chinese e-commerce industry in recent years. According to a report from iiMedia (2020), live-streaming commerce industry transactions were
estimated to exceed 129 billion USD in 2020, up from 61 billion USD in 2019 [3]. In 2019, on
the Taobao e-commerce platform alone, over 60,000 live-streaming shows hosted by brands,
stores and celebrities attracted more than 400 million consumers [4]. Shopping via LSSAs
has established an entertainment environment for customers, facilitating a revolution incommerce. Under the lockdown measures for the defence against COVID-19 transmission
especially, interaction and entertainment were in significant demand from individuals.
Despite several previous studies demonstrating that the adoption of LSSAs was influenced
by the motivations of participants [5,6], technology features [7] and human-computer243

Int. J. Environ. Res. Public Health 2021, 18, 13004

interaction [2], few studies have focused on the customer’s psychological processes whensubject to the moderating effects of age and gender in a particular environment. LSSAs
are mobile entertainment and commerce applications, and their adoption should take themoderating effects of age and gender into consideration [8].
Consequently, the objective of the current study is to investigate the psychologicalprocesses of customers of different ages and genders when shopping via LSSAs during
the COVID-19 pandemic lockdown situation. The proposed model embeds the revised
Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as a stimulus, alongwith Flow Theory as an organism, into the stimulus-organism-response (SOR) framework.
In this way, the SOR framework, as the main structural foundation of the research model,
explains that customers’ behavioral psychological processes are determined by external
antecedents as well as internal cognitions [9–12]. UTAUT2, as a theoretical framework, hascoordinated consumer-oriented perceptions to predict users’ behavioral intentions [13],which is legitimately considered a perceptive process in the explanation of customers’
perceptions of LSSAs and the constitution of stimulus components. Flow Theory supports
organisms theoretically by representing participants’ concentration and engagement in
shopping activities via LSSAs [14]. These theoretical frameworks are initially integrated
and verified in this study, supporting relevant researchers and stakeholders in order tobetter understand the behaviors of customers.
The current study comprises eight sections, investigating the psychological processesof customers shopping via LSSAs under pandemic lockdown conditions. Section 2 consistsof a literature review regarding the subject of shopping via LSSAs, as well as relevant
theoretical frameworks. This section is followed by the research model and hypotheses
development, which are presented in Section 3. Section 4 illustrates the method of datacollection and the demographic distribution of data. Subsequently, Section 5 presents
the results of the data analysis. Section 6 discusses the findings of these results, while
Section 7 illustrates their theoretical and practical implications. Finally, limitations andrecommendations for future research and conclusion are outlined in Section 8.
2. Theoretical Background2.1. Live-Streaming Shopping Apps (LSSAs)
The current LSSAs comprise e-commerce functions that are integrated into livestreaming platforms with simultaneous and authentic consumption interactions between
vendors and customers [5,6]. Shopping via LSSAs can be divided into two patterns. The
first of these patterns is consumption activities on mobile e-commerce apps with extensional
live-streaming functions, such as Taobao and AliExpress, while the second is shopping via
LSSAs through a third-party e-commerce service, such as Tiktok and LiveMe [6]. Shopping
via LSSAs has become a thriving new consumption phenomenon. Off-line consumption
activities were restricted, especially under lockdown conditions during the COVID-19
pandemic; shopping via LSSAs supported customers’ daily supply and demand requirements and provided a relaxation pattern during the quarantine time, formulating a positive
perception among users. Cai et al. (2018) claimed that customers’ decisions to shop via livestreaming are not only influenced by utilitarian perceptions of service and production but
are also determined by hedonic motivation [5]. According to the entertainment feature of
live-streaming, viewers’ engagement and gratification significantly affected their shopping
activities [15]. Based on LSSA’s facilitation of conspicuous human-machine interaction,
this affordance of LSSAs and customers’ engagement conjointly determine the number ofcustomers purchasing via LSSAs [2].
Meanwhile, previous studies have found that customers’ endorsement of, and behavioral responses to LSSAs were observably determined by their intrinsic and extrinsic motivations, social influence, entertainment, perceived flow and emotional engagement [16–18].
Accordingly, customers’ mental perceptions, such as trust and perceived value, have significantly influenced their engagement with live-streaming commerce [1]. Moreover, thesimultaneity, authenticity, interactivity and customizability characteristics of LSSAs signifi-244

Int. J. Environ. Res. Public Health 2021, 18, 13004

cantly formulate customers’ perceptions of this technology, affecting their behavior [7,19].
However, prior literature has insufficiently investigated customers’ psychological processes
while shopping via LSSAs under specific conditions. Moreover, the moderating effects ofage and gender might lead to different results in different market segmentations [20]. Thus,
it is meritorious to clarify the role of age and gender in moderating customers’ mental
processes while shopping via LSSAs in the pandemic lockdown situation.
2.2. Stimulus-Organism-Response (SOR) Framework
The SOR framework demonstrates that external antecedents influence customers’
psychological processing, first as a perceptive stimulus, affecting their cognitive andemotional reflections, then as an organism, contributing towards formulating their mental
or behavioral traits, and finally as a response, such as an attitude, adoption intention
or actual usage [21]. The SOR framework has been modified with external variables toanalyze in a qualified way the connections between the stimulus (environmental input),the organism (mental process) and the response (behavioral outputs), in order to explain
users’ behaviors in various business analysis studies [11,22] as well as works of literatureon the adoption of innovative technology [10,12,23], which are demonstrated in Table 1. Intheir investigation of users’ visiting intentions in virtual reality tourism, Kim, Lee and Jung
(2020) designated customers’ actual experiences as stimuli, their cognition and affection(including enjoyment, emotional involvement and flow) as organisms and their attachment
and intention as responses [12]. Zhao, Wang and Sun (2020) proposed that stimuli include
interactivity, media richness and sociability, and assumed that virtual experience as anorganism would include telepresence, social presence and flow, which in turn determined
students’ continuing intentions regarding the use of massive open online courses [23].
Moreover, the SOR framework has been applied in an investigation of customers’ online
shopping intentions, which were significantly influenced by their attitude, which was inturn affected by their internal and external environment [9]. However, compared withtraditional online shopping, streaming service quality and promotion campaigns played
more significant roles in formulating customers’ purchase intentions via mobile shopping
apps [22].
Furthermore, the moderating effects of age and gender have rarely been examinedin the SOR framework. There have been a few previous studies partially involving themoderators in the SOR model, but their results were presented inconsistently. Wu and Li(2018) found that gender had a significant moderating effect on customers’ loyalty in onlinesocial commerce [11], against the findings of Islam and Rahman (2017) [24]. Therefore, thisstudy uses the SOR framework as a theoretical foundation to create a research model.

245

Int. J. Environ. Res. Public Health 2021, 18, 13004

Table 1. Literature review of the SOR framework.
Studies

[22]

Topic

Stimulus

Organism

Response

Mobile shopping

Ubiquity;
Ease of use;
Information exchange;
Discounted price;
Scarcity

Impulsive buying
tendency;Normative evaluation;
Positive affect

Purchase intention

Consumer value

Consumer loyalty

Structural capital;
Cognitive capital;
Relational capital;
Social identification;
Social influence;
Social commerce needs;
Social commerce risk;Social commerce
convivence;[11]

Social commerce[10]

Mobile payment

Usefulness;
Emotion;Security

Flow

Satisfaction;
Purchase intention[12]

Virtual reality tourism

Actual experiences

Enjoyment,
Emotional involvement,
Flow

Attachment;
Visit intention[23]

Massive open
online courses

Interactivity;
Media richness;
Sociability

Virtual Experience;
Telepresence;
Social presence;flow

Continuance intention

2.3. Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)UTAUT2 was developed by Venkatesh, Thong and Xu (2012). As an extension of
the UTAUT model, UTAUT2 predicts users’ technological perceptions, determining theirintention regarding the adoption of a particular technology [13]. Several researchers havemodified UTAUT2 in miscellaneous mobile-technology-adoption studies by extending
it with additional variables or moderators, for example, trust [25,26], privacy [27] andHofstede’s cultural values [28,29]. Some studies have incorporated UTAUT2 into other theoretical frameworks, such as diffusion of innovation [30] and the expectation-confirmation
model [31,32], in investigating customers’ behaviors while using mobile technology. Furthermore, UTAUT2 involves the use of age, gender and experience as moderators to explain
individual differences in adoption intention [13]. Moreover, UTAUT2 was applied to theadoption of mobile shopping applications by Tak and Panwar (2017), who found that
hedonic motivation was the most significant antecedent, which corresponds with the recognition of the current study that LSSAs are entertaining mobile shopping applications [33].
Therefore, UTAUT2 is considered the appropriate theoretical foundation for investigating
users’ perceptions as a stimulus in the proposed model.
2.4. Flow Theory
Flow Theory was initially proposed by Csikszentmihalyi (1975) as a way to predict
individuals’ mental engagement in a certain activity [14]. Subsequently, Flow Theory’s
applicability has been extended into the human-computer interaction domain to describe
users’ absorption in technology [34]. Specifically, flow represents users’ holistic, immersive consciousness when they concentrate entirely on a particular activity or technology;
their involvement will be self-reinforced by constitutional enjoyment and engaging interactivity, and, in turn, their self-consciousness will become indistinct in order to ignore
irrelevant interruptions [35]. Flow has been applied as a mediator in various technology
adoption studies to describe customers’ cognition and engagement for predicting users’
adoption intention [10,36,37], especially in the fields of entertaining technologies, such as
246

Int. J. Environ. Res. Public Health 2021, 18, 13004

live-streaming [16] and mobile shopping [38]. Flow is significantly influenced by users’
technological perceptions [37,39], as well as mental determinants such as emotion [10],trust [38] and enjoyment [16]. Meanwhile, the combination of Flow Theory with otherframeworks, such as the Information Systems Success Model [37,38] and the StimulusOrganism-Response framework [10], also reasonably illustrated users’ adoption intention.
Thus, Flow Theory is considered a theoretical foundation for the representation of users’
shopping engagement via LSSAs during the pandemic lockdown period, acting as theorganism in the proposed model.
2.5. Moderating Effects of Age and GenderAccording to the current research objectives, age and gender are proposed as moderating variables involved in the analysis process. Venkatesh, Thong and Xu (2012) initiallyconfirmed that age and gender have moderating effects on the UTAUT2 constructs affecting
users’ adoption intention [13]. Moreover, other works of literature have integrated age
and gender as moderators within various frameworks (UTAUT, Technology Acceptance
Model (TAM), Diffusion of Innovation (DOI) Theory) and have confirmed that age andgender significantly moderate constructs in different contexts [20,40–44]. However, basedon the differences in research objectives, sample targets and involved variables in various
scenarios, the moderating effects of age and gender have been diverse in different literature.
Venkatesh and Zhang (2010) validated the idea that performance expectancy in the behavioral intention of information technology was significantly moderated by younger maleusers, and effort expectancy was strongly moderated by older female customers [40], whichis contrary to the findings of Riskinanto, Kelana and Hilmawan (2017), who claimed thatstated age had insignificant effects on perceived usefulness and ease of use regarding theintention of adoption of E-payment technology [43]. On the other hand, Liébana-Cabanillas,
Sánchez-Fernández and Muñoz-Leiva (2014) complementarily illustrated that social influence bore a strong influence on users above 35 years old, and that trust was more affectedby younger groups in the adoption of mobile payments [41]. Moreover, Shao et al. (2018)claimed that males had a stronger moderating effect on mobility and reputation in thetrust-formation process of mobile payments, while females moderated customization andsecurity of trust more [44]. Likewise, Pascual-Miguel, Agudo-Peregrina and ChaparroPeláez (2015) found that the moderating effects of female customers on effort expectancy
and social influence were significantly stronger than male customers on online purchaseintention [45]. In order to analyse the moderating effects of age and gender on all constructs
in the proposed research model, a multi-group analysis is applied in this research, which is
widely applied in previous studies for multi-group comparisons [20,44,45].
3. Development of Research Model and Hypotheses
Based on the previous literature reviews, the integration of the SOR framework withUTAUT2 and Flow Theory is considered a theoretical foundation on which to propose acomprehensive model for the investigation. Specifically, according to previous paradigmsof the SOR framework application, this research extends the SOR framework by integrating
variables from the revised UTAUT2 model, which are proposed as stimulus components,
roused by technological perceptions of LSSAs during the pandemic lockdown period(performance expectancy, effort expectance) [22,24], namely, social influence [11], hedonic
motivation [12] and trust [12]. These variables reflect users’ external and internal perceptions towards inciting their further psychological cognition. On the other hand, due to the
popularization of smartphones, proficiency in using various mobile applications and theabsence of monetary cost in the operation of LSSAs, original variables such as facilitating
conditions, habit and price value are excluded from the UTAUT2 model, which is in accordance with previous findings [25,26,28,31,32]. Flow Theory provides theoretical support
to the reflection of customers’ mental, cognitive and affective intermediary states during
shopping via LSSAs in the COVID-19 pandemic lockdown period, which it is appropriate
to consider as an organism in the SOR framework [10,12,23]. Moreover, this study proposes247

facilitating conditions, habit and price value are excluded from the UTAUT2 model, whichis in accordance with previous findings [25,26,28,31,32]. Flow Theory provides theoretical
Int. J. Environ. Res. Public Health 2021,support
18, 13004 to the reflection of customers’ mental, cognitive and affective intermediary states
during shopping via LSSAs in the COVID-19 pandemic lockdown period, which it is appropriate to consider as an organism in the SOR framework [10,12,23]. Moreover, thisstudy
proposesvaluethat andperceivedvalue
and adoption
intention reflects
customers’
psychothat perceived
adoption
intentionreflects customers’
psychologicalreactionsandlogicalreactionsandbehaviors,constituting
theresponse
elements
oftheSOR
framework
behaviors, constituting the response elements of the SOR framework [10,12]. In addition,[10,12].
addition,age and gender
are considered
moderators
the theoreticalmodel
age and In
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the different
comparingthedifferent
effectsofantecedentsoncustomers’
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regardingeffects of antecedents on customers’ adoption intention regarding LSSAs in each subgroup.
LSSAs
in each research
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The isproposed
researchmodel is generalized
and presentedinThe
proposedmodel
generalized
and presented
in Figure 1 withthe relevant
Figure1 withthe relevant hypotheses relations.
hypotheses
relations.

