# Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A
Review

Md. Mokhlesur Rahman, Kamal Chandra Paul, , Md. Amjad Hossain, G. G. Md. Nawaz
Ali, , Md. Shahinoor Rahman, and Jean-Claude Thill

###### Abstract

The ongoing COVID-19 global pandemic is affecting every facet of human lives
(e.g., public health, education, economy, transportation, and the
environment). This novel pandemic and citywide implemented lockdown measures
are affecting virus transmission, people’s travel patterns, and air quality.
Many studies have been conducted to predict the COVID-19 diffusion, assess the
impacts of the pandemic on human mobility and air quality, and assess the
impacts of lockdown measures on viral spread with a range of Machine Learning
(ML) techniques. This review study aims to analyze results from past research
to understand the interactions among the COVID-19 pandemic, lockdown measures,
human mobility, and air quality. The critical review of prior studies
indicates that urban form, people’s socioeconomic and physical conditions,
social cohesion, and social distancing measures significantly affect human
mobility and COVID-19 transmission. During the COVID-19 pandemic, many people
are inclined to use private transportation for necessary travel purposes to
mitigate coronavirus-related health problems. This review study also noticed
that COVID-19 related lockdown measures significantly improve air quality by
reducing the concentration of air pollutants, which in turn improves the
COVID-19 situation by reducing respiratory-related sickness and deaths of the
people. It is argued that ML is a powerful, effective, and robust analytic
paradigm to handle complex and wicked problems such as a global pandemic. This
study also discusses policy implications, which will be helpful for policy
makers to take prompt actions to moderate the severity of the pandemic and
improve urban environments by adopting data-driven analytic methods.

###### Index Terms:

COVID-19, Coronavirus, Pandemic, Machine Learning, Public Health, Human
Mobility, Air Quality, Review

##  I Introduction

The Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic and
public health crisis that was first reported in Wuhan, China, in December 2019
[1, 2, 3, 4, 5]. Human movement and interactions are significantly affected by
COVID-19 pandemic [6, 7, 8]. Besides affecting healthful living, this highly
infectious disease is influencing every domain of human lives (e.g., mental
health, social life, education, economy, global supply chains, production,
mobility, energy consumption, environment, and so on) [2, 9, 10, 11, 12, 13,
14, 15]. This study aims to summarize the results from selected studies
conducted recently using Machine Learning (ML) techniques to portray the
interplay between the COVID-19 pandemic, human mobility, and air quality.
Moreover, this study will be helpful for policymakers to take immediate
actions to mitigate the severity of the pandemic and improve well-being in
urban environments.

As of January 20, 2021, more than 97 million people from 218 countries have
been infected and 2.08 million people have died; this amounted to critical
conditions in 0.4% of active cases and a fatality rate of about 3% of closed
cases [16, 17]. Aggregating the total number of confirmed cases and deaths by
the World Health Organization (WHO), it is observed that countries in the
Americas constitute the most affected region in the world, with about 43.87%
of all confirmed cases and 47.61% of the deaths (Fig. 1). Europe is the second
most affected region with 32.18% of confirmed cases and 32.19% of deaths,
followed by South-East Asia with 14.31% of confirmed cases and 10.03% of
deaths. In contrast, the Western Pacific Region is the least affected with
1.34% of confirmed cases and 1.11% of deaths worldwide.

Figure 1: COVID-19 confirmed cases and deaths (%) by WHO regions (January 6,
2021).

Controlling the outbreak of COVID-19 has become a major concern of governments
and public health professionals. Different measures such as stringent travel
restrictions and self-quarantine measures (e.g., lockdown, emergency stay-at-
home orders, closing of public transportation Systems, travel ban,
restrictions on public gathering, closure of workplaces and educational
institutions) and personal protective measures (e.g., face masks, gloves) have
been implemented [2, 10, 11, 12, 18, 19, 20] throughout the world to control
the COVID-19 outbreak. These various measures undertaken by governments have
influenced both essential (e.g., work) and non-essential (e.g., recreation)
trips. For example, lockdown measures undertaken in Singapore to mitigate the
COVID-19 pandemic caused a 30% reduction in mobility [21]. A 64.6% reduction
in private vehicle trips was observed in Rome, Italy, during lockdown periods
of March-April 2020 [22]. Similarly, about 80%, 23%, and 2% reduction in
public transport, cycling and bike-sharing, respectively, were observed in
Budapest, Hungary [23]. In contrast, a 43 to 65% increase in car travel was
reported in Budapest, Hungary, due to the voluntary practice of social
distancing and avoidance of public transportation. Air transportation has also
been devastated by this COVID-19 situation. Researchers have reported that
Canadian Civil Aviation and military aviation activities dropped by 71% and
27%, respectively, compared to the business as usual situation [13]. Thus, the
widespread diffusion of COVID-19 is adversely affecting all modes of
transportation.

Strict confinement measures have seriously affected mobility in public places
and spaces. Fig. 2 shows these effects in selected countries (US, Brazil,
India, and New Zealand) during the period from February 17 to November 27 in
2020. The figure shows a reduction in the number of visitors (%) in retail and
recreation outlets, groceries and pharmacies, parks, transit stations, and
workplaces compared to the baseline scenario (i.e., the median value of the
day for the 5‑week period starting from January 03 to February 06, 2020), with
a deeper reduction in March and April. In contrast, the number of visitors has
increased in residential areas due to implemented social distancing measures
(i.e., stay-at-home order), which exemplifies the adverse impacts of the
pandemic on travel patterns of the people.

Figure 2: Longitudinal changes in mobility in selected countries.

The COVID-19 pandemic also has had substantial impacts on air quality. As a
primary method of slowing down the spread of COVID-19, initially, a lot of
countries imposed lockdown or confinement measures to enforce strict social
distancing regulations. As a result, businesses and shops were closed,
manufacturing activities were either stopped or shrunken while the number of
vehicles in cities has declined dramatically [24, 25, 26]. Therefore, lockdown
and confinement played a critical role in curtailing emissions and in
improving overall air quality. Improved air quality refers to the reduction of
concentration of criteria pollutants such as $NO_{2}$, ${SO_{2}}$,
${PM_{10}}$, ${PM_{2.5}}$, ${CO_{2}}$ in the air. According to the
International Energy Agency (IEA), the global energy demand decreased by 3.8%
in the first quarter of 2020 compared to the same period of 2019 because of
the sudden reduction in economic activities and mobility[24]. Many recent
studies on urban air quality have estimated the impacts of lockdown and
confinement measures on various criteria pollutants. These studies mainly
estimate the business as usual concentration of the pollutants for 2020 based
on climate variables using ML algorithms. Finally, the impacts of the lockdown
and confinement on air quality were assessed by comparing the estimated
baseline concentration with actual concentration of pollutants in 2020. Some
studies explored the relationship between pre-existing air pollution and
COVID-19 mortality rates in different sections of cities.

Considering the magnitude of these effects, this study has been conceptualized
as a survey of previous research on the impacts of the COVID-19 pandemic on
urban mobility and air quality and their associations. However, this study
focuses particularly on past studies that used various machine and deep
learning approaches to evaluate the relationships among these concepts and
conditions. With the advent of novel data technologies, methods of ML have
been used extensively in disease prediction [27, 28, 29], transportation
modeling [30, 31], economic analysis [32, 33], environmental modeling [34,
35], public sentiment analysis [36, 37], etc. due to their remarkable
computational ability to extract meaningful relationships between input and
output features from large and complex datasets that are semantically diverse
and that exhibit heterogeneous spatial and temporal granularity [38]. The
problem at hand exhibits multiscalarity (from the individual, to the family,
the community, the region, the nation, and eventually the global humanity),
and other properties such as endogeneity, nonlinearity, non-independence,
ambiguity and contextuality, that legitimately make them so-called ”wicked
problems” [39, 40]. ML has proved very effective to tackle such problems.
Learning-based algorithms can retrieve meaningful features from a large volume
of data to predict outcome accurately and able to reveal the hidden patterns
in the data set that were previously unknown [41, 42, 43]. At variance with
traditional data processing systems, ML algorithms build models based on
existing data with little or no distributional requirements for future
predictions or decision making, which increases their performance tremendously
[44].