Figure 1. Proposed researchresearch model.
model.

3.1. Stimulus
Stimulus Components:
Components: Variables
Variables fromfrom thethe Revised
Revised UTAUT2UTAUT2 Model
3.1.
Model
Performance expectancy
expectancy (PE),as aa technological
technological perception,
perception, represents
represents the
Performance(PE), asthe perceived
perceived
usefulnessofacertaintechnology
in
the
eyes
of
a
user,
and
its
potential
tooptimize
usefulness of a certain technology in the eyes of a user, and its potential to optimize their
theirexperience ofof aaspecificspecifictechnology
technologyorortotoreinforce
reinforce
theirperformance
particular
activiexperience
their
performancein inparticular
activities
ties [13].
Moreover,technologicalfeatureswhichusersperceive,
perceive,suchsuchasas compatibility,
compatibility,[13].
Moreover,the thetechnologicalfeatures
which
users
service quality,
quality, information
quality, canservice
information quality
quality andand systemsystem quality,
can bebe generalized
generalized asas perceived
perceived
usability ofof technology,
usability
technology, represented
represented as
as PEPE [37,46,47].
[37,46,47]. Related
Related toto technology
technology adoption,
adoption, PEPEsignificantly
formulates
users’
mentalresponses,
suchasattitude,adoption
intentionandsignificantly formulates users’ mental responses, such as attitude, adoption intention andcontinued usage
confirmed
by by
prior
literature[30,48,49].
Accordingly,
continued
usageintention,
intention,which
whichareareconfirmed
prior
literature[30,48,49].
Accordcustomers’
psychologicalcognitionsareformulatedby
theirperceptionsofsatisfying
usingly, customers’ psychological cognitions are formulated by their perceptions of satisfyability,
which whichindicatesthat PEthat
significantly
influences
customers’
perceived
flow whening
usability,
indicates
PE significantly
influences
customers’
perceived
flow
they intend to adopt new technology [10,39]. Therefore, the hypothesis can be generalized
as follows:
Hypothesis 1 (H1). Customers’ performance expectancy (PE) as a stimulus positively determines
the organism flow (FL) when shopping via LSSAs during the pandemic lockdown period.

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Int. J. Environ. Res. Public Health 2021, 18, 13004

Effort expectancy (EE), as a technological perception, expresses the idea that users
acquire feelings of easiness from understanding, operating and interacting with a specificinformation technology [13]. A variety of literature has verified EE’s considerable effect on
customers’ attitude and behavioral intention in technology adoption research [43,47,48].
Consequently, customers’ engagement and flow experience are formulated by understandability, operability and intractability [36,39]. Moreover, the influence of EE has been
confirmed by Kim et al. (2013) as not only affecting flow but also performance expectancy
when users adopt entertainment technology [39]. When customers recognize that a technology is easy to access, they will tend to confirm its usability. This phenomenon has beenvalidated in various technology adoption works of literature, such as live-streaming [19],mobile banking [26,46,49] and mobile payment [41,47]. Thus, the hypotheses related to EE
are proposed as follows:
Hypothesis 2 (H2). Customers’ effort expectancy (EE) as a stimulus positively determines theorganism flow (FL) when shopping via LSSAs during the pandemic lockdown period.
Hypothesis 3 (H3). Customers’ effort expectancy (EE) as a stimulus positively determines
performance expectancy (PE) when shopping via LSSAs during the pandemic lockdown period.
Social influence (SI), as an environmental perception, represents customers perceiving an influence from people particularly relevant to them, such as close friends, family
members and colleagues, who recommend and support them in using a certain technology [13]. Customers’ anxiety is derived from the uncertainty of new technology, which
can be decreased by the influence of their close social network [25]. Various technology
adoption studies have involved SI in theoretical frameworks and have confirmed SI as
an essential antecedent in determining customers’ attitudes and behaviors [27,29,42,50].
Moreover, Chen and Lin (2018) claimed that the effect of SI was to dramatically formulate
users’ mental awareness of engagement when using live-streaming [16]. Accordingly,interacting with relevant people on a specific information technology can facilitate users’
flow experience [23]. Hence, the current study proposes that users’ flow experience of
LSSAs is positively determined by SI, which formulated the following hypothesis:
Hypothesis 4 (H4). Social influence (SI) as a stimulus positively determines the organism flow(FL) when shopping via LSSAs during the pandemic lockdown period.
Hedonic motivation (HM) was initially adapted in UTAUT2 by Venkatesh, Thongand Xu (2012), which is defined as the internal emotional perception of enjoyment andpleasure descend from a user’s expectation or experience of a certain information technology [13]. The directly positive effect of HM on adoption intention has been confirmed
by prior researchers who applied UTAUT2 on mobile technology adoption, e.g., mobile
payment [27] and mobile banking [26,28]. Meanwhile, HM has been recognised as anantecedent which also has a significant indirect effect on customers’ behavioral intention.
Yeo, Goh and Rezaei (2017) claimed that HM formulated attitude via convenience motivation and post-usage usefulness when customers adopt online food delivery services [51].
Likewise, engagement, as the main characteristic of users’ flow experience, is formulatedby enjoyment, curiosity and concentration [52,53].
Consequently, Wongkitrungrueng and Assarut (2018) validated HM directly andindirectly (through trust), formulating users’ engagement in live-streaming commerce [1].
Furthermore, Chen and Lin (2018) illustrated that live-streaming’s entertainment features
formulated viewers’ HM, which positively determined their mental perception of value,
which in turn affected their final behavioral intention [16]. Thus, the current study assumes
that HM positively affects customers’ engagement and affection in Hypothesis 5.
Hypothesis 5 (H5). Customers’ hedonic motivation (HM) as a stimulus positively determines theorganism flow (FL) when shopping via LSSAs during the pandemic lockdown period.
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Int. J. Environ. Res. Public Health 2021, 18, 13004

Gefen (2000) defined trust (TR) as describing users’ subjective awareness of believinga particular technology can fulfil obligations and positively guarantee a qualified performance to meet their expectations [54]. Specifically, under the lockdown measures of the
COVID-19 pandemic, trust reflected users’ perceptions of technological characteristics suchas mobility, security, etc., which correspond with perceived security against perceived
risk and uncertainty conditions [44]. LSSAs’ contactless online consumption functions, abeneficial feature in the lockdown situation during the COVID-19 pandemic, formulated
customers’ perceived trust and positively influenced enjoyable and practical cognitions [42].
Accordingly, trust as an essential variable in the investigation of users’ behavioral intention
has been integrated into various adoption models, such as UTAUT2 [25], TAM [46] andthe IS success model [37]. Moreover, from the participation aspect, the interaction between
customers and vendors on live-streaming commerce platforms facilitated users’ perceived
trust in sellers and products, which in turn optimized engagement [1]. Therefore, this paper
proposes that trust formulates users’ engagement and cognitive acceptance, positively
influencing flow [37,38]. Meanwhile, as an antecedent of users’ utilitarian perceptions,trust positively facilitates customers’ performance expectancy stimulus [26,46]. Hence, thefollowing hypotheses are addressed:
Hypothesis 6 (H6). Trust (TR) as a stimulus positively determines the organism flow (FL) when
shopping via LSSAs during the pandemic lockdown period.
Hypothesis 7 (H7). Trust (TR) as a stimulus positively determines performance expectancy (PE)when shopping via LSSAs during the pandemic lockdown period.
3.2. Organism Component: Flow (FL)
Csikszentmihalyi (1975) defined flow as an individual’s feeling of intrinsic absorption
in a particular activity or technology [14]. Flow experience of technology was described as
users’ temporary unawareness caused by their internal enjoyment of, pleasure in and engagement and interaction with a certain technology [16,38]. Moreover, lockdown measuresprovided an appropriate environment for the enhancement of an individual’s immersive
shopping experience via LSSAs at home. Various technology adoption studies have validated the idea that flow is significantly formulated by users’ technological perceptions
regarding their behavioral intention of adoption or continued usage [18,37,38]. Consequently, flow is in accordance with the conception of an organism, which is assumed as amediator connecting technological and environmental stimuli and responses in shopping
via LSSAs.
Flow has been examined by previous researchers as also having an indirect effecton users’ final responses, such as adoption intention, actual usage and continuance intention, via users’ mental reflection variables, such as perceived value, satisfaction andattitude [16,39,55]. Meanwhile, the effects of flow and mental reflections has been validated
as a way in which to determine customers’ behavioral intentions conjointly in various
pieces of literature [10,37,38]. Chen and Lin (2018) claimed that flow positively affected perceived value regarding the formulation of customers’ intention to use live-streaming [16].
Therefore, the following hypotheses are proposed:
Hypothesis 8 (H8). Customers’ flow (FL) as an organism positively determines the response
perceived value (PV) when shopping via LSSAs during the pandemic lockdown period.
Hypothesis 9 (H9). Customers’ flow (FL) as an organism positively determines the response
behavioral intention (BI) when shopping via LSSAs during the pandemic lockdown period.
3.3. Response Components: Perceived Value (PV) and Behavioral Intention (BI)Perceived value (PV), as defined by Zeithaml (1988), represents customers’ universal
assessments of a service or technology. PV is determined by users’ perceptions of acquisition and investment [56]. Sweeney and Soutar (2001) extended the dimensions of perceived
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Int. J. Environ. Res. Public Health 2021, 18, 13004

value, including quality and price of production, customers’ emotional responses andsocial influence [57]. Meanwhile, Petrick (2002) modified behavioral price, monetary price,emotional response, quality and reputation, all of which emerged as other dimensions of
PV [58]. PV also represents customers’ perceived multi-dimensional benefits, including
those from utilitarian, hedonic and social perspectives [50,59]. Specifically, in this study,
perceived value represents the customers’ general mental responses to shopping via LSSAs
during the COVID-19 pandemic lockdown period. Perceived value has been assumed
as a cognitive variable in various adoption models, such as the Expectation Confirmation Model [36], the Value-based Adoption Model [60] and the Mobile user Engagement
Model [59], which positively determines customers’ behaviors. On the other hand, Chen
and Lin (2018) confirmed that perceived value, as a conative factor, was determined by flow
and, in turn, formulated users’ behaviors [16]. Therefore, this study proposes that perceived
value, which, in a customer, would be a conational response, constitutes one component
amongst a variety of responses, which is demonstrated in the following hypothesis:
Hypothesis 10 (H10). Customers’ perceived value (PV) positively determines behavioral intention(BI) when shopping via LSSAs during the pandemic lockdown period.
3.4. Moderation Hypotheses
Gender and age moderators were incorporated within the UTAUT2 model to investigate information technology adoption [13]. Various studies involved gender and age
moderators in different adoption models, and validated gender and age as moderating
constructs in different scenarios respectively [13,20,40–45]. As shown in Figure 1, genderand age moderators are assumed to be multi-group controls, which should optimize thepredictive validity of the proposed model in the explanation of any conflicting results [42].
Meanwhile, due to the way in which the proposed model was initially developed, themoderation hypotheses are established by the exploratory approach in this study, which
was common in previous studies [42]. The moderating effect will be assessed by subgroup
analysis after the general model evaluation. Therefore, the following hypotheses are proposed in investigation of the different moderating effects of age and gender on customers’
psychological processes experienced while shopping via LSSAs.
Hypothesis 11 (H11). Gender moderates the relations among all constructs of the proposed model.
Hypothesis 12 (H12). Age moderates the relations among all constructs of the proposed model.
4. Methodology and Data Demographic Distribution
4.1. Measurement
A quantitative methodology was applied in this study to evaluate the proposed model.
An online questionnaire survey was conducted to collect data in China, which comprised
two parts. The first part requested demographic information (consisting of gender, age
and frequency of using LSSAs during the COVID-19 pandemic lockdown period) ofparticipants, using dichotomous, bounded continuous and ordinal-polytomous close-ended
questions; the second part consisted of a seven-point Likert scale (from strongly disagree =“1” to strongly agree = “7”), comprising structural questions which evaluated performance
expectancy (PE), effort expectancy (EE), social influence (SI), hedonic motivation (HM), trust(TR), flow (FL), perceived value (PV) and behavioral intention (BI), with 34 measurement
items taken from previous literature, shown in Table A1 in Appendix A.
The questionnaire was designed and managed in English, before being translated into
the Chinese language by language experts to avoid the biases of language and culture (thetarget population of the survey was smartphone users in China). Afterwards, according to
the translation-back translation method, it was reverse-translated into the English language.
In order to minimize the non-response rate, a short introduction and respondent-friendly
survey questionnaire techniques were applied in the survey [61].