In this regard, many studies have used ML and deep learning along with the
SEIR (susceptible-exposed-infectious-recovered) model to predict COVID-19
transmission rates and evaluate its impacts on public health, urban mobility
and the environment. Thus, considering the model prediction accuracy and the
inherent power to explore big data, this study purports to review the
literature that only used ML and deep learning based approaches. The main
objective of this study is pursued by investigating the following research
questions:

  * •

What are the impacts of the COVID-19 pandemic on mobility patterns of urban
populations?

  * •

What are the impacts of the COVID-19 pandemic on urban air quality?

  * •

How do the different aspects of COVID-19 pandemic, human mobility, and air
quality interact with each other?

The salient contributions of this paper are five-fold:

  * •

Understanding and quantifying the impacts of the COVID-19 pandemic on human
mobility and air quality by reviewing past literature;

  * •

Identifying data sources and ML approaches that have been used in the previous
studies and could be used by these researchers to estimate the impacts of
COVID-19 on mobility reduction and on improving air quality in urban and rural
areas;

  * •

Developing a conceptual framework to clearly articulate the complex
relationships among COVID-19 reported cases (and deaths), lockdown and
confinement measures, human mobility patterns, and factors of air quality;

  * •

Identifying policy options that could be used by decision makers and
researchers to reduce the severity of the pandemic, facilitate human mobility,
and improve air quality;

  * •

Identifying potential research options that could be adopted by the
researchers in their future work.

##  II Conceptual framework

Based on existing theories and concepts, a conceptual framework has been
developed in this review study, Fig. 3. The figure depicts that the urban form
and structure, socioeconomic features of the people, health factors, social
networks, civic engagement, and different lockdown and confinement measures
significantly influence the COVID-19 pandemic. For example, high population
density and public places are positively associated with COVID-19 because of
increased interaction among the people. Elderly people and people with certain
health conditions are more vulnerable to COVID-19. Similarly, limited access
to health resources (e.g., hospitals, clinics, physicians) increases the risks
of the pandemic. On the other hand, strong agency and institutional compliance
(i.e., limited social engagement) and lockdown and confinement measures
significantly reduce community transmission of COVID-19. These factors also
influence human mobility patterns, travel mode choice behaviors, and travel
purposes. For example, usually, the people in the high-density areas are more
likely to use public transportation, active transportation (e.g., walk,
cycle), and less likely to use private cars. But during the COVID-19
situation, the elderly, children, and disabled people are less likely to use
public transport and more likely to use private cars to reduce infection risk.
The lockdown and confinement measures significantly reduce human mobility in
both essential (e.g., work, healthcare facilities) and non-essential (e.g.
parks, fitness centers) trips. People adjust their travel schedules and change
travel patterns to avoid infection risks. Thus, there is a bidirectional
relationship between human mobility and the diffusion of the virus. The
severity of the COVID-19 pandemic also influences governments and decision
makers to impose lockdown measures to reduce the infection rate or slow the
pace of growth (the so-called ”flattening of the curve”).

Air quality significantly depends on urban form and structure. Industrial
development, urbanization, and higher use of private transportation degrade
air quality in urban areas. However, air quality has significantly improved
during the COVID-19 lockdown periods thanks to reduced human mobility and the
closure of many workplaces and industries. Improved air quality, in turn,
lessens COVID-19 related deaths, particularly among people with chronic
respiratory illnesses. Thus, similar to human mobility, COVID-19 has a
bidirectional association with air quality.

Figure 3: Conceptual framework of the study.

##  III Human mobility patterns

This section discusses the interrelationships among the COVID-19 transmission,
confinement measures, socioeconomic factors, human mobility and travel
patterns of the people, etc., based on prior studies that used ML techniques.
A summary of this literature is provided in Table I.

TABLE I: Geographical context and objectives of past studies of COVID-19 and human mobility. Authors |  Study context |  Objective
---|---|---
[7] |  Los Angeles, US |  Investigating the relationships among socio-economic features of people and human mobility during COVID-19.
[45] |  13 countries of the world |  Evaluating the effectiveness of lockdown measures on the COVID-19 pandemic.
[46] |  New York and Seattle, US |  Investigating the impacts of post-COVID-19 reopening strategies on travel patterns and mode choice of people.
[47] |  Cracow, Poland |  Investigating changes in pedestrian activities in public places (e.g., tourist spots, residential areas, and places with mixed land uses) before and during COVID-19.
[48] |  50 states of the US |  Assessing the impacts of policy instruments (e.g., closing and reopening of retail stores, workplaces, businesses, places of entertainment and worship, and restriction on mobility) on the COVID-19 pandemic.
[49] |  US, Italy, Spain, Germany, France, and South Korea |  Understand the impacts of social distancing measures (i.e., mobility) on the transmission of COVID-19.
[50] |  401 counties in Germany |  Exploring the spatial (e.g., population density) and aspatial (e.g., socio-economic) factors of coronavirus diffusion
[51] |  US and Australia |  Forecasting the effects of the COVID-19 pandemic on the tourists’ arrivals.
[52] |  50 states of the US and District of Columbia |  Investigating the factors that affect human mobility and travel during the COVID-19 pandemic.
[53] |  26 countries of the world |  Examining the role of social distancing measures on the COVID-19 transmission rate.
[54] |  Iran |  Predicting coronavirus cases and identifying the associated factors that influence new daily cases.
[55] |  Boston, US |  Estimating the changes in people’s mobility due to COVID-19 situations and related local policy measures.
[56] |  Hayatabad, Pakistan |  Detecting the violation of social distancing measures.
[57] |  US |  Modeling COVID-19 transmission at the county level.
[58] |  Detroit, US |  Investigating the impacts of COVID-19 and social distancing measures on traffic volume and safety.
[59] |  Dane and Milwaukee County, City of Madison, US |  Modeling of COVID-19 spread and investigating the associations between COVID-19 transmission and mobility, business foot-traffic, and socioeconomic features.
[60] |  Wuhan, China |  Predicting the number of COVID-19 infection cases related to patient recovery and death.
[61] |  3219 counties in the US |  Developing a COVID-19 case prediction model with ML techniques based on county-level data.
[62] |  US |  Developing an interactive platform to analyze COVID-19 impact.
[63] |  China |  Predicting COVID-19 cases on the next day.
[64] |  Iran |  Investigating the impacts of air and inter-city travel on COVID-19 confirmed cases.

###  III-A Impacts of the COVID-19 pandemic on mobility and travel patterns

It is observed that COVID-19 significantly influences people’s travel habits
for both essential and non-essential trips. The McKinsey Center for Future
Mobility (MCFM) [65] studies the key factors impacting travel mode choice
behaviors of people in China, France, Germany, Italy, Japan, the UK, and the
US before and during the COVID-19 pandemic, Fig. 4. Travel time, cost, and
convenience played a significant role in people’s travel mode choices for
business, commuting, and personal trips before COVID-19 and similarly
disruptive conditions. However, during this COVID-19 pandemic, reducing the
risk of infection has become people’s primary factor in deciding on their
travel mode. Thus, the use of personal cars, cycling, walking, and shared
micro-mobility is outpacing the use of various forms of public transportation.

Figure 4: Key reasons to choose transportation modes before and during
COVID-19 pandemic [65].

Wang et al. [46] investigated the impacts of post-COVID-19 reopening
strategies on travel patterns and mode choices of the people in New York and
Seattle, US. Upon collecting data, these authors observed a reduction in
subway ridership in May 2020 compared to the 2019 level. An agent-based
simulation demonstrates that full reopening could only expect about 73% of the
pre-pandemic transit ridership, but on the flip side, an increase in private
car ridership by 142%. Similarly, the number of walking trips and bike trips
would increase by 101% and 104% of the pre-pandemic time, respectively.
Applying a capacity restriction on public transit to 50% for practicing social
distancing, the study observed a decrease in transit ridership to 64%, whereas
car trips would increase by 143% and bike trips by 123%. Using a deep-learning
based real-time video processing method of feature recognition, they found an
increasing number of pedestrians at multiple locations in New York in May.
They also observed a reduction in pedestrians who are practicing 6-feet social
distancing guidelines from 91% on April 2 to 86% on May 27. Although
pedestrian density was found low at some other locations, car and pedestrian
density began to increase during the peak hour periods due to travel of
essential workers, but the density of bicyclists was similar to the pre-
pandemic situation. In a similar study context (i.e., Detroit, US),
researchers in [58] investigated the impacts of COVID-19 pandemic and social
distancing measures on traffic volume on the transportation network and on
safety. Collecting data from 73 signalized intersections, they mentioned that,
aside from the continued dominance of car trips, the number of bicycle and
motorcycle trips has mushroomed four-fold during the COVID-19 pandemic. The
number of trucks and vans remains unchanged before and during the confinement
and lockdown periods. However, a 40% increase in the number of trucks and vans
was observed when restrictions were lifted. Thus, the COVID-19 pandemic,
reopening strategies, and confinement and lockdown measures have severe
impacts on personal mobility and on the state of the urban transportation
system more generally.

In a recent survey, MCFM [66] discovered that about 70% of respondents would
choose walking or cycling at least once per week, even after returning to
normal life, which is more than 6% higher than the pre-crisis situation.
Similarly, private car-sharing would increase from 78% to 79% in the post-
pandemic scenario. However, despite a dramatic drop during the pandemic,
transit ridership would return to its pre-existing conditions at around 40% on
average across all surveyed countries. Similarly, shared micro-mobility and
car-sharing would be slightly more popular (i.e., 1 to 2%) after returning to
normal life. Thus, while travel mode choice behaviors of the people are found
to be significantly affected by the COVID-19 pandemic, people’s overall travel
patterns would be expected to bounce back to their state before the pandemic.

COVID-19 has also significantly affected the social and economic aspects of
tourism and associated industries (e.g., air transportation) in major tourist
destinations around the world. Polyzos et al. [51] forecasted the effects of
the COVID-19 pandemic on Chinese tourist arrivals in the US and Australia.
Using data from the 2003 SARS outbreak, they trained a deep-learning-based
Long Short-Term Memory (LSTM) neural network model to predict the social and
economic impacts of the pandemic on the tourism industry. The trained model is
calibrated considering the particulars of current situations (e.g., lockdown,
flight bans) to simulate the impacts of a COVID-19-like pandemic on the
tourism industry. The results indicate a significant drop in tourist arrivals
from China to the US and Australia. Upon cross-validating the findings based
on backtesting techniques (i.e., the sample is split into smaller training
sets and error is used to train the model), the researchers commented that it
would require 6 and 12 months for tourist arrival rates to Australia and the
US, respectively, to recover to their pre-pandemic levels after the recent
collapse of the industry. They also mentioned that the LSTM technique performs
better than other artificial neural networks (e.g., hidden Markov, Support
Vector Regression) and forecasting models, such as ARIMA (Auto-Regressive
Integrated Moving Average), to predict the impacts of COVID-19.

The changes in pedestrian activities in public places (e.g., tourist
attractions, residential areas) because of COVID-19 have been investigated in
[47] for Cracow, Poland, using YOLO (You Only Look Once) - an ML algorithm
that allows easy and less erroneous end-to-end object detection. After
collecting data from webcams located at public sites covering the period of
June 9, 2016, to April 19, 2020, the images are first split into smaller
tiles, which increases pedestrian detection capacity by more than 50%.
Estimating hourly, daily and weekly averages of pedestrian activities, the
study observed a 34-50% reduction in pedestrians in residential zones and a
78-85% reduction in tourist localities due to lockdown and confinement
measures during the Covid-19 pandemic. The study claimed that the proposed
method is more efficient to detect and count pedestrians from time-lapse
webcam images than other approaches such as Single Shot MultiBox Detector
(SSD), existing YOLO and Faster Region-based Convolutional Neural Network
(R-CNN), in terms of mean absolute error (i.e., 4.28 vs 9.87, 5.48, and 5.38,
respectively) and root mean square error (i.e., 7.96 vs 14.32, 10.23, and
9.16, respectively). However, it has a longer processing time (3.28s) compared
to existing YOLO (0.75s) and SSD (1.46s), although a lower processing time
than Faster R-CNN (8.35s).

Changes in human mobility and activity patterns can also be evidenced through
the impact the pandemic would have in other economic sectors such as energy
consumption. The pandemic oil demand analysis (PODA) is a ML technique
proposed in [67] to project the US gasoline demand during the COVID-19
pandemic. It consists of two projection modules. The first, dubbed the
Mobility Dynamic Index Forecast Module, identifies the changes in travel
mobility caused by the evolution of the COVID-19 pandemic. The second, the
Motor Gasoline Demand Estimation Module, estimates vehicle miles traveled on
pandemic days, while considering the dynamic indices of travel mobility, and
quantifies motor gasoline demand by coupling the gasoline demands and vehicle
miles traveled. The prediction model used Apple and Google mobility data and
showed a significant reduction in US gasoline demand in March and early April
of 2020. Another study [68] proposed to use mobility data as a complementary
component in a day-ahead electrical load forecast model based on a multi-task
neural network and showed that the load forecasting accuracy can be improved
significantly. This study also found that the sudden changes in electric
consumption due to COVID-19 pandemic cause higher forecasting error.

The above discussion clearly demonstrates that COVID-19 has significant
impacts on urban transportation systems by influencing people’s travel and
mode choice behaviors and their energy consumption. During this pandemic,
people adjust not only the sheer volume of their travel, but also their travel
schedule, route, and modes to reduce potential health risks to themselves and
to others. Consequently, it is also indirectly affecting other sectors of the
economy (e.g., office businesses, manufacturing, retail and services).
Overall, this pandemic is affecting human lives and the economic system of
society very broadly and very deeply.

###  III-B COVID-19 prediction models to understand the factors affecting
virus diffusion

Many researchers have used Artificial Intelligence (AI) to predict coronavirus
infection rate, recovery, and death rate with good accuracy throughout the
world. These researchers used compartmental models (such as the Susceptible-
Exposed-Infectious-Recovered (SEIR) model), ML based models, and hybrid models
to predict the pandemic itself. For example, the YYG model [69] uses a simple
ML technique to estimate the number of COVID-19 infection cases and deaths in
a state/country. This projection model adopted the SEIR model as the
underlying simulator for generating simulated infections under specific
scenarios and the simple brute-force grid search technique as the ML model for
tuning the model parameters. The model used two types of parameters: fixed
parameters (e.g., latency period, infectious period, time to recovery) and
dynamic parameters (e.g., reproduction rate, mortality rate, mitigation
effects).