251Int. J. Environ. Res. Public Health 2021, 18, 13004

4.2. Data Collection
The online questionnaire was designed via Wenjuan.com (a Chinese online survey
platform). According to the formulae from Westland (2010) and the numbers of 34 observed
indicators and eight latent variables in the proposed model, the recommended minimum
sample size for the model structure was 91 [62]. The questionnaires were distributed
online and via WeChat (a Chinese mobile social media application) on 9 August 2020 for
data collection. After four weeks of data collection, 400 empirical data were collected on
6 September 2020, of which 138 were derived from online responses and 262 via WeChat.
After filtering out the responses with missing values in a scrutinizing process, 374 valid
data were accepted for data analysis, which obtained a 93.5% final response rate. The
Kolmogorov-Smirnov test was applied to examine the early respondents’ group with
100 participants and the late respondents with 274 participants. This test confirmed nostatistical difference between the two independent groups [63]. Meanwhile, the ShapiroWilk test of the demographic data, age, gender and frequency of those shopping via LSSAs
were 0.636, 0.862 and 0.918, respectively, and all showed a significant level of 0.000, whichindicated the data was non-normally distributed.
4.3. Data Demographic Characteristics
The data was collected online and via WeChat randomly, and the geographical distribution of respondents consisted of 43.5%, 13.1% and 3.5% located in Henan, Guangdong
and Shandong provinces, respectively, which are the three largest Chinese provinces interms of population. This represented general smartphone users in China. 51.87% female
and 48.13% male smartphone users participated in the survey. The largest age group was
adults between 21 and 35, at 27.01%, while the age range of participants younger than
36 comprised 51.07%, with the group over 35 years old being 48.93%. These figures are
consistent with the QusetMobile report (2020) that users between 19 and 35 years old werethe leading group of shoppers via LSSAs in China [64]. Moreover, 24.06% of participants
used LSSAs at least once per week. More than 39% of respondents shopped via LSSAs
every week. Table 2 presents the specific demographic distribution of participants.
Table 2. Demographic distribution of participants.
MeasureGender

Age

Item

N

%

Male

180

48.13%

Female194

51.87%<2090

24.06%

21–35

101

27.01%

36–50

96

25.67%>51

87

23.26%At least 1 time per 1 day

59

15.78%At least 1 time per 1 week90

24.06%At least 1 time per 2 weeks

81

21.66%At least 1 time per 1 month55

14.71%At least 1 time per 3 months

41

10.96%At least 1 time per 6 months

29

7.75%

Never used during the pandemic lockdown period

19

5.08%

Frequency of using
LSSAs during the
COVID-19 pandemic
lockdown period

5. Data Analysis
As the proposed model was generated based on a solid theoretical foundation, structural equation modelling is appropriate to operationalizing the hypothesized latent constructs and associated indicators for theory development [65]. Besides minimizing the252

Int. J. Environ. Res. Public Health 2021, 18, 13004

difference between the observed and estimated covariance matrices, covariance-based structural equation modelling (CB-SEM) applies a maximum likelihood procedure to assesscorrelations among all constructs and their interactive effects simultaneously [66,67]. Meanwhile, the proposed model in this study consists of mediating variables and moderators.
The CB-SEM approach is well suited to the assessment of models involving mediation andmoderating effects [65,67]. CB-SEM performs very accurately, with sum scores higher thanboth PLS-SEM and regression in a small sample size [68]. Meanwhile, CB-SEM presents
more accurately than PLS-SEM for non-normally distributed data with a sample size over
50 [69]. CB-SEM provides optimal coefficient estimates and more accurate model analyses
in the evaluation of research models [70]. Moreover, the two-step approach, consistingof the measurement model assessment and structural model evaluation [71], is appliedin this study. Specifically, the CB-SEM technique is conducted for confirmatory factor
analysis (CFA), to assess the convergent and discriminant validity for each construct in themeasurement model assessment, and to evaluate the path coefficient to test hypotheseswith a comparison of differences between age (<36 VS >35) and gender (Male VS Female)
sub-samples in the structural model evaluation by AMOS.
Before implementing the two-step approach, The Exploratory Factor Analysis (EFA)
was applied by SPSS to evaluate the dataset adequacy. The Kaiser criterion and scree plot
were applied in order to identify the number of underlying extractive factors. The Kaiser
test obtained an eight-factor solution with eigenvalues larger than 1, and the first inflexion
point was located at the 9th point in the scree plot. The results indicate that eight factors
can be extracted, which is aligned with the proposed model [72].
5.1. Measurement ModelFirstly, the reliability and validity of the measurement model was assessed by the following criteria: Construct reliability was confirmed by Cronbach’s alpha (CA). The CAs of
all constructs exceeded 0.70 [73], and convergent validity was validated by factor loadings
above 0.7 [74]; Composite Reliability (CR) exceeded 0.7; Average Variance Extracted (AVE)exceeded 0.5 [75]; discriminant validity was qualified by the square root of AVE of eachlatent construct exceeding any two pairs of its inter-construct correlation [75] and the AVE
was higher than the maximum shared squared variance (MSV) of each construct [66]. The
constructs’ CA, CR, AVE, MSV, and factor loading of items results are presented in Table 3.
Table 4 displays the results of the square root of AVE, which are bigger than the correlations of each latent construct. The values of the results reached the relevant recommended
threshold of each criterion. Therefore, the reliability and validity of the measurement
model were confirmed for further assessment.
Moreover, the model-fit of measurement model shown in Table 5 was validated by
the goodness-of-fit results meeting the standards of each index [70]. Namely, the ratioof chi-square to degrees-of-freedom (X2 /df < 3), comparative fit index (CFI > 0.9), thegoodness of fit index (GFI > 0.8) [76], adjusted goodness-of-fit index (AGFI > 0.8) [70],normalized fit index (NFI > 0.9), Tucker-Lewis index (TLI > 0.9) and root mean square errorof approximation (RMSEA < 0.08) [77].
Furthermore, the potential common method bias of this study was evaluated byHarman’s one-factor test and the fitness of a single-factor model. The result of Harman’s
one-factor test is 49.63%, which meets the criteria proposed by Podsakoff et al. (2003)
that the largest variance of one factor should be below 50% in order to confirm that asingle factor cannot explain the majority of the variance [78]. The fitness results of asingle-factor model are shown in Table 5, which illustrates the unqualified model fit of asingle-factor model.
Based on the previous assessments, the measurement model is eligible for further
structural model evaluation.

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Int. J. Environ. Res. Public Health 2021, 18, 13004

Table 3. Latent constructs’ CA, CR, AVE, MSV, and items’ factor loading.
FactorsCA

Performance expectancy (PE)

Effort expectancy (EE)

0.943

0.935

Social influence (SI)

0.949

Hedonic motivation (HM)0.946

Trust (TR)

0.948

Flow (FL)

0.965

Perceived value (PV)0.943

Behavioral Intention (BI)

0.923

CR

0.943

0.932

0.936

0.949

0.946

0.948

0.965

0.923

AVE

0.807

0.820

0.785

0.822

0.776

0.786

0.847

0.800

MSV

0.510

0.326

0.264

0.416

0.412

0.446

0.510

0.394

Items

Loadings

PE1

0.875

PE2

0.912

PE3

0.905

PE4

0.899

EE1

0.896

EE2

0.874

EE3

0.916

EE4

0.858

SI1

0.896

SI2

0.904

SI3

0.925

SI4

0.902

HM1

0.868

HM2

0.89

HM3

0.909

HM4

0.869

HM5

0.869

TR1

0.905

TR2

0.883

TR3

0.873

TR4

0.898

TR5

0.873

FL1

0.911

FL2

0.923

FL3

0.930

FL4

0.910

FL5

0.927

PV1

0.882

PV2

0.901

PV3

0.909

PV4

0.900

BI1

0.892

BI2

0.892

BI3

0.899

(CA = Cronbach’s alpha; CR = Composite Reliability; AVE = Average Variance Extracted; MSV = maximum
shared squared variance).

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Int. J. Environ. Res. Public Health 2021, 18, 13004

Table 4. Latent constructs’ square root of AVE and correlation.

PV
PE
EE
SI
HM
TR
FL
BI

PV

PE

EE

SI

HM

TR

FL

BI

0.898
0.571
0.514
0.645
0.642
0.668
0.714
0.608

0.905
0.416
0.408
0.422
0.506
0.523
0.494

0.886
0.427
0.463
0.460
0.470
0.486

0.907
0.551
0.565
0.599
0.538

0.881
0.535
0.592
0.541

0.886
0.635
0.600

0.920
0.628

0.894

(Number in Bold: Latent constructs’ square root of AVE).

Table 5. The Model-fit of each model.
X2 /df

CFI

GFI

AGFI

NFI

TLI

RMSEA

Recommend Value

<3

>0.9

>0.8

>0.8

>0.9

>0.9

<0.08

Single-Factor Model

12.471

0.526

0.398

0.360

0.505

0.525

0.175

Measurement Model

1.166

0.994

0.918

0.902

0.959

0.993

0.021

Original Structural Model

1.477

0.982

0.899

0.882

0.947

0.980

0.036

Model with Age Subgroups

1.433

0.968

0.825

0.796

0.902

0.965

0.034

Model with Gender Subgroups

1.371

0.972

0.829

0.801

0.906

0.970

0.032

5.2. Structural Model
According to the research objectives and proposed hypotheses, the structural equation
model was created by AMOS and developed into two versions with age and gender
subgroups, respectively.
Firstly, the model-fits of structural models (including the original structural modeland two structural models with age and gender subgroups) were assessed consistently as
the previous evaluation process of model-fit of the measurement model. The results met all
thresholds of goodness-of-fit as presented in Table 5, which demonstrates that all structuralmodels have eligible goodness-of-fit.
Moreover, the R2 values of endogenous variables were assessed to evaluate the structural models’ explanatory powers. The R2 values of endogenous variables in the threestructural models are presented in Table 6. Specifically, the model with gender subgroups
has the highest R2 values of performance expectancy (R2 = 0.35), flow (R2 = 0.70) andbehavioral intention (R2 = 0.45). The model with age subgroups has the highest R2 value ofperceived value (R2 = 0.55).
Table 6. R2 values of endogenous variables in different models.
R2
Endogenous Variables

Original
Structural Model

Model withAge Subgroups

Model with
Gender Subgroups

PE
FL
PV
BI

0.30
0.58
0.53
0.45

0.30
0.58
0.55
0.42

0.35
0.70
0.52
0.45

Furthermore, the testing of the hypotheses was evaluated by the coefficient of eachpath. The results are depicted in Table 7. Specifically, except for H2 (EE → FL) being
rejected (ß = 0.078, p = 0.068), all the other hypotheses were supported in the original
structural model and sorted by the significance from high to low, shown as follows: H8(FL → PV, ß = 0.732, p < 0.001), H9 (FL → BI, ß = 0.44, p < 0.001), H7 (TR → PE, ß = 0.38,
255Int. J. Environ. Res. Public Health 2021, 18, 13004

p < 0.001), H10 (PV → BI, ß = 0.308, p < 0.001), H6 (TR → FL, ß = 0.275, p < 0.001), H5 (HM→ FL, ß = 0.232, p < 0.001), H3 (EE → PE, ß = 0.209, p < 0.001), H4 (SI → FL, ß = 0.205,
p < 0.001) and H1 (PE → FL, ß = 0.187, p < 0.001).
Table 7. Hypotheses testing of the original structural model.
Original Model
H
H1
H2
H3
H4
H5
H6
H7
H8
H9
H10

Relations
PE→FL
EE→FL
EE→PE
SI→FL
HM→FL
TR→FL
TR→PE
FL→PV
FL→BI
PV→BI

Estimate
0.187
0.078
0.209
0.205
0.232
0.275
0.38
0.732
0.44
0.308

S.E.
0.047
0.043
0.048
0.044
0.052
0.053
0.052
0.046
0.069
0.069

T
3.958
1.825
4.319
4.642
4.485
5.196
7.323
15.799
6.38
4.469

P
***
0.068
***
***
***
***
***
***
***
***

DecisionsSupported
Rejected
Supported
Supported
Supported
Supported
Supported
Supported
Supported
Supported(Est. = estimate; S.E. = standard error; T = t-value; P = p-value; ***: p-value < 0.01).