Similarly, many studies have investigated the factors that affect the COVID-19
transmission rate in cities and regions on the basis of an assumed COVID-19
transmission model. For example, Yao et al. [58] evaluated the role of
transportation (e.g., traffic volume, accident), social distancing measures,
and weather conditions in the incidence of coronavirus confirmed cases.
Calculating correlation coefficients, they found that daily confirmed cases
are highly correlated with the transportation volume (e.g., cars), total
crashes, social distancing indicators, and average temperature. Using the LSTM
approach, they estimated the number of confirmed cases per day. Model results
demonstrate that the inclusion of all selected features improved the
performance of the model to predict daily confirmed cases with lower RMSE
(0.0606), mean absolute error (MAE) (0.0378), and a high $R^{2}$ (0.9088).
Thus, besides daily confirmed cases and the social distancing indicator in the
previous days, the inclusion of traffic volume, crashes, and weather
conditions significantly improves the predictive performance of the model.
Considering daily inter-state traffic and air traffic data, including the
number of transfer passengers, Shirvani et al. [64] predicted new confirmed
cases in Iran. Using a supervised ML model consisting of an ensemble of linear
regression, LASSO regression, K-Nearest Neighbor (KNN) regression, random
forests, the model predicted the number of new cases with an accuracy of 85%.
The study found a positive correlation between inter-state travel and new
confirmed cases. Consequently, it suggested imposing travel restrictions to
limit COVID-19 transmission and slow the spread of the pandemic.

In the US, Kuo and Fu [61] developed a COVID-19 prediction model after
collecting demographic, environmental, and mobility data at the county level.
Data from 172 metropolitan counties were used to design a hybrid framework
based on eight different ML algorithms to predict daily and cumulative
confirmed cases. The final model was developed using a general linear model
(GLM) that combines the predictions from all ML algorithms. The study showed
that human mobility in the metropolitan areas reduced substantially after
implementing lockdown measures in mid-March. Scenario assessment results show
that a 1- week and 2-week lockdown in Phase I reopening could reduce
infections by 4-29% and 15-55% in the future week, respectively. However, a
2-week reopening in Phase II could increase infections by 16-80%. Thus, this
study suggested a mandatory lockdown order lasting more than one week to
control the COVID-19 pandemic by reducing community mobility and transmission.
Researchers in [70] investigated the non-linear relationships of COVID-19
death rates with environmental, health, socioeconomic, and demographic risk
factors using geographically weighted random forests (GW-RF) non-parametric
regression model in the US. Collecting county-level daily death counts in 3108
counties, the model showed that walking trips to work, concentration of air
pollutants, households with a mortgage, unemployment status, and percent of
black or African Americans have a strong correlation with the spatial
distribution of COVID-19 incidence with an $R^{2}$ of 0.78.

Another study in the US [59] developed a stochastic SEIR-style epidemic model
augmented by human mobility to predict historical growth trajectories of
COVID-19 cases in two counties (Dane and Milwaukee) in the state of Wisconsin.
The model was combined with the data assimilation (i.e., Kalman Filter) and ML
techniques (i.e., Walktrap network) to reconstruct the COVID-19 trajectories.
The Walktrap network partitions the counties into clusters based on the
observed human mobility data. A local SEIR model for each region was developed
using geographic, socioeconomic, cultural and transportation factors of the
people to get a region-specific effective reproduction number. Finally, the
combined model helps to investigate the associations of COVID-19 diffusion
with mobility patterns, business foot-traffic, race, and age groups. The study
found a strong association between reproduction number and visits to drinking
establishments (alcoholic beverages). It is suggested that policy makers
should explicitly consider the local transmission scenario even after
restricting intra-regional movement for preventing more health disparities in
future pandemics. In another North American country (i.e., Labrador and
Newfoundland in Canada), Kevln et al. [71] explored the impact of partial and
total reopening of airports on COVID-19 outbreaks with SEIR and Bayesian
Interference models. Using data on air-traffic mobility incoming and outgoing
from/to other Canadian provinces and the US with different measures of
quarantine, the study found that relaxing travel restrictions is possible
entirely (total reopening). However, strict (100%) quarantine conditions are
necessary to control the disease outbreak. Voluntary quarantine, even at an
overall rate of 95%, is not enough to entirely prevent future outbreaks. Thus,
strict policies on quarantine after entering the city are essential to control
the massive outbreak of the pandemic.

In China, Lu et al. [63] developed an ensemble-based back-propagation neural
network (BPNN) model for predicting COVID-19 cases on the next-day. Using
Baidu‘s migration data (e.g., migration index, internal travel flow index and,
confirmed cases from the 13 previous days), they trained the model for
predicting the coronavirus cases and achieved a 97% correlation with the
actual data. Multiplying Baidu’s mobility index values by two and considering
the incremented values due to mild government interventions on human mobility,
this study reported a significant increase in the COVID-19 cases and
continuous growth for a long time. Thus, mobility has significant impacts on
COVID-19 transmission. However, the researchers mentioned that the inclusion
of other social distancing measures could increase the accuracy of the
prediction. Another study in Wuhan, China [60], proposed a fine-tuned Random
Forest model boosted by the AdaBoost algorithm for predicting the coronavirus
cases and their possible outcomes (i.e., recovery, death). Collecting
information on COVID-19 patient’s geographic location, travel, health, and
demographic features, the study developed the prediction model using a grid
search algorithm which provides a set of best-performing parameters. The model
results indicate that the proposed fine-tuned model correctly predicts the
outcomes with an accuracy of 94% and an F1 score of 0.86. The study found
higher death rates among the Wuhan natives compared to non-natives. The study
also reported higher death rates of male patients than female patients and the
majority of the patients were from 20 to 70 years old.

In summary, ML is widely used in predicting COVID-19 confirmed cases, deaths,
and the recovery status of populations (Table II). These models use a variety
of transportation, demographic and socioeconomic, geographical, environmental,
health, and lockdown-related factors to predict confirmed cases and deaths.
The results from these models indicate that ML can efficiently predict the
COVID-19 pandemic with high rates of accuracy. Most of the studies suggested
putting an embargo on the movement of people to prevent a widespread pandemic.
Yet, some the evidence that has accumulated points in other directions. For
instance, with data on daily trips per person and COVID-19 confirmed cases in
different US states, [72] found a positive but weak association between the
number of trips per day and the incidence of COVID-19 confirmed cases, Fig. 5.
More analytic research that comprehensively controls for a full range of
factors and circumstances is needed to have a robust knowledge base in which
to ground policy making.

Figure 5: Impact of mobility on COVID-19 infection. TABLE II: Data and ML methods used in past studies on COVID-19 and human mobility. Author |  Data and sources |  ML methods
---|---|---
[7] |  Mobility to different points of interest (POI) such as restaurants, grocery stores, religious establishments, fitness centers, and supermarkets from SafeGraph, social vulnerability index (i.e., socioeconomic status, household composition and disability status, minority status and language proficiency, housing type and transportation) from CDC, unemployment rate from American Community Survey, population density from WorldPop, and $NO_{2}$ concentration from NOAA, Daily COVID-19 cases from LA Public Health Department |  Gradient Boosting Decision Tree (GBDT)
[45] |  Mobility patterns at retail and recreation, grocery stores and pharmacies, parks, transit stations, workplaces, residential areas, and schools from Google Mobility Report and UNESCO |  Random Forest (RF), K-Nearest Neighbor (KNN)
[46] |  Subway ridership and vehicular traffic data from the Metropolitan Transportation Authority (MTA), transit, driving, and walking trips from Apple Mobility Trends reports |  Agent-based simulation (MATSim-NYC), deep-learning-based real-time video processing method (e.g., RetinaNet, ResNet-50)
[47] |  Time-lapse webcam images from different urban zones (e.g., tourist areas, residential areas) for detecting pedestrian and their activities |  YOLO, Single Shot MultiBox Detector (SSD), and Faster R-CNN
[48] |  Information on policy instruments from the press release from states and mobility data from google mobility reports |  Extreme Gradient Boosting Decision Tree (XGBDT)
[49] |  Coronavirus case count from WHO, CDC, NY Times, and Texas DSHS, socioeconomic data from US census, data on mobility changes in retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, residential from Google and Apple |  SIRNET - a hybrid model comprising of Recurrent Neural network (RNN) and SEIR model
[50] |  COVID-19 cases from the county, socioeconomic data (e.g., age, gender, education, income) from various ministries, federal states, and municipal governments, built environment (e.g., density of airports, train stations, grocery stores, parks) from OpenStreetMap |  Bayesian Additive Regression Trees (BART) model
[51] |  Monthly Chinese tourist arrival data from the 2003 SARS outbreak up to October 2019 from the National Travel & Tourism Office and Australian Bureau of Statistics |  Long Short-Term Memory (LSTM) model
[52] |  COVID-19 cases and deaths, number of trips, social distancing index, out of county trips, transit mode share, population density, population staying at home, working from home, socioeconomic information (e.g., race, income, and employment status, unemployment rate) from University of Maryland (UMD), age category, education, male to female ratio, gross domestic product from US Census Bureau |  Ridge, LASSO, Elastic Net modeling techniques
[53] |  COVID-19 data from the European Centre for Disease Prevention, mobility in retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential areas from Google and driving, walking, and transit trips from Apple |  Gradient Boosting Decision Tree (GBDT)
[54] |  Coronavirus daily incidence from Worldometer, number of searches for Antiseptic, Handwashing, Hand sanitizer, and Ethanol from google search trend |  LSTM
[55] |  COVID-19 cases and deaths from the CDC, POI visits data from SafeGraph, population and income from US census, policies such as the closure of public venues and schools from local government |  COVID-GAN (Conditional generative neural network approach)
[56] |  An indoor video data recorded at the Institute of Management Sciences, Hayatabad, Peshawar Pakistan, which contains video sequences captured from the overhead view |  You Only Look Once (YOLO)
[57] |  COVID-19 cases and deaths from New York Times, mobility index from Google mobility reports, socioeconomic data (e.g., population, age, number of hospitals and ICU beds, percentage of smokers and diabetes, and heart disease mortality) from CDC |  Hawkes process using the expectation-maximization (EM) algorithm
[58] |  Transportation data (e.g., traffic volume, accident) from 73 signalized intersections, COVID-19 cases from Michigan’s official Coronavirus dashboard, weather factors (e.g., temperature and wind speed) from National Oceanic and Atmospheric Administration, social distancing data from UMD, Crash Data from Michigan State Police (MSP) |  Long Short Term Memory (LSTM)
[59] |  COVID-19 cases from local COVID-19 Dashboards, census-tract geographic boundaries and socioeconomic attributes from US Census Bureau, POIs with human travel patterns from SafeGraph |  Walktrap network-based community clustering method, modified SEIR model, Ensemble Kalman Filter
[60] |  Coronavirus cases, deaths, patients age, gender from the World Health Organization and John Hopkins University |  Decision Tree (DT) Classifier, Support Vector Machine (SVM) Classifier, Gaussian Naïve Bayes Classifier, Boosted RF Classifier
[61] |  Coronavirus cases and stay-at-home order from New York Times, socioeconomic (e.g., population density, labor force rate, unemployment rate, income) from US Department of Agriculture, Economic Research Service, weather information (e.g., temperature, wind speed, precipitation, solar radiation) from gridMET, mobility data from Google mobility reports |  Elastic net (EN) model, KNN, DT, RF, GBDT, and Artificial Neural Network (ANN)
[62] |  Mobile device location data based on GPS, call detail record, Cellular Network data, Social media Location-based Services, COVID-19 data from John Hopkins University, socioeconomic data from American Community Survey, travel information from National Household Travel Survey |  Deep learning algorithms
[63] |  Migration data (e.g., migration index, % of migrants coming from other cities, and internal travel flow index) from Baidu Maps LBS open platform, confirmed cases |  Ensemble-based back propagation neural network (BPNN) model
[64] |  Car traffic data from the road management center of Iran, air flight traffic data from Iranian’s airports and air navigation office, and COVID-19 daily new cases from John Hopkins University |  LASSO regression, KNN, RF, and ensemble learning

###  III-C Impact of lockdown and confinement measures on mobility and
COVID-19 pandemic

Many studies have assessed the effectiveness of lockdown and confinement
measures and travel restrictions imposed by state and governmental agencies to
control the massive outbreak of the COVID-19 pandemic. Al Zobbi et al. [45]
evaluated the effectiveness of these measures using data analytics and ML.
Collecting data from Google mobility reports and UNESCO’s website, they
estimated the mobility pattern of the people at retail and recreation sites,
grocery and pharmacy outlets, parks, transit stations, workplaces, residential
buildings, and schools during the period when the pandemic prompted lockdown
orders and confinement directives. They used the reproduction number (R0) to
represent the COVID-19 pandemic in 13 countries around the world. The daily R0
values were grouped into interquartile ranges and close monitoring of mean
values was performed to check the efficiency of daily social distancing
measures. Random Forest (RF) and KNN methods were used to establish the direct
correlation between lockdown and confinement measures and pandemic severity.
The results show a higher correlation (0.68) and the least MAE. Thus, these
measures have a significant influence on the coronavirus infection rate.
Dividing lockdown efficiency into four categories from Group A (highest
efficiency) to B, C, and D (lowest efficiency), they found higher efficiency
in Australia and South Korea. Efficiency improved significantly from group D
to group B, group D to group C, and group C to B in Germany, Spain, and India,
respectively. Despite not having strict lockdown measures, South Korea showed
high efficiency due to widespread self-quarantine and self-awareness in the
population. However, the US and Brazil showed lower levels of efficiency due
to the late implementation of lockdown measures. The study concluded that the
coronavirus infection rate dramatically dropped due to strict lockdown
procedures, self-quarantine, and people’s awareness about the disease.

By building a predictive model, researchers in [48] assessed the impacts of
different policy instruments (e.g., graded closing and reopening of retail
stores, workplaces, businesses, places of entertainment and worship, and
restriction on mobility) on the COVID-19 transmission rate in the 50 US
states. With data on policy instruments collected from press releases and with
data from Google’s mobility reports for the period of March 09 to August 2
2020, they used XGBboost to predict the transmission of COVID-19 under
different policy scenarios. With an $R^{2}$ value of 0.79 for training and
0.76 for testing, the model showed a robust estimation of the transmission
rate as a function of policy instruments. Thus, various policy instruments
have a significant influence on future transmission. This study concluded that
state agencies could ensemble policy instruments in a structured way for data-
driven decision making. Similarly, Badr et al. [73] studies the social
distancing index based on population mobility of 25 American counties. Using
the mobility data from Teralytics (Zürich, Switzerland), they found a 35-63%
reduction in mobility compared to normal conditions due to restrictions
imposed on mobility. The study also shows a strong correlation between
popuklation adherence to strict social distancing directives and COVID-19
cases reduction in the US.