Moreover, based on the evaluation of path coefficients of each subgroup in Table 8, themodel performed variously. With regards to moderation hypotheses, H11 and H12 wereconfirmed partially. Five out of ten paths were significantly different when comparing
the gender groups. However, only one out of ten hypotheses were significantly different
for age groups. The effect of flow on perceived values (H8) was validated, having themost significantly positive influence in all four subgroups. Meanwhile, the effect of flow
on behavioral intention (H9) was verified with the second largest coefficient in the male
subgroup (ß = 0.555, p < 0.001) and the over-35 subgroup (ß = 0.51, p < 0.001), respectively.
The male subgroup significantly moderated effort expectancy on flow (ß = 0.162, p = 0.003).
However, H2 (EE → FL) was rejected in the model with the female group (ß = 0.038,
p = 0.55), the group with age below and equal to 35 (ß = 0.076, p = 0.18) and the groupwith age higher than 35 (ß = 0.084, p = 0.207). Meanwhile, relations between hedonic
motivation and flow, and perceived value and behavioral intention, were significantly
moderated by the female moderator. Hypotheses H5 (HM → FL, ß = 0.07, p = 0.248) andH10 (PV → BI, ß = 0.179, p = 0.083) were rejected in the model with the male subgroup.
Likewise, the male subgroup moderated the effects of social influence and trust on flow.
H4 (SI → FL, ß = 0.124, p = 0.062) and H6 (TR → FL, ß = 0.12, p = 0.155) were found withinsignificant effects in the female subgroup. On the other hand, the age moderator only
caused a significant difference in hedonic motivation as part of flow in two age groups, theyounger age group having a more significant moderating effect on hedonic motivation aspart of flow than the older age group.
In addition, the model invariances were evaluated by comparing the chi-square of two
subgroup models to evaluate the moderating effects of gender and age, and to assess theH11 and H12; see the results presented in Table 9. H11 was supported by the results of themodel, which demonstrated a variance under the moderating effect of gender. However,there were insignificant differences at the level of the model with different age groups, andthus H12 should be interpreted cautiously. Specifically, to illustrate the differences of thepath effect in each subgroup, the critical ratio was assessed to test the hypotheses, and thez-score was tested to evaluate the data. As shown in Table 8, the results demonstrate theeffects of hedonic motivation, trust and social influence on flow, as well as the effects ofperceived values on behavioral intention, both of which were significantly variant between
male and female groups. Meanwhile, only the flow path to perceived value significantly
differed between younger and older age groups.

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Table 8. Hypotheses testing of the subgroups.
Model with Gender Subgroups
Male

FemaleH

Relations

Est.

S.E.

T

P

Dec.

Est.

S.E.

T

P

Dec.

Z-Score

H1
H2
H3
H4
H5
H6
H7
H8
H9
H10

PE→FL
EE→FL
EE→PE
SI→FL
HM→FL
TR→FL
TR→PE
FL→PV
FL→BI
PV→BI

0.146
0.162
0.265
0.283
0.07
0.367
0.385
0.681
0.555
0.179

0.061
0.054
0.066
0.056
0.061
0.064
0.071
0.066
0.099
0.103

2.401
3.014
4.009
5.075
1.154
5.697
5.447
10.293
5.609
1.734

0.016
0.003
***
***
0.248
***
***
***
***
0.083

Sup.
Sup.
Sup.
Sup.
Rej.
Sup.
Sup.
Sup.
Sup.
Rej.

0.186
0.038
0.151
0.124
0.465
0.12
0.389
0.771
0.337
0.409

0.068
0.064
0.07
0.067
0.088
0.084
0.076
0.065
0.096
0.092

2.73
0.598
2.151
1.866
5.297
1.421
5.145
11.902
3.526
4.46

0.006
0.55
0.031
0.062
***
0.155
***
***
***
***

Sup.
Rej.
Sup.
Rej
Sup.
Rej.
Sup.
Sup.
Sup.
Sup.

0.446
−1.485−1.173−1.83 *
3.699 ***
−2.331 **
0.035
0.973
−1.583
1.664 *

Model with Age Subgroups

H

Relations

Est.

S.E.

H1
H2
H3
H4
H5
H6
H7
H8
H9
H10

PE→FL
EE→FL
EE→PE
SI→FL
HM→FL
TR→FL
TR→PE
FL→PV
FL→BI
PV→BI

0.196
0.076
0.145
0.21
0.287
0.204
0.479
0.744
0.352
0.341

0.063
0.056
0.068
0.063
0.066
0.076
0.074
0.065
0.099
0.1

≤35

>35

T

P

Dec.

Est.

S.E.

T

P

Dec.

Z-Score

3.106
1.341
2.131
3.319
4.383
2.666
6.512
11.37
3.566
3.405

0.002
0.18
0.033
***
***
0.008
***
***
***
***

Sup.
Rej.
Sup.
Sup.
Sup.
Sup.
Sup.
Sup.
Sup.
Sup.

0.182
0.084
0.285
0.205
0.155
0.342
0.277
0.72
0.51
0.284

0.071
0.067
0.07
0.062
0.082
0.075
0.073
0.065
0.096
0.095

2.57
1.263
4.097
3.295
1.892
4.58
3.773
10.995
5.308
2.983

0.01
0.207
***
***
0.058
***
***
***
***
0.003

Sup.
Rej.
Sup.
Sup.
Rej
Sup.
Sup.
Sup.
Sup.
Sup.

1.431
−0.153−0.411
0.096−0.058−1.2591.293−1.947 *−0.268
1.15(Est. = estimate; S.E. = standard error; T = t-value; P = p-value; Dec.= decision; Sup. = Supported; Rej. = Rejected; ***: p-value < 0.01;**: p-value < 0.05; *: p-value < 0.1).

Table 9. Comparison between the models of gender and age subgroups.
Model with Gender Subgroups
Chi-Squaredf

Unconstrained

1401.159

Fully Constrained

1451.548

Number of Groups
Difference

p-Value

Model with Age Subgroups
Chi-Square

df

1022

1464.315

1022

1058

1496.514

1058

250.389Model Invariant

p-Value

2

36

0.056

NO

32.199

36

0.650

YES

6. Discussion
Based on the key objectives of this research, as well as the data analysis results, thefindings are discussed in the sequence of the stimulus-organism-response of customers’ psychological shopping processes via LSSAs under lockdown measures during the COVID-19
pandemic. Specifically, the significant determinants of each subgroup’s stimulus-organismresponse components were summarized in Table 10.

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Table 10. The significant determinants of each subgroup.
Mediator

GenderAge

Subgroup

Stimulus

Organism

Response

Male

Performance expectancy;
Effort expectancy;
Social influence;
Trust

Flow

Behavioral Intention

FemalePerformance expectancy;
Hedonic motivation;

Flow

Perceived value;
Behavioral Intention≤35

Performance expectancy;
Social influence;
Hedonic motivation;
Trust

Flow

Perceived value;
Behavioral Intention>35

Performance expectancy;
Social influence;
Trust

Flow

Perceived value;
Behavioral Intention

The variables from the revised UTAUT2 model, as the stimulus in users’ psychological processing, demonstrated variance in different sub-models. Specifically, except for
performance expectancy, which had significant effects on flow in all subgroups, the other
path effects of antecedences of flow presented differently in different subgroups. Effort
expectancy only significantly affected flow in the male subgroup, contrary to the previous
findings [45]. This study found that male customers’ engagement and immersion weremore determined regarding the understandability, accessibility and operability of LSSAs.
On the other hand, effort expectancy had a positive influence on performance expectancy inall subgroups, which was consistent with previous findings that when customers perceive
the ease of using LSSAs, they will feel using an LSSA is a useful and efficient way to shop
online [26,43,79]. LSSA providers should maintain applications with easily understandable
interfaces and functions to increase the accessibility of LSSAs.
Meanwhile, social influence had a significant effect on flow in all subgroups except for
female customers, which was consistent with the findings of Liébana-Cabanillas, SánchezFernández and Muñoz-Leiva [41], but contrary to the results of Pascual-Miguel, AgudoPeregrina and Chaparro-Peláez’s study [45]. The results of this study validated that female
customers are more influenced with more difficulty by other relevant people when theypurchase through LSSAs during the COVID-19 pandemic lockdown period. However,recommendations and support from relevant important people significantly formulate
users’ mental cognition in the male subgroup and both age subgroups, which means theywould feel less anxiety and uncertainty if provided with the support of important, relevant
people when using LSSAs during the pandemic lockdown period [25,80]. When customers’
close friends or families are engaged in LSSAs, they are more inclined to participate andinteract with sellers on LSSAs [16]. Therefore, word-of-mouth marketing is an efficientand reliable way to establish the excellent reputation of LSSAs, to increase male customers’
engagement and to increase enjoyment when shopping via LSSAs during the pandemic
lockdown period.
Moreover, hedonic motivation had more significant influences on the younger female
group when they were shopping via LSSAs during the pandemic lockdown period. Therefore, the enjoyment of live-streaming content, as well as its entertainment features, areessential to optimize users’ experience and increase engagement, especially for younger
female customers. Furthermore, trust had a significant impact on flow in all age subgroups
and the male group. Specifically, male customers paid more attention to the trustworthiness and security of LSSAs [44]. On the other hand, trust was an essential antecedent offlow in all age ranges, which was opposite to the findings of Liébana-Cabanillas, SánchezFernández and Muñoz-Leiva (2014), where trust was more affected by younger groups [41].
Under the situation of social commerce lockdown, live-streaming production demonstration increased customers’ perceived trust by providing reliable control, which positively
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influenced consumers’ shopping experiences during the pandemic lockdown period [38].
Likewise, trust had a significant influence on performance expectancy in all subgroups.
When users perceive a higher sense of trust in shopping via LSSAs under the pandemic lockdown measures, their holistic mental perceptions of the utility of technology will increase
accordingly [80]. Therefore, information accuracy, information security and customers’
privacy should be guaranteed by LSSA providers, especially for male customers [39].
Furthermore, flow, as an organism of users’ psychological processing, had the mostsignificant effects on perceived value and behavioral intention in all subgroup models,
which is consistent with previous findings, which suggest that engagement and an immersive experience can significantly formulate users’ mental and physical reactions to
shopping via LSSAs during the pandemic lockdown period [16,38,39]. Specifically, flow as
mediator in the proposed model performed diversely in each subgroup, the proportion of
the variance for flow in gender subgroup model and age subgroup model being 0.70 and
0.58, respectively, which demonstrates the higher explanatory power in the model withthe gender subgroup than the model with the age subgroup. Meanwhile, comparing thecoefficients of flow with other variables between four subgroup models, flow has the mostsignificant relations with effort expectancy, social influence, trust and behavioral intention
in the male subgroup, while flow has the strongest relations with hedonic motivation andperceived value in the female group. Performance expectancy has the most significant effecton flow in the young age subgroup. The result indicates that functional and environmental
factors affect young male customers’ immersive experience more, while the entertainment
factor influences female users’ flow experience more. When customers are immersed inlive-streaming shopping, they tend to escape from the pandemic situation and forget abouttime and problems, which irrelevant things do not easily disturb [10]. Therefore, optimizing customer engagement and interaction in live-streaming demonstrations is necessary
to increase users’ immersive shopping experiences via LSSAs in the pandemic lockdown
situation [38].
In addition, in response components, perceived value significantly determined customers’ behavioral intention regarding the use of LSSAs in all subgroups except malecustomers. When customers feel pleasure when purchasing through LSSAs during the
pandemic lockdown period, they will perceive higher multi-dimensional benefits of LSSAs,
including utilitarian, hedonic and social benefits, which in turn significantly determine
customers’ usage intention [16,50,59]. Male customers’ holistic perceptions of the benefitsof LSSAs might be indirectly influenced by their perceptions of technology [9]. Therefore,
it is necessary to optimize the interfaces and functions of LSSA-services for improving thepracticability, usability and creditability of LSSAs, to attract male customers’ engagement.
These methods can increase users’ enjoyment, recognition and satisfaction when shopping
through LSSAs, contributing towards formulating their actual usages.
7. Theoretical and Practical Implications
7.1. Theoretical Implications
As shopping via live-streaming is becoming an immensely popular social and commercial phenomenon, the factors determining customers’ intentions regarding shopping
via live-streaming apps have attracted increasing attention in recent years. Current researchhas demonstrated novel insights into explaining customers’ psychological shopping processes via LSSAs during the COVID-19 pandemic lockdown period. This study contributes
three theoretical implications. Firstly, this study bridges a gap in the existing literature
by initially evaluating the moderating effects of gender and age on the determinants ofcustomers’ psychological processing in the use of LSSAs, which enriches the literature of
relevant fields and verifies previous findings regarding the moderating effects of genderand age. Comparing the influences of different moderators, especially gender and age, oneach path provides a better understanding of the effects of customers’ demographic characteristics on LSSA adoption. Secondly, this study contributes to theoretical developmentby extending the SOR framework with UTAUT2 and Flow Theory. Notably, this study

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integrates the variables from the revised UTAUT2 model as the stimulus component of
the SOR framework, and Flow Theory supports the organism in the SOR framework as amediator of the adoption model. The comprehensive model was validated in this study tosupport the understanding of applying the SOR framework in the LSSA adoption context.
Thirdly, the current study successfully explains that customers’ psychological processes
experienced while shopping via LSSAs under the pandemic lockdown condition are induced by perceived technological perceptions (performance expectancy, effort expectance,
hedonic motivation) and environmental perceptions (social influence, trust), mediatedby mental cognition (flow) and demonstrated by actual responses (perceived value andbehavioral intention). This finding generates new insights for future research to assess
various connections, interactions, and relationships among the variables between or withindifferent components in the SOR framework for different technology adoption studies.
7.2. Practical Implications
This study’s results are essential for LSSA-service providers, LSSA sellers, streamers and relevant stakeholders interested in the live-streaming commerce industry. The
current study supports stakeholders relevant to LSSAs in understanding the behaviors
of different customer demographics influenced by the moderating effects of gender andage. In particular, hedonic motivation, trust and social influence had the most significant
differences in male and female groups. This study provides insights for LSSA stakeholders,encouraging them to consider gender differences affecting various antecedents at different
stages of psychological processes experienced by customers while shopping via LSSAs,
helping them to create or manage a better strategy for their target customers in the future.
For example, LSSA providers and vendors should focus on maintaining a relaxing andcomfortable live-streaming environment and guaranteeing the originality and fascinationof the live-streaming context to optimize entertainment for attracting female customers.
Moreover, this study helps LSSA-platform providers, streamers and LSSA sellers,
acting as a guidebook to understanding each component in the mental processes undergone by customer when using LSSAs for shopping during the pandemic lockdown period.
Based on the findings, flow had both significant effects on perceived value and behavioral intention. LSSA-platform providers should emphasize user-centered principles toguarantee the reliability, convenience and efficiency of LSSA-services to meet customers’
expectations and requirements, helping to formulate an immersive environment for customers to improve their engagement and optimize their mental cognition of shopping
through LSSAs. Streamers and LSSA sellers should ensure entertainment, instantaneity andaccuracy of interactions with customers to formulate a pleasant and enjoyable environment
for optimizing their shopping experience when using LSSAs. Furthermore, the current
research contributes a framework for the investigation of customers’ mental processes
under a specific environmental condition. This study proposed a critical procedure (technological and environmental perceptions → engagement and mental cognition → reaction)
to evaluate customers’ psychological processes. Meanwhile, the assessment and evaluationof the moderating effects of gender and age applied in this study provide a reference withwhich to analyze demographically different customers’ behaviors. Relevant stakeholders
can generate particular strategies for their different customers based on the variation of themoderating effects of gender and age on different determinants.
8. Conclusions
Shopping via live-streaming is booming after the lockdown measures of the COVID-19
pandemic. This study investigated the psychological processes undergone by customer
shopping via LSSAs during the COVID-19 pandemic lockdown period in China. The
proposed model extended the SOR framework with UTUAT2 and Flow Theory and wastested by CB-SEM with 374 valid data, with four subgroups divided by age and gender.

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8.1. Result of the Study
The empirical results demonstrate that flow as a mediator had the most significant
influence on users’ responses. Technological and environmental perceptions significantly
formulate customers’ engagement and immersive experience, which determine their behaviors. This study validates that gender has significant moderating effects on effectexpectancy, hedonic motivation, trust, social influence and perceived value. Specifically,effect expectancy, social influence and trust had significant effects on flow in the male group.
On the other hand, hedonic motivation and perceived value were found to significantly
affect female customers’ psychological processes when shopping via LSSAs. Moreover,hedonic motivation has a more significant effect on flow in younger customers than inolder customers.
The current research provides a better understanding of customers’ psychologicalprocesses under a particular condition, namely, the COVID-19 lockdown situation. The
current study contributes a theoretical development, helping to integrate psychologicalframework with technological adoption models, and provides a practical guideline on
investigating the psychological processes experienced by customer who shopped via LSSAs
under the pandemic lockdown situation. This helps by supporting relevant researchersand stakeholders in understanding customers’ behaviors under a specific condition.
8.2. Limitations and Future Research
Although the current study proposed a rigorous framework of psychological processing on behalf of customers to adopt LSSAs, four limitations are summarized as follows withcorrespondent recommendations for future research. First, this study’s target location was
China, which indicates the limited generalizability of results in different cultures, regions
and countries. Therefore, future researchers are recommended to pay more attention toinvestigating relevant studies in various regions and cultural backgrounds, as well as tomake comparisons between locations with different cultures. Second, moderators analyzed
in this study only consisted of the participants’ basic demographic characteristics, namely,
gender and age. Various moderators can contribute different moderating effects to different constructs in the model. Thus, future research is recommended to investigate users’
behaviors under various moderating effects, such as experience, educational background,
Hofstede’s cultural values, etc. Third, this research did not distinguish the types of LSSAs
in the study. The different types of LSSAs may lead to different results [6].
Consequently, future research is recommended to distinguish the differences between
various technologies and platforms. Last, this study conducted a four-week data collection
during the COVID-19 pandemic lockdown period, indicating the limitation of a short
investigation period for generalizing an overall analysis in different scenarios. Thus, along-term approach, as well as a comparison between customers’ different stages of shopping experiences via LSSAs and under different situations might be several meritorious
directions for future research.
Author Contributions: Conceptualization, Y.Z. and F.B.; methodology, Y.Z.; software, Y.Z.; validation,
Y.Z.; formal analysis, Y.Z.; investigation, Y.Z.; resources, Y.Z.; data curation, Y.Z.; writing—originaldraft preparation, Y.Z.; writing—review and editing, Y.Z.; visualization, Y.Z.; supervision, F.B.; project
administration, Y.Z. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Data available on request due to restrictions eg privacy or ethical.
Conflicts of Interest: The authors declare no conflict of interest.

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Appendix A
Table A1. Online questionnaire.
Dear participant:Thank you for taking a few minutes to participate in this questionnaire! The purpose of this questionnaire is to collect thefactors of shopping via live-streaming shopping app. (i.e., Douyin app, Kuaishou app, Taobao live, etc.). The questionnaire isanonymous. The data obtained is only for academic research. Please feel free to fill in the questionnaire according to yourpersonal situation.
Thank you for your support!
Part 1–Demographic Information
MeasureItem
Male

GenderFemale
<20
21–35

Age

36–50
>51
At least 1 time per 1 day
At least 1 time per 1 week
Frequency of using LSSAs
during lockdown period

At least 1 time per 2 weeks
At least 1 time per 1 month
At least 1 time per 3 months
At least 1 time per 6 months
Never used during lockdown period
Part 2–Structural Evaluation

Construct
Performance
expectancy (PE)

Effort expectancy (EE)

Social influence (SI)

Hedonic motivation (HM)

Items

References

PE1: I feel using LSSA is a useful way of shopping during lockdown period.
PE2: Using LSSAs makes purchasing easier during lockdown period.
PE3: Using LSSAs improves my shopping efficiency during lockdown period.
PE4: Using LSSAs makes shopping more convenient during lockdown period.

[13]

EE1: Learning how to use LSSAs is easy.
EE2: It is easy to follow all the functions of LSSAs.
EE3: It is easy to become skillful at using LSSAs.
EE4: Interaction with LSSAs is clear and comprehensible.

[13]

SI1: People who are important to me (e.g., family members, close friends, andcolleagues) recommend I use LSSAs for shopping during lockdown period.
SI2: People who are important to me view LSSA as beneficial way for shopping
during lockdown period.
SI3: People who are important to me think it is a good idea to use LSSAs for shopping
during lockdown period.
SI4: People who are important to me support my use of LSSAs.
HM1: Shopping via LSSAs is entertaining during lockdown period.
HM2: Shopping via LSSAs relaxes me during lockdown period.
HM3: Shopping via LSSAs gives me pleasure during lockdown period.
HM4: Activities (e.g., flash sales, freebies) on LSSAs make me excited.
HM5: I enjoy shopping via LSSAs during lockdown period.

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[13]

[1,13]

Int. J. Environ. Res. Public Health 2021, 18, 13004

Table A1. Cont.
Dear participant:Thank you for taking a few minutes to participate in this questionnaire! The purpose of this questionnaire is to collect thefactors of shopping via live-streaming shopping app. (i.e., Douyin app, Kuaishou app, Taobao live, etc.). The questionnaire isanonymous. The data obtained is only for academic research. Please feel free to fill in the questionnaire according to yourpersonal situation.
Thank you for your support!
Part 2–Structural Evaluation
Construct

Trust (TR)

Flow (FL)

Perceived value (PV)Behavioral intention (BI)Items
TR1: I believe LSSAs are competent and effective in handling customers’
shopping activities.
TR2: I believe LSSAs keep customers’ interests in mind.
TR3: I trust the product demonstration from high-reputation sellers on LSSAs.
TR4: I believe that the products I purchase from LSSAs will be the same as thosedemonstrated on LSSAs.
TR5: Overall, I believe LSSAs are trustworthy way for shopping during
lockdown period.
FL1: When using LSSAs, my attention is focused on the shopping activities.
FL2: When shopping via LSSAs, I do not realize how time passes.
FL3: Using LSSAs gives me a temporary escape from the real-world
pandemic situation.
FL4: While shopping through LSSAs, I am able to forget my problems.
FL5: When shopping via LSSAs, I often forget the work I should do.
PV1: Using LSSAs makes shopping more efficient and safer during lockdown period.
PV2: Shopping via LSSAs would allow me to take advantage of additional
promotions during live-streaming.
PV3: Shopping via LSSAs provides me with a lot of enjoyment, or gives me happinessduring lockdown period.
PV4: Given the time I need to spend doing it during lockdown period, shopping via
LSSAs is worthwhile to me.
BI1: Shopping via LSSAs had become one of consumption and entertainment patterns
for me.
BI2: Given the opportunity, I will continuously shop via LSSAs in future.
BI3: I would like to recommend others to use LSSAs for shopping during
lockdown period.