Soures et al. [49] proposed a new hybrid ML model comprised of neural network
and epidemiology models (SIRNET) to understand the impact of social distancing
(i.e., mobility) on the spread of COVID-19 infections in the US, Italy, Spain,
Germany, France, and South Korea. With data assembled from multiple sources
(e.g., WHO, CDC, NY Times, US Census, Google, and Apple), they evaluated
COVID-19 situations in different geographic regions. The results demonstrate
that the SIRNET model is able to predict coronavirus cases by region using
mobility information. They observed that low mobility in a population group
has a significant impact on reducing the COVID-19 cases in this population.
Thus, they suggested keeping mobility at least below 50% of the nominal
mobility in the immediate future to control the pandemic until herd immunity
is achieved.

Researchers in [53] examined the role of social distancing measures on
COVID-19 transmission rates by integrating mobility data from Google and
Apple, and COVID-19 data from the European Centre for Disease Prevention and
Control in 26 countries of the world. The transmission rate was calculated
using the susceptible-infected-recovered (SIR) model. Their Gradient boosted
decision tree regression analysis indicated that mobility changes in retail
and recreation businesses, grocery stores and pharmacies, parks, transit
stations, workplaces, and residential areas due to social distancing policies
explain about 47% variation in the disease outbreak. Thus, controlling
restrictions on people’s attendance and mobility in public places with high
density of people are effective public health policy measures to mitigate the
impacts of the pandemic.

It is now well documented that strict lockdown and confinement measures and
proper practice of social distancing are very effective in mitigating the
COVID-19 pandemic by reducing mobility and gathering of people. However,
evidence much be used with great care and caution. To demonstrate this point,
we further investigated the impacts of social distancing practices on human
mobility and COVID-19 daily confirmed cases using the data collected from [72]
for the period of January 01 to December 26, 2020, Fig. 6. As anticipated, the
results indicate a negative correlation of the social distancing index with
daily trips per person. Intuitively, there should be a negative correlation
between the incidence of COVID-19 cases and the daily trips per person;
however, at first blush there is no significant correlation to be found. This
emphasises the criticality of reliable data collected over time across varied
communities, but also the importance of a modeling framework suitable for the
handling of complex, multiple and possibly non-linear relationships and of the
socio-spatial contexts of individual decisions, social group dynamics, and
public policy elements [74]. Thus, we contend that further research using ML
is fully warranted to reach strong conclusive statements.

(a)

(b)

Figure 6: Impacts of social distancing on mobility and COVID-19 pandemic.

###  III-D Socio-economic factors affecting mobility and travel patterns
during COVID-19 pandemic

Besides the severity of the pandemic and related confinement and lockdown
measures, other social, economic, and environmental factors may also affect
the mobility and travel patterns of the population. A study in [52]
investigated the factors that affect human mobility and travel in the US
during the COVID-19 pandemic using OLS regression, ridge regression, LASSO,
and Elastic Net modeling techniques. With statewide data from January 1, 2020,
to June 13, 2020, and dividing them into train and test data sets, the study
examined that ridge regression provides superior results with the least error.
However, LASSO and Elastic Net modeling techniques performed better than OLS
regression. The results indicate that the number of daily trips per person has
a negative association with the number of new cases, social distancing index,
median income, percentage of elderly population, number of people staying at
home, socioeconomic status, stay-at-home order, and domestic travel
restrictions. In contrast, the number of daily trips per person has a positive
association with transit mode share, percentage of Hispanic and African
American population, mandatory statewide mask policy, and driving mobility
index. Finally, using LASSO regression, the study found that the percentage of
the population over 60, social distancing index, and percentage of the
population working from home have higher impacts on the number of daily trips
per person compared to other covariates.

Another study investigated spatial (e.g., population density) and aspatial
(e.g., socioeconomic) factors of coronavirus transmission over 401 counties in
Germany [50]. The Bayesian Additive Regression Trees (BART) model demonstrated
that higher densities of churches were the most important factors for
predicting COVID-19 cases due to higher walkability, interpersonal
interactions, higher social connectivity, and community engagement,
particularly among senior and elderly populations. Similarly, long-distance
train stations increase the probability of inter-personal coronavirus
transmissions due to the high density of travelers and their long-distance
interactions. Among the socioeconomic factors, in-person participation in vote
casting was identified as the strongest predictor of COVID-19 transmission.
The study also noticed that the foreign guests in tourist establishments,
employment density, community centers, beauty salons, etc., significantly
impacted on COVID-19 transmissions. Therefore, they suggested implementing
social distancing measures and reduce unnecessary travel to reduce coronavirus
infection.

Researchers in [7] investigated the relationships between socio-economic
characteristics of the people and human mobility in the context of the
COVID-19 pandemic after collecting geolocated human mobility data from
SafeGraph in Los Angeles, US. Quantifying mobility indices and social
distancing metrics, they classified census blocks into areas of High, Medium,
and Low vulnerability to the COVID-19 pandemic using supervised ML classifiers
such as Gradient Boosting, Support Vector Machines, and Multinomial Logit
models. The results indicated that tree-based classifiers (i.e., Gradient
Boosting) performed well with an accuracy of 97.4% and area under curve (AUC)
score of 98.7% compared to Random Forest (96.8%), Support Vector Machines
(90.5%), and Multinomial Logit models (91.3%). In substance, the study
reported that socially vulnerable populations, high mobility indices and low
social distancing index increases the vulnerability of the local communities
to COVID-19 infection.

Using the expectation-maximization (EM) algorithm, the study in [57] reported
that the Hawkes process –a model that simultaneously estimates the intensity
of the events and tracks dynamics of the reproduction number of the virus–has
a good potential to predict COVID-19 transmission with minimum MAE and
percentage error (PE). It also demonstrated that the performance of the
prediction model increases substantially when the modeling of disease
transmission is integrated with mobility, social distancing measures, and
other socio-demographic covariates (e.g., population, median age, number of
hospitals and ICU beds, percentage of smokers and diabetic patients, and heart
disease mortality). Estimating Poisson regression, the study mentioned that
the reproduction number is positively associated with mobility changes in
retail/recreation and grocery/pharmacy. In contrast, it is negatively
associated with mobility changes in transit stations, parks, and residential
areas. They also found that the reproduction number is positively associated
with higher population density, number of hospitals, and ICU beds. In
contrast, the reproduction number is negatively associated with median age,
percentage of the population with diabetes, heart disease, and smoking habits,
which imply that people with high health risks are more cautious and tend to
live in areas with fewer coronavirus cases and population density, which
protects them from getting infected.

The above discussion reveals the facts that in addition to COVID-19 related
factors, socioeconomic situations of the people influence human mobility. For
example, disadvantaged segments of society (e.g., elderly, people with heart
disease) voluntarily limit their movement due to their concern and fear of
exposure and infection. On the other hand, people who live in areas with more
health care facilities would be more comfortable to travel around due to their
preparedness to control any unwanted situations. Thus, socioeconomic, health
and environmental factors have a significant impact on mobility and the
COVID-19 pandemic.

###  III-E COVID-19 interactive platforms and dashboards

A number of platforms have been developed to provide real-time COVID-19
related information such as new cases, deaths, testing, hospitalization,
contact tracing, COVID-19 prediction, lockdown and confinement measures,
impacts of COVID-19 on daily mobility, economy, and socioeconomic
characteristics of people, etc. For example, Zhang et al. provided a COVID-19
impact analysis platform to understand the daily impact of COVID-19 on
mobility, economy, and society [72, 62]. The proposed interactive model uses
location data of mobile devices, COVID-19 cases data, and population census
data to compute the social distancing index of the US states or counties. The
computed social distancing index provides an estimate of how likely the people
of a state or a county are obeying the government order about social
distancing and hence helps to reduce the spreading of the disease in that
region. The location data representing the movement of human and vehicles are
used in different deep learning algorithms to impute or infer other mobility-
related data such as travel mode (air, car, bus, walking, etc.), trip length,
trip purpose, points-of-interest visited (restaurants, shops), and socio-
demographics of the travelers (income, gender, race, etc.). The imputation
shows more than 90% accuracy. The computational algorithms are also validated
based on a variety of independent datasets such as the National Household
Travel Survey and the American Community Survey. The resultant datasets are
daily updated and publicly available.