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[50]

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International Journal ofEnvironmental Researchand Public Health
ArticleFacilitators and Reducers of Korean Travelers’ Avoidance/Hesitation
Behaviors toward China in the Case of COVID-19

Heesup Han 1 , Chen Che 2

and Sanghyeop Lee 3, *
1
2
3

*

Citation: Han, H.; Che, C.; Lee, S.
Facilitators and Reducers of Korean
Travelers’ Avoidance/Hesitation
Behaviors toward China in the Caseof COVID-19. Int. J. Environ. Res.
Public Health 2021, 18, 12345. https://doi.org/10.3390/ijerph182312345

College of Hospitality and Tourism Management, Sejong University, Seoul 143-747, Korea;
heesup.han@gmail.com
College of History and Tourism Culture, Inner Mongolia University, Hohhot 010021, China;
cecencan@naver.com
Major in Tourism Management, College of Business Administration, Keimyung University,
Daegu 42601, Korea
Correspondence: leesanghyeop@kmu.ac.kr

Abstract: Given that little is known about overseas travelers’ responses and behaviors toward China
after the outbreak of COVID-19, this study aimed to uncover risk perception factors and investigate
its role in Korean travelers’ avoidance/hesitation behaviors toward China as an international tourism
destination in the case of the COVID-19 pandemic. To explore the relationship with risk perception,
anticipated emotion and avoidance/hesitation behavior, a quantitative method along with an online
survey was employed. This focus was on Korean tourists who had traveled to China at least once.
Findings revealed that risk perception and negative anticipated emotion are vital facilitators of
avoidance/hesitation behaviors, and that positive anticipated emotion reduces such behaviors. The
efficacy of a higher-order structure of risk perception, which encompasses six dimensions, was also
demonstrated. In addition, destination attachment lowered the influence of risk perception on theformation of avoidance/hesitation behaviors. Overall, our results will help tourism researchers andpractitioners understand what factors drive and reduce international travelers’ avoidance/hesitation
behaviors toward China in the post-pandemic world. Implications for theory and practice are offered.
Keywords: risk perception; China as an international tourism destination; anticipated emotions;destination attachment; post-pandemic

Academic Editors: Sachiko Kodera
and Essam A. Rashed

1. Introduction
Received: 14 October 2021
Accepted: 22 November 2021
Published: 24 November 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

The impact of a coronavirus diseases 19 (COVID-19) on the entire world is substantial [1–3]. The pathogenic influence of COVID-19 has been drastically increased since China
reported the first confirmed case in late 2019 [4,5]. This virus fast became a huge threat tohuman health [6]. Moreover, ever since the detection of the COVID-19, this disease has
considerably affected human mobility [7]. COVID-19 had a huge adverse effect on the
tourism sector [1,8]. Particularly, the international tourism industry has been massively
reduced due to border closing, lockdowns, and bans on traveling [3,5]. The COVID-19
characteristics of high prevalence, broad distribution, and geographical variables madethis situation worse for the tourism industry [3,4,7].
Until 11 November 2021, the World Health Organization (WHO) reported that there
had been 25.127 million confirmed cases of COVID-19 in the world, including 0.507 million
deaths [9]. In China, there have been 3283 confirmed cases of COVID-19 [10]. Most
overseas travelers have inevitably restrained themselves from visiting international tourism
destinations until the pandemic is entirely under control [1,8]. COVID-19 has also blockedinternational tourists’ visit to China. The global concern for human health derived fromthe pandemic significantly reduced international tourism demand throughout the world.
According to Chen et al. [11], there is a high possibility that another new type of coronavirus
will emerge in the future because of rapid climate change, ecological problems, fast-267

Int. J. Environ. Res. Public Health 2021, 18, 12345

increasing flows of human populations, and increase in human–animal interactions. Indeed,
many countries are shifting to the with-corona era.
Irrefutably, there exists a critical association between COVID-19 and tourist responses/
behavior [7,8]. Yet scant research has uncovered the possible influence of perceived risk pertinent to the disease on overseas travelers’ anticipated emotions and avoidance responses
to traveling to China. Especially, very little is known about Korean international travelers’
responses and behaviors toward traveling to China since the emergence of COVID-19. For
Korean travelers, China is one of the preferred destination countries. Indeed, the statisticsof the Korea Tourism Organization [12] showed that about 4,346,567 Koreans visited China
in 2019 before the outbreak of the pandemic. They traveled to China for diverse purposes(wellness, sightseeing, reputation/image of local destinations, low price, shopping, leisure,and foods) [12]. Undoubtedly, the image and popularity of China as a tourism destination
has been considerably influenced by COVID-19. Exploring Korean international tourists’perception of traveling to China in the post-pandemic world is vital to better understandtheir emotional tendencies and behaviors.
The objectives of this study were therefore to identify Korean travelers’ perception
regarding possible risks of traveling to China in the post-pandemic world and to explore
the influence of such risk perception and its dimensions (i.e., human crowding risk, spatial
crowding risk, quality risk, psychological risk, health and safety risk, and financial risk) on
the formation of avoidance/hesitation behaviors. In addition, this research aimed to uncover
the efficacy of a higher-order structure of risk perception. Moreover, this study was designed
to unearth the moderating impact of destination attachment and to investigate the mediating
effect of positive and negative anticipated emotions. Lastly, this research assessed the impactof Korean travelers’ avoidance/hesitation behaviors in the COVID-19 era.
2. Literature Review
2.1. Risk Perception and Its RoleThe criticality of risk perception has long been emphasized in various sectors due toits considerable influence on individuals’ behaviors [13–15]. Particularly in the hospitality/tourism literature, risk perception and its importance are extensively stressed [13,16,17].
Undoubtedly, risk perception that has multiple dimensional characteristics is the major
constituent of traveler approach/avoidance decision formation [17,18]. The vital facet ofrisk perception in tourism as well as in consumer behavior and psychology is uncertainty
and an individual’s apprehension of it [15,19]. Such uncertainty is negatively linked to
anticipated responses from individuals [19]. The critical aspects of risk perception in an
unsafe consumption environment (e.g., the pandemic world) can be crowding risk (human and spatial), quality risk, psychological risk, health and safety risk, and financial
risk [15,16,18,20]. Given this, the concept of risk perception in the present research indicatestravelers’ uncertainty about tourism-destination performances and their cognitive anxiety
regarding the disparity between such performances and travelers’ expectations.
An extensive amount of the extant literature has assessed the possible effect of risk
perception on individuals’ approach/avoidance responses and behaviors [14,17,21]. Olya
and Altinay [22] explored how risk perception influences traveler post-purchase behaviors either in a positive or negative way. In their investigation on international travelers’
behaviors, Al-Ansi et al. [13] found risk perception as a vital contributor to behavioral
intention generation. More interesting in this research is how to manage consumers’ perceived risk to improve their satisfaction and trust. Law [23] examined travelers’ decision
formation for overseas destination choices when there is any probability of the occurrencesof virus infection, terrorist attack, and natural disasters. He found that ones’ international tourism decision is significantly affected by risk perception level. Scholars in recent
research have also revealed that risks related to crowdedness, quality/performance, mental health/personal psychology, physical health/safety, and money are of importance assuch risks elicit positive and negative emotional evaluations and avoidance/hesitation
behaviors [16,17,20]. Olya and Al-Anish [16] examined how health risk, quality risk, and268

Int. J. Environ. Res. Public Health 2021, 18, 12345

psychological risk positively influence tourist satisfaction, and financial risk significantly
affected tourist behavioral intention. Yu et al. [17] point out the risk perception (psychological and financial risk) of COVID-19, which strongly negatively influences tourist
behavioral intention. The studies discussed above support the possible linkages amongrisk perception, anticipated emotional responses, and avoidance/hesitation behaviors. In
addition, the finding of a recent study evidenced the efficacy of a second-order structure ofrisk perception in the tourism context [13]. Therefore, we developed hypotheses as follows:
Hypothesis 1 (H1). Risk perception has a significant influence on positive anticipated emotion.
Hypothesis 2 (H2). Risk perception has a significant influence on negative anticipated emotion.
Hypothesis 3 (H3). Risk perception has a significant influence on avoidance/hesitation behavior.
2.2. Anticipated Emotions
For the last few decades, anticipated emotions have been an important concept incustomer behavior and tourism [24,25]. Anticipated emotions are described as anticipatedaffective process. Perugini and Bagozzi [26] indicate the prospect of feeling favorable
or unfavorable affects when conducting or not conducting a particular action [27]. This
conceptualization is in line with Han [28] who described these anticipated emotions are
one’s expected positive/negative feelings after performing or not performing a specificaction. In general, researchers agree that anticipated emotions have two dimensions(positive and negative) [28–30]. These positive and negative forms of anticipated affective
responses are believed to be important determinants of consumer behaviors [25].
There has been a significant improvement in extant socio-psychological theories/modelsof anticipated emotion, such as the theory of planned behavior, norm activation theory,and the value-belief-norm model [24,26,30]. Rosenthal and Ho [24] found that personalnorms have a mediating effect on anticipated negative emotions and awareness of consequences. According to their two studies, Perugini and Bagozzi [26] demonstrated that
anticipated emotions are an essential factor of desire. Lu et al. [30] distinguish anticipatedguilt and anticipated pride in analyzing employees’ pro-environment behavior. Indeed,
theoretical/conceptual frameworks encompassing positive and negative forms of anticipated emotion, which were designed for self-interest/pro-social consumer behaviors, havebenefited from the increase in prediction power for consumer approach/avoidance responses and behaviors [26,28]. The inclusion of positive and negative anticipated emotions
often increases the ability of a theoretical model in explicating individuals’ behaviors in a
product-purchase or consumption situation [29,31]. Findings of the existing studies in theextant literature have also revealed that positive and negative anticipated emotions playa crucial role in inducing avoidance or approach behaviors for products/services [24,31].
Therefore, we developed hypotheses as follows:
Hypothesis 4 (H4). Positive anticipated emotion has a significant influence on avoidance/
hesitation behavior.
Hypothesis 5 (H5). Negative anticipated emotion has a significant influence on avoidance/
hesitation behavior.
2.3. Destination Attachment
Destination attachment has received increasing attention from tourism marketers
and academics since it contributes considerably to enhancing travelers’ favorable decisions/behaviors regarding a tourism destination (retention, loyalty behaviors, protection
behaviors, word-of-mouth, sustainable actions at a destination) [24,32–34]. Destination
attachment is a concept that describes the emotional tie between visitors and place [35].
Similarly, Rosenthal and Ho [24] described destination attachment as an affective bond269

Int. J. Environ. Res. Public Health 2021, 18, 12345

between the place and its visitors. This concept is often an alternative for terms such as
sense of belonging and involvement [28].
Sohn and Yoon [36] identified five characteristics of risk perception (social, physical,
financial, health and psychological risk). They argued that destination attachment had amoderate influence on risk perception (physical and health) and destination image. Pangaribuan et al. [37] demonstrated that risk perception had a moderate effect on destination
attachment and voluntary behavior. In general, risk perception is important for tourist
avoidance/hesitation behavior [38]. According to Pradhananga and Davenport [34] andFournier and Lee [32], a traveler with a strong attachment to a certain destination willmore actively practice loyalty and citizenship behaviors regarding that destination (e.g.,
volunteering, repeat visit, recommendation). Indeed, when visitors’ level of destination
attachment is high, they are likely to show positive emotional responses, feel involvement,
and show approach behaviors towards the destination [28,32,34,35]. Undoubtedly, destination attachment is a significant contributor influencing the formation of emotional
reactions and behavioral responses [24]. The contributing role of attachment/involvement
as a moderator is evident in consumer behavior and tourism [32,33]. Accordingly, thefollowing hypotheses were developed:
Hypothesis 6a (H6a). Destination attachment has a moderating influence on relation between
risk perception and positive anticipated emotion.
Hypothesis 6b (H6b). Destination attachment has a moderating influence on relation between
risk perception and negative anticipated emotion.
Hypothesis 6c (H6c). Destination attachment has a moderating influence on relation between
risk perception and avoidance/hesitation behavior.
2.4. Theoretical Model
The research model is exhibited in Figure 1. Our proposed framework contains
risk perception and anticipated emotions (positive and negative) as predictors of traveler
avoidance/hesitation behaviors and encompasses destination attachment as a moderator.
Human crowding risk, spatial crowding risk, quality risk, psychology risk, health andInt. J. Environ. Res. Public Health 2021,safety
18, x risk, and financial risk are incorporated as the first-order factors of risk5 of
13
perception.
In addition, the model includes six research hypotheses within its theoretical framework.

Figure 1.
model.
Figure1. TheTheproposed
proposedmodel.

3. Methods
3.1. Measurement Development 270
The measures of risk perception and its dimensions are generated based on a thor-

Int. J. Environ. Res. Public Health 2021, 18, 12345

3. Methods
3.1. Measurement Development
The measures of risk perception and its dimensions are generated based on a thoroughreview of the literature [13,15,39–41]. Additionally, several measurement items were addedon the basis of qualitative interviews (i.e., “The flow of people in the tourist areas of China
is slow because of too many people”, “Hotels/restaurants/shopping places are in general
not spacious enough”, “I am worried that the quality of tourism products in China will
be lower after the COVID-19 pandemic ends in the near future”, and “The thought oftraveling to China causes me to experience unnecessary tension although the COVID-19
pandemic may end in the near future”). The person-to-person interviews were conductedwith actual Korean travelers who often visited China for international tourism before theoutbreak of the pandemic. Overall, three items were utilized to measure human crowding
risk (i.e., “Too many people are in tourist sites in China”, “Because of the huge numberof tourists, visitors feel crowded in many tourist sites in China”, “The flow of people in
the tourist areas of China is slow because of too many people”), and three items were
employed to assess spatial crowding risk (i.e., Because of spatial crowdedness, visitors
often feel stuffy in many tourist sites in China”, “Due to little space, moving around is
not easy in many of the tourist sites of China”, “Hotels/restaurants/shopping places arein general not spacious enough”. Additionally, two items and three items were utilized
to measure quality risk (i.e., “I am concerned with the lower quality of tourism products
in China after the COVID-19 pandemic ends in the near future”, “I am worried that thequality of tourism products in China will become lower after the COVID-19 pandemic
ends in the near future”. and psychological risk (i.e., “The thought of traveling to China
makes me feel anxious although the COVID-19 pandemic may end in the near future.”,“The thought of traveling to China makes me feel psychologically uncomfortable although
the COVID-19 pandemic may end in the near future”, “The thought of traveling to China
causes me to experience unnecessary tension although the COVID-19 pandemic may
end in the near future”), respectively. Health and safety risk (i.e., “Traveling to China,
although the COVID-19 pandemic may end in the near future, is still unsafe”, “Travelingto China, although the COVID-19 pandemic may end in the near future, is still unhealthy”,“Traveling to China, although the COVID-19 pandemic may end in the near future, is stillrisky”) and financial risk (i.e., “I am afraid there will be considerable extra expenses inmany tourist sites in China after the COVID-19 pandemic ends in the near future”, “I worry
that visiting China will involve unexpected extra expenses after the COVID-19 pandemic
ends in the near future”) were measured with three items and two items, respectively.
The measures for other constructs were taken from the existing studies [26,31,32,42,43].
Specifically, destination attachment was assessed with three items. Four items were used tomeasure positive anticipated emotions whereas three items were used to evaluate negative
anticipated emotions. Moreover, avoidance/hesitation behavior was measured with fiveitems. That is, multiple measures with a seven-point Likert-scale for all constructs wereused in the present research.
3.2. Data Collection Procedure and Sample Characteristics
To test the proposed hypothesized framework, this study employed a Web-based
survey method. Using an online market research firm’s system, the survey link was
generated. Afterwards, using social media (e.g., kakaotalk, Instagram), this was frequently
used by Korean people to send this link to potential survey participants who had traveled
to China at least once. Before sending the questionnaire link, respondents were asked about
their willingness to participate and their experience of visiting China. The qualified survey
participants were asked to read thoroughly the study description and questions whenfilling out the questionnaire. To complete this survey, the participants had to answer all
inquiries in the questionnaire. A total of 450 survey questionnaires was collected. Amongthem 429 usable responses were gathered through this process. These cases were used fordata analysis.

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Among 429 survey participants, 50.6% were male travelers whereas 49.4% were female
travelers. The participants’ age fell between 20–73 years old with a mean age of 40.1 years.
Specifically, about 23.3% were less than 29 years old; 25.6% were between 30–39 years
old; 24.7% were between 40–49 years old; and 26.3% were above 50 years old. About
94.6% of the participants reported that they had visited China within the last three years.
All respondents had visited to China within the last 5 years. Regarding education level,
69.2% were college graduates, followed by graduate-degree holders (16.8%), high-school
graduates (9.3%), and others (4.7%). About 56.9% of the survey participants reported thatthey were married whereas 43.1% reported other forms of marital status. In terms of annual
income level, about 38.9% indicated an income between 30,000–50,000 USD, followed byless than 30,000 USD (28.0%), 50,000–80,000 USD (23.3%), and over 80,000 USD (9.8%).
4. Results
4.1. Reliability and Validity Assessment
A confirmatory factor analysis was performed to create a measurement model. The
model assessment revealed that a satisfactory model fit (χ2 = 969.977, df = 387, p < 0.001,
χ2 /df = 2.506, RMSEA = 0.059, CFI = 0.963, IFI = 0.963, TLI = 0.956). All items were loaded
to their relevant latent factor in a significant manner (p < 0.01). As shown in Table 1, allcomposite reliability values (human crowding = 0.925; spatial crowding = 0.831; quality
risk = 0.955; psychological risk = 0.946; health and safety risk = 0.959; financial risk = 0.914;
positive anticipated emotion = 0.957; negative anticipated emotion = 0.885; destination
attachment = 0.969; and avoidance/hesitation behavior = 0.967) exceeded the suggested
threshold of 0.70 [44]. This result evidenced internal consistency of the multiple-item
measures. Average variance extracted values were calculated. All values (human crowding= 0.804; spatial crowding = 0.622; quality risk = 0.913; psychological risk = 0.853; healthand safety risk = 0.886; financial risk = 0.841; positive anticipated emotion = 0.846; negative
anticipated emotion = 0.720; destination attachment = 0.913; and avoidance/hesitation
behavior = 0.853) exceeded the recommended threshold of 0.50 [44]. In addition, the values
were greater than between-construct correlations (squared), as evidenced in Table 1. Thisevidenced the convergent and discriminant validity of the multiple-construct measures.
4.2. Structural Model Assessment and Hypothesis TestingA structural equation modeling was conducted with the use of Maximum likelihood
estimation (see Table 2). The result demonstrated a satisfactory model fit (χ2 = 1079.063,
df = 337, p < 0.001, χ2 /df = 3.202, RMSEA = 0.072, CFI = 0.946, IFI = 0.946, TLI = 0.939)(see Figure 2). The second-order model for risk perception indicates that the global higherorder factor is significantly related to the six first-order variables (human crowding, spatial
crowding, quality risk, psychological risk, health and safety risk, and financial risk). The
coefficients were 0.419 (p < 0.01) for human crowding risk, 0.457 (p < 0.01) for spatial
crowding risk, 0.405 (p < 0.01) for quality risk, 0.961 (p < 0.01) for psychological risk, 0.987(p < 0.01) for health and safety risk, and 0.531 (p < 0.01) for financial risk, respectively.
The higher-order latent variable accounted for about 17.6%, 20.9%, 16.4%, 92.4%, 97.5%,
and 28.2% of the total variance for human crowding, spatial crowding, quality risk, psychological risk, health and safety risk, and financial risk, respectively. This evidenced
the adequacy and effectiveness of the higher-order structure of risk perception within the
proposed theoretical framework.

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Table 1. Measurement model and data quality assessment results (n = 429).
Constructs

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(1) Human crowding risk

1.000

–

–

–

–

–

–

–

–

–

(0.274) b

1.000

–

–

–

–

–

–

–

–

(3) Quality risk

0.223
(0.050)0.363(0.132)

1.000

–

–

–

–

–

–

–

(4) Psychological risk

0.403
(0.162)0.412(0.170)0.380(0.144)

1.000

–

–

–

–

–

–

(5) Health and safety risk

0.378
(0.143)0.431(0.186)0.375(0.141)0.905(0.819)

1.000

–

–

–

–

–

(6) Financial risk

0.235
(0.055)0.375(0.141)

0.415(0.172)0.456(0.208)

0.501
1.000
(0.251)

–

–

–

–

(7) Positive anticipated emotion−0.269(0.072)−0.246(0.061)−0.145(0.021)−0.564(0.318)−0.569 −0.217
1.000(0.324) (0.047)

–

–

–

(8) Negative anticipated emotion

0.159(0.025)0.251(0.063)0.209(0.044)0.528(0.279)0.573 0.336 −0.649
1.000(0.328) (0.113) (0.421)––(9) Destination attachment−0.262(0.069)−0.207(0.043)−0.080(0.006)−0.533(0.284)−0.532 −0.180 0.743 −0.482
1.000(0.283) (0.032) (0.552) (0.232)(10) Avoidance/Hesitation behavior

0.363(0.132)0.338(0.114)0.238(0.057)0.751(0.564)0.780 0.317 −0.674 0.642 −0.631
1.000(0.608) (0.100) (0.454) (0.412) (0.398)

Mean

5.664

5.117

4.622

5.677

5.601

4.944

3.385

4.759

3.045

5.509

Standard deviation

1.147

1.170

1.624

1.269

1.290

1.423

1.539

1.383

1.603

1.367

CR

0.925

0.831

0.955

0.946

0.959

0.914

0.957

0.885

0.969

0.967

AVE

0.804

0.622

0.913

0.853

0.886

0.841

0.846

0.720

0.913

0.853

0.523 a(2) Spatial crowding risk–

Note1: a Correlations between variables are below the diagonal. Note2: b Squared correlations between variables are within parentheses.
Note3: Goodness-of-fit statistics: χ2 = 969.977, df = 387, p < 0.001, χ2 /df = 2.506, RMSEA = 0.059, CFI = 0.963, IFI = 0.963, TLI = 0.956.

The hypothesized influence of risk perception on anticipated emotions and avoidance/hesitation behavior was tested. Our findings showed that risk perception exerted asignificant influence on positive anticipated emotion (β = −0.614, p < 0.01) and negative
anticipated emotion (β = 0.635, p < 0.01). Additionally, risk perception affected avoidance/hesitation behavior significantly (β = 0.555, p < 0.01). Hypotheses 1, 2, and 3 werehence supported. The proposed effect of anticipated emotions was assessed. Results
revealed that both positive anticipated emotion (β = −0.237, p < 0.01) and negative anticipated emotion (β = 0.202, p < 0.01) had a significant impact on avoidance/hesitation
behavior. Therefore, Hypotheses 4 and 5 were supported. About 37.8% and 40.3% of
the variance in positive and negative anticipated emotions were accounted for by risk
perception. In addition, these variables explained 74.5% of the total variance in avoidance/hesitation behavior. A close examination of indirect relationship showed that risk
perception contained a significant indirect influence on avoidance/hesitation behavior
through positive and negative anticipated emotions (β = 0.274, p < 0.01). The total impactof risk perception on avoidance/hesitation behavior (β = 0.828, p < 0.01) was the greatest
among study variables. Overall, these results indicate that risk perception (e.g., human
crowding risk, spatial crowding risk, quality risk, psychological risk, health and safety risk,
financial risk) has a positive relationship with anticipated emotions (positive and negative
anticipated emotion) and avoidance/hesitation behavior.

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Table 2. Structural equation modeling results and hypothesis testing (n = 429).
Hypothesized Paths
H1
H2
H3
H4
H5

Standardized
Estimates

Int. J. Environ. Res. Public Health 2021, 18, x
Risk perception→
Positive anticipated emotion−0.614 **Risk perception→
Negative anticipated emotion
0.635 **
Risk perception→
Avoidance/hesitation behavior
0.555 **
Positive anticipated→
Avoidance/hesitationPsychologicalbehavior−0.237 **risk
emotion Risk perception
Negative anticipated
Risk perception
Healthand safety risk 0.202 **→Avoidance/hesitation
behavior
emotion
Risk
perception
Financial
risk
Risk perception→
Human crowding risk
0.419 **
Risk perception→
Spatialcrowdingrisk
Total Variance Explained:
Indirect Impact on Retention: 0.457 **Riskperception→
Quality risk
0.405 **
2 for avoidance/hesitation behavior =RRisk
perception→
Psychological risk
0.961 **
Risk perception
Health and safety risk
0.987 **
0.745 →→
Financial risk
0.531 **
2 Risk perception

R for positive anticipated emotion = 0.378

Total
Variance
Explained:
Indirect Impact on Retention:
R2 fornegative
anticipated emotion = 0.403

t-Values−7.430
7.327
7.253

8o

−5.659
**
8.451
0.961
0.987
**
8.463
4.494
0.531 **
6.861
6.198
Total Impact on RI:
6.145
8.451
8.463
6.861

Total Impact on RI:

β risk perception = 0.828 **R2 for avoidance/hesitation
behavior= 0.745 risk = 0.176
R2 for human
crowding
β risk perceptionpositive & negative anticipated emoβ positive anticipated emotion = −0.237 **R2 for positive anticipated emotion = 0.378
2 for spatial crowding risk = 0.209
2
R
tionsavoidance/hesitation behavior = 0.274 **R for negative anticipated emotion = 0.403
β negative anticipated emotion = 0.202 **βrisk perception = 0.828 **R2 for human crowding= 0.176
R2 for risk
qualityrisk = 0.164 βrisk perception→positive & negative anticipated emotions2
βpositive anticipated emotion = −0.237 **R for spatial crowding
risk = 0.209
R2 for psychological risk = 0.924 →avoidance/hesitation behavior = 0.274 **βnegative anticipated emotion = 0.202 **R2 for quality risk = 0.164
R2 for healthsafety risk = 0.975
R2 for psychologicalrisk =and
0.924
R2 for health andRsafety
risk = 0.975risk = 0.282
2 for financial
R2 for financial risk = 0.282

Note1. Goodness-of-fit statistics for the structural model (higher-order framework): χ2 = 1079.063, df = 337, p < 0.001, χ2/df

Note1. Goodness-of-fit statistics for the structural model (higher-order framework): χ2 = 1079.063, df = 337, p < 0.001, χ2 /df = 3.202,= 3.202, RMSEA = 0.072, CFI = 0.946, IFI = 0.946, TLI = 0.939. Note2. **p < 0.01.
RMSEA = 0.072, CFI = 0.946, IFI = 0.946, TLI = 0.939. Note2. ** p < 0.01.