The Johns Hopkins Coronavirus Resource Center (CRC), through their interactive
platform [75] continuously updates the sources of COVID-19 data and expert
guidance. To inform the public, policy makers, and healthcare professionals
about the COVID-19 pandemic and to respond accordingly, the platform
aggregates and analyzes the COVID-19 cases, testing, contact tracing, and
vaccine efforts. The COVID-19 project model of Institute for Health Metrics
and Evaluation (IHME) [76] is perhaps the most visited COVID-19 prediction
model in the US. In response to requests from the University of Washington
School of Medicine and other US hospital systems and state governments, IHME’s
COVID-19 projections were developed to determine when COVID-19 would overwhelm
their ability to care for patients. IHME’s COVID-19 forecast model shows the
daily demand for hospital services, daily and cumulative deaths due to
COVID-19, rates of infection and testing, and the impact of social distancing
measures organized by county and by state.

These interactive platforms are helping policy makers, health professionals,
and researchers to understand the interplay between different factors of
COVID-19 spread and undertake appropriate policy measures to control pandemic
situations and improve quality of lives.

###  III-F Applications of ICT and ML techniques for COVID-19 surveillance

Information and communication technologies (ICTs) are popularly being used in
different places around the world to monitor COVID-19 situations (Table II).
To facilitate human movement during the pandemic, some studies have explored
possible options for transferring people safely from origin to destination.
For example, Darsena et al. [77] proposed a Safe and Reliable Public
Transportation Systems (SALUTARY) system to proactively tackle crowding
situations in public transportation (PT) during the COVID-19 pandemic using
the technologies of the Internet of Things (IoT). The system is proposed for
adoption in the various segments of the public transportation system
(buses/trams/trains, railway/subway stations, and bus stops) to (i) monitor
and predict crowding events, (ii) adapt public transport system operations in
real time (i.e., modifying service frequency, timetables, routes, and so on),
and (iii) inform the users by electronic displays installed in correspondence
of the bus stops/stations and/or by mobile transport applications. Another
study used a ML driven intelligent approach to trace daily train travelers in
different age cohorts of 16-59 years and over 60 (i.e., vulnerable age-group)
to recommend certain times and routes for safe traveling [78]. The study
utilized the ICTs, including WiFi, RFID, Bluetooth, and UWB (Ultra-WideBand).
The LUO (London Underground and Overground) Network dataset was used and
various ML algorithms were compared to correctly classify different age group
travelers. The results of the models indicate that the Support Vector Machine
(SVM) performs better to predict the mobility of travelers with an accuracy of
up to 86.43% and 81.96% in the 16-59 and over 60 age groups, respectively.

Researchers in [55] estimated the changes in people mobility during the
pandemic and local policy measures using a Spatio-temporal Generative
Adversarial Network (COVID-GAN), a conditional generative neural network
approach. They collected and integrated data from multiple sources (e.g.,
SafeGraph, US Census, CDC, and local government) for the city of Boston, US to
provide multi-view insights in estimating mobility changes. The experimental
results show that COVID-GAN sufficiently mimics real-world scenarios and
performed reasonably well for a spatially unseen region (i.e., regions are not
included in the training dataset) that is relatively small and is still
adjacent to the spatially seen data. Similarly, this method can maintain a
high quality to predict human mobility for the unseen periods, which is
important to evaluate the effects of ongoing COVID-19 related policies.

Ahmed et al. [56] have detected violation of social distancing measures using
a deep learning-based social distancing monitoring framework using the YOLOv3
object recognition paradigm in video sequences. Moreover, this approach used
the transfer-learning method to enhance model performance. The experiment
results show that the developed framework efficiently identifies the people
who walk too close and violate social distancing measures with an accuracy of
92% and 98% without and with transfer learning, respectively. The model has a
tracking accuracy of 95%. Thus, video surveillance using emerging technologies
(e.g., Bluetooth, smartphones, global positioning systems, computer vision,
ML, and deep learning) are very effective and provide critical solutions for
enforcing social distancing measures during emergencies. Research has shown
that applications of ICT and ML-based approaches are crucial to direct people
on how to travel during this pandemic situation.

##  IV Urban Air quality

Besides mitigating COVID-19 transmission and associated risks, confinement and
lockdown measures yield some additional health benefits by inducing a drop in
air pollutants [79, 80, 81, 82, 83]. Researchers estimated that emission of
${PM_{2.5}}$, ${SO_{2}}$, and ${NO_{2}}$ was reduced by 2.5 Gt, 0.6 Mt, and
5.1 Mt, respectively in the world from the start of the pandemic through May
2020 [82]. Many studies empirically investigated the impacts of COVID-19
related confinement and lockdown measures on air quality in urban areas
specifically (Table III). The main results of these studies are reported in
Table IV. Some of these recent studies have found that social distancing
measures can improve air quality significantly [84, 85, 86]. Researchers
estimated $NO_{2}$ reduction of 22·8 $\mu g/m^{3}$ in Wuhan and 12·9 $\mu
g/m^{3}$ in 367 cities of China [87]. It is also reported that $PM_{2.5}$ was
reduced by 1·4 $\mu g/m^{3}$ in Wuhan and 18·9 $\mu g/m^{3}$ across 367 cities
of China. NASA’s scientists reported a 30% reduction in $NO_{2}$ emissions in
Central China [88]. The same study also estimated 25% and 6% reduction in
$CO_{2}$ emissions in China and worldwide, respectively. About 50% reduction
of $PM_{2.5}$ and $PM_{10}$ were observed in New Delhi, India after the
implemention of lockdown measures [89] in March 2020. Another study in India
[80] reported reduction in $PM_{10}$, $PM_{2.5}$, $NO_{2}$ and $SO_{2}$
concentration in Delhi (55%, 49%, 60%, and 19%, respectively) and Mumbai (44%,
37%, 78% and 39%, respectively). A 36% and a 51% reduction in $PM_{2.5}$ and
$NO_{2}$ concentrations, respectively, were noted shortly after the shut down
of New York City [90]. Quito, Ecuador, also reported 5.6 times and 4.8 times
lower concentration of $NO_{2}$ in 2020 compared to 2018 and 2019,
respectively [91]. Another study from South America reported a drastic
reductions in the concentrations of $NO$ (up to 77.3%), $NO_{2}$ (up to
54.3%), and $CO$ (up to 64.8%) in the State of São Paulo, Brazil [92].
Therefore, it is evident from recent studies that urban air quality improved
substantially due to citywide lockdown measures and travel cutbacks from local
populations and businesses.