Figure2. Structuralmodel modeland invariancemodelGoodness-of-fit
results. Note1.statistics
Goodness-of-fit
statistics modelfor the(higherFigure 2. Structuralmodel
and invarianceresults. Note1.
for the structural
2
2
2
2
model
(higher-order= 1079.063,= 337,CFI
p < =0.001,/df==0.946,
3.202,TLI
RMSEA= Note2.
= 1079.063,
df = 337,
p < 0.001, modelχ /df = χ
3.202,RMSEA df
= 0.072,
0.946,χ IFI= 0.939.
order model χstructural
2/df = 2.262, RMSEA = 0.054, CFI = 0.925,
1567.221,
df =Goodness-of-fit
693, p < 0.001, χstatistics
Goodness-of-fit0.072,
statistics
the baselinemodel:
CFI for= 0.946,
IFI = 0.946,
TLIχ=2 =0.939.
Note2.
for the baseline model:
IFI = 0.925, TLIχ=2 0.918.
Note3.df* =p <693,0.05,
p < 0.01.
Note4.
TwoRMSEA
identical
were
proposedfor= high= 1567.221,
p <**0.001,
χ2 /df= 2.262,= models
0.054, CFI= 0.925,
IFI = (models
0.925, TLI
0.918.and lowgroups of destination
attachment).
Note3. * p < 0.05, ** p < 0.01. Note4. Two identical models were proposed (models for high and lowgroups of destination attachment).

The hypothesized influence of risk perception on anticipated emotions and avo
ance/hesitation behavior was tested. Our findings showed that risk perception exerte
significant influence on positive anticipated emotion (β = −0.614, p < 0.01) and negat
anticipated emotion (β = 0.635, p < 0.01). Additionally, risk perception affected avo
ance/hesitation behavior significantly (β = 0.555, p < 0.01). Hypotheses 1, 2, and 3 w
hence supported. The proposed effect of anticipated emotions was assessed. Results
vealed that both274
positive anticipated emotion (β = −0.237, p < 0.01) and negative ant
pated emotion (β = 0.202, p < 0.01) had a significant impact on avoidance/hesitation
havior. Therefore, Hypotheses 4 and 5 were supported. About 37.8% and 40.3% of

Int. J. Environ. Res. Public Health 2021, 18, 12345

4.3. Baseline Model and Test for Metric Invariance
A baseline model was created to evaluate the hypothesized moderating influenceof destination attachment. A total of 429 responses gathered through the survey weredivided into high destination attachment group (n = 198) and low destination attachmentgroup (n = 231). We utilized a k-means cluster analysis for the grouping process. Findings
showed that this baseline model contained a satisfactory model fit (χ2 = 1567.221, df = 693,
p < 0.001, χ2 /df = 2.262, RMSEA = 0.054, CFI = 0.925, IFI = 0.925, TLI = 0.918). The baselinemodel results are shown in Table 3.
The generated baseline model was compared to the nested models, where a particularlink of interest was constrained equivalently across high and low destination attachmentgroups. The results of a Chi-square test indicated that the linkage from risk perception
to positive anticipated emotion significantly differed across groups (∆χ2 (1) = 3.888, p< 0.05). Therefore, Hypothesis 6a was supported. Yet findings revealed that the paths
from risk perception to negative anticipated emotion (∆χ2 (1) = 2.712, p > 0.05) and to
avoidance/hesitation behavior (∆χ2 (1) = 0.772, p > 0.05) were not significantly dissimilar
between destination attachment groups. Accordingly, Hypotheses 6b and 6c were notsupported. In summary, these results indicate that destination attachment has positive
moderating effect on risk perception and avoidance/hesitation behavior. Conversely, thedestination attachment was not a moderating effect on risk perception and anticipatedemotions (positive and negative anticipated emotion).
Table 3. Baseline and invariance model assessment results.

Paths

High Group of
Destination
Attachment (n = 198)

Low Group of
Destination
Attachment (n = 231)

Baseline Model(Freely Estimated)

Nested Model(Constrained to
Be Equal)βt-Valuesβt-Values

Risk perception →Positive
anticipated emotion−0.297 **−3.014−0.481 **−3.729

χ2 (693) = 1567.221

χ2 (694) = 1571.109 a

Risk perception →Negative
anticipated emotion

0.416 **

3.536

0.588 **3.883

χ2 (693) = 1567.221

χ2 (694) = 1569.933 b

Risk perception →avoidance/
hesitation behavior

0.610 **

4.186

0.477 **3.741

χ2 (693) = 1567.221

χ2 (694) = 1567.993 c

Chi-square Difference Test:a ∆χ2 (1) = 3.888, p < 0.05 (H6a—supported)b ∆χ2 (1) = 2.712, p > 0.05 (H6b—not supported)c ∆χ2 (1) = 0.772, p > 0.05 (H6c—not supported)

Note1. Goodness-of-fit statistics for the baseline model: χ2 = 1567.221, df = 693, p < 0.001, χ2 /df = 2.262, RMSEA = 0.054, CFI = 0.925,
IFI = 0.925, TLI = 0.918; Note2. ** p < 0.01.

5. Discussion
This study provides a meaningful theorization related to tourist international behaviors
in the with-corona era. The present research is one of few studies that have built a theoreticalframework by risk perception factors as core variables and considered their influence oninternational travelers’ avoidance behavior through anticipated emotions. This research
aimed to integrate the moderating impact of overseas travelers’ destination attachmentwith the formation of their anticipated emotional responses and hesitation behaviors. In
sum, the findings of the present research help us to comprehend more explicitly the roleof risk perception, which increases international travelers’ negative anticipated emotion,
decreases their positive anticipated emotion, and ultimately enhances their avoidance
actions regarding travel to China. In addition, our findings help us to clearly understand the275

Int. J. Environ. Res. Public Health 2021, 18, 12345

criticality of attachment and how it moderates the risk perception and positive anticipatedemotion relationship. Given that international tourism toward China can be one of themost critical issues in the global tourism industry in the with-corona era, the findings of thepresent research are of utmost importance in helping international tourism practitioners andresearchers in Korea enhance their knowledge about outbound travelers’ decision-making
processes and behaviors in relation to China in the with-corona era.
It is indisputable that there exists a significant relation between disease outbreak
and tourist behaviors. Nevertheless, little research has yet uncovered the possible effectof perceived risk related to COVID-19 on Korean travelers’ destination choices in theoutbound tourism sector. Filling this void, this is the first empirical study that identifies
the possible risks of traveling to China after the outbreak of COVID-19 and explores
the influence of various risk factors on Korean travelers’ avoidance behaviors/responses
toward traveling to China. Our result therefore enriches the literature on international
tourism and can be used as a basic framework for future research about disease outbreak
and tourist behavior.
Al-Ansi [13] determined the risk perception dimension of health risk, psychologicalrisk, environmental risk, social risk, quality risk, financial risk, and time-loss risk and foundthat, except for time-loss risk, risk perception significantly influenced tourist behavioral
intention. In the current research, a particular meaningful point is the second-order
framework of risk perception. It was apparent that the six first-order dimensions (human
crowding risk, spatial crowding risk, quality risk, psychological risk, health and safety
risk, and financial risk) belong to the global latent factor of risk perception. In other words,
the commonality underlying the six dimensions was fully extracted by its second-ordervariable. This empirical result enriches the extant literature by providing a higher-orderapproach, which clearly captures risk perception pertinent to international tourism inChina. Moreover, the parsimonious framework of this hierarchical structure enlightens
academics and destination marketers regarding the effectiveness of theorizing intricate risk
perception factors more succinctly in the with-corona era of the international tourism sector.
For sector can ensure social distancing in tourism destinations to avoid human crowding
and spatial crowding risk. Zhang et al. [45] suggested that destination managers should
weigh up destination measures and tourist health risk perception, and that destination antiepidemic measures positively influence destination image. Combined with this research,
tourism departments need to improve this destination management measure.
In a previous study on the relationship between health risk and tourist protective
behavior, worry played a crucial role in health risk perceptions [17,46]. A prior study
confirmed the relationship between financial and quality risk and tourist behavioral intention [11,16]. In this research health and safety risk were two prominent factors of risk
perception. Therefore, for tourism practitioners in China, improving unhealthy, unsafe,and uncomfortable tourism environments can be crucial to minimize risk perception of
Korean international travelers regarding China.
Financial risk and quality risk were also uncovered as vital dimensions of risk perception.
In addition, human crowding risk and spatial crowing risk were unearthed as significant
dimensions of risk perception. Hence, lowering financial and quality risks in tourist sites in
China, as well as minimizing human and spatial crowding risks, are critical. The facilitatorsand inhibitors of tourist avoidance behaviors relating to international tourism are weakly
researched and understood in the with-corona era. Employing an empirical approach, thisresearch successfully provides evidence that risk perception and its constituents are fundamental sources for helping tourism academics/practitioners in Korea and China in understanding
outbound and inbound travelers’ responses/behaviors, respectively.
According to prior research, tourists’ destination attachment strongly influences destination loyalty and positive emotional response [32,34,35]. The findings of our research frommetric-invariance assessment evidenced that destination attachment significantly moderates the relation between risk perception and positive anticipated emotion. Particularly, therisk perception→positive emotion relationship was weaker in the high group of destination

276

Int. J. Environ. Res. Public Health 2021, 18, 12345

attachment (βrisk perception → positive anticipated emotion = −0.297, p < 0.01) as compared to thelow group (βrisk perception → positive anticipated emotion = −0.481, p > 0.05). Our result indicatesthat Korean overseas travelers’ risk perception is less likely to reduce their positive anticipated emotion when they feel a strong attachment to China as an international tourism
destination. Sohn and Yoon [36] reported that destination attachment has a moderate effecton risk perception and destination image. The result of this research evidence provides theoretically important information that the risk perception and positive anticipated emotion
link significantly depends on the level of destination attachment. The use of this moderator
concept can hence be hence for a better grasp of understanding Korean tourists’ avoidance
responses when considering China for international tourism. Practically, our result offers
critical insights as it reports that strong bonding between a destination and its visitors
reduces the detrimental influence of risk perception on otherwise positive anticipatedemotional responses.
6. Conclusions
Our investigation of the indirect influence of research constructs evidenced that positive and negative anticipated emotions are crucial mediators within the hypothesizedconceptual framework. Two main influential factors mediated the impact of their proximal
antecedent to their outcome construct. The result indicates that for a clear understanding of the role of risk perception in overseas travelers’ behaviors, dealing with positive
and negative anticipated emotions is a fundamental requisite. Based on this finding,tourism practitioners need to boost anticipated positive emotion and lower anticipatednegative emotion pertinent to international traveling as this effort minimizes the impactof the predictor on international-tourism avoidance behavior. This research contains a
few limitations. First, the present study includes an issue of high correlation. Although
the between-construct correlations in this study are not at the problematic level, somecorrelations are still high. Future research should minimize this issue by effective design ofthe measurement framework. Second, this research centers on the role of risk perception ineliciting emotional and behavioral responses among international travelers. Yet, according
to recent destination studies in tourism, many crucial factors influence such affective andbehavioral processes (e.g., satisfaction, image, destination performance, attributes, trust,value, travel experience) [1,47–49]. Future research should expand the proposed theoretical framework to better account for the total variance of avoidance/hesitation behavior.
Such effort would enhance the comprehensiveness of the conceptual framework and its
prediction power.
Author Contributions: Conceptualization, H.H.; methodology, H.H.; Writing—original draft preparation, H.H.; writing—review and editing, C.C. and S.L.; visualization, C.C.; supervision, S.L.; project
administration, S.L.; funding acquisition, H.H. and S.L. All authors have read and agreed to the
published version of the manuscript.
Funding: This work was supported by the Ministry of Education of the Republic of Korea and the
National Research Foundation of Korea (NRF-2020S1A5A2A01046684).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The dataset used in this research are available upon request from thecorresponding author. The data are not publicly available due to restrictions, i.e., privacy or ethics.
Conflicts of Interest: The authors declare no conflict of interest.

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