The reduction in criteria pollutants in the atmosphere indirectly saved human
lives. Researchers in [88] mentioned that improved air quality during the
quarantine period avoided a total of 8,911 $NO_{2}$-related deaths in China
(i.e., about 6% of normal deaths in China). Another study [93] estimated that
improved air quality has saved at least 19% of premature deaths because of
regular respiratory illness and 11% year of life lost. Moreover, a number of
studies also reported an increase in $O_{3}$ due to the reduction of other
pollutants (i.e., $NO_{2}$, $PM_{2.5}$) [91, 92, 94]. For example, researchers
in [92] estimated a 30% increase in ozone concentration in the urban areas of
the State of São Paulo, Brazil. Despite a significant decrease in the
concentration of primary pollutants (i.e., $SO_{2}$ 5-28%, $NO_{2}$ 1-33%,
$CO$ 5-41%, $PM_{10}$ 1.4-30%), the concentration of $O_{3}$ increased by
0.5-103% in Iran [94]. Thus, improving air quality has associated benefits
which points to the need to undertake an integrated policy option to reduce
air pollution in the urban areas to protect public health.

Fig. 7 illustrates daily mean air quality changes in 2020 compared to 2019 in
North America and Europe. The changes in five criteria pollutants in major
cities are mapped to understand the overall impacts of lockdown measures,
travel cutbacks, and pause in industrial production on air quality. Many
recent studies confirmed that air quality improved due to these factors
overall. However, as Fig. 7 indicates, quality improvement may vary across
pollutants and geographic locations. The concentration of ${NO_{2}}$ decreased
significantly in most of the cities in Europe and North America. It is
observed that the pandemic seems to have a limited and no impact on ${SO_{2}}$
concentration. An opposite scenario can be observed for ${O_{3}}$
concentration which is increased significantly in most cities. This is
probably because of the fact that ${O_{3}}$ formation depends on the
availability of other pollutants. The concentration of particulate matters
mostly decreased in cities of Europe, whereas it is increased in most of the
mid-eastern cities in the USA. Although, there could be many factors
associated with air quality, Fig. 7 indicates the impact of the level of
various aspects of the pandemic, including lockdown measures. Europe was under
much stricter lockdown measures compared to North America. As a result, most
of the criteria pollutants are shown to have decreased in Europe.

Figure 7: Daily mean air quality change in 2020 compared to 2019 in the US and Europe. Data source: EPA and World Air Quality Index Project, available at [95]. TABLE III: Geographical context and objectives of past studies on COVID-19 and air quality. Author |  Study context |  Objective
---|---|---
[70] |  US |  Investigating the impacts of environmental, health, socioeconomic, and demographic risk factors on COVID-19 death rates.
[71] |  Labrador, Newfoundland, Canada |  Exploring the impact of partial and total reopening of airports on COVID-19 outbreak
[83] |  US |  Predicting the number of COVID-19 cases at the county level.
[91] |  Quito, Ecuador |  Analyzing the effect of quarantine policies on air quality (i.e., the concentration of $NO_{2}$, $PM_{2.5}$, and $O_{3}$).
[93] |  Lombardy, Italy |  Investigating the effect of lockdown measures on $PM_{2.5}$ and $O_{3}$ concentration.
[96] |  Kanpur, India |  Prediction and analyze the spatial distribution of $PM_{2.5}$ during various phases of lockdown for COVID-19.
[97] |  Graz, Austria |  Investigating the effect of lockdown measures on air quality ($NO_{2}$, $PM_{10}$, $O_{3}$, and Total Oxidant).
[98] |  46 countries of the world |  Investigating the effect of lockdown measures on $NO_{2}$ and $O_{3}$ emission.
[99] |  Spain |  Measuring the impact of lockdown measures on $NO_{2}$ emission.
[100] |  Lima, Peru |  Exploring the effect of air pollution on COVID infection.
[101] |  Delhi, India |  Monitoring the effect of lockdown on the various air pollutants during the coronavirus pandemic.
[102] |  Dhaka, Bangladesh |  Studying the impact of lockdown scenarios on air quality and COVID-19 transmission.
[103] |  Sao Paolo, Brazil |  Investigating the impacts of lockdown measures under four hypothetical scenarios (i.e., 10%, 30%, 70%, and 90% lockdown) on air pollution levels ($CO$, $O_{3}$, $NO_{2}$, NO, $PM_{2.5}$, and $PM_{10}$)
[104] |  Paris, Lyon, and Marseille, French |  Investigating the relationship between air pollution and COVID-19 transmission.
[105] |  Emilia-Romagna, Italy |  Predicting the possibility of resurgence (second wave) of the COVID-19 pandemic
[106] |  Hangzhou, China |  Estimated the impacts of COVID-19 lockdown measures on Black carbon (BC) emissions
[107] |  30 cities in China |  Quantifying the impact of COVID-19 lockdown on the concentration of air pollutants.
[108] |  Italy |  Evaluating the effects of exposure to air pollutants on COVID-19 susceptibility.
[109] |  30 European countries |  Quantifying the reduction of primary pollutants from the energy industry, manufacturing industry, surface, and air transportation during COVID-19 lockdowns.
[110] |  25 major cities, India |  Exploring the relationships between pollutants emission, economic growth, and COVID-19 related deaths
[111] |  Portici City, Italy |  Monitoring air quality during COVID-19 pandemic with the help of IoT intelligent multisensory devices.
[112] |  6 Megacities, China |  Analyzing the impacts of COVID-19 related lockdown measures on air quality.
[113] |  100 European cities |  Estimating changes in NOx due to COVID-19 lockdown.
TABLE IV: Changes in air pollutants induced by COVID-19 related lockdown measures (’-’ sign indicates a decrease and ’+’ sign indicates an increase in pollutants). Study |  $\mathbf{CO}$, $\mathbf{CO_{2}}$ |  $\mathbf{NO}$, $\mathbf{NO_{2}}$ |  $\mathbf{PM_{2.5}}$, $\mathbf{PM_{10}}$ |  $\mathbf{SO_{2}}$ |  $\mathbf{O_{3}}$
---|---|---|---|---|---
[93] |  - |  -33% $NO_{2}$ |  -15% $PM_{2.5}$ |  - |  -
[96] |  - |  - |  -4% (for lockdown), -47% for post lockdown with previous year |  - |  -
[97] |  - |  -(36.9 - 41.6)% |  -(6.6 - 14.2)% |  - |  \+ (11.6 - 33.8)%
[98] |  - |  -18% |  - |  - |  +50%
[99] |  - |  -50% $NO_{2}$ |  - |  - |  -
[100] |  - |  -36% $NO_{2}$ |  Insignificant change |  - |  -
[101] |  $CO$ Decreased |  $NO_{2}$ Decreased |  Both Decreased |  Decreased |  Increased
[102] |  -8.8% $CO$ |  -20.4% $NO_{2}$ |  -26% $PM_{2.5}$ |  -17.5% |  -9.7%
[103] |  Decreased $CO$ |  Decreased NO & $NO_{2}$ |  Decreased $PM_{2.5}$ & $PM_{10}$ |  - |  Decreased
[107] |  Insignificant $CO$ change |  -63% $NO_{2}$ (fell by 24ug/m3) |  -63% $PM_{10}$ |  Insignificant change |  -
[109] |  - |  -33% $NO_{2}$ |  -7% $PM_{2.5}$ |  -7% |  -
[111] |  -(7-11)% $CO$ |  \- (23 - 37)% $NO_{2}$ |  -(14 - 20)% $PM_{10}$, -(7 - 16)% $PM_{2.5}$ |  -(2 - 20)% |  -
[112] |  $CO$ decreased |  -(36-53)% $NO_{2}$ |  $PM_{2.5}$ Decreased |  - |  Decreased

###  IV-A Impact on concentration of Particulate Matters (PM)

Particulate Matters (PM), composed of solids and gases, are largely generated
during the burning of fossil fuels and woods in the transportation,
manufacturing industry, and power plants [114]. Due to lockdown measures,
human mobility is reduced, and the majority of industries are closed; hence,
emissions of PM have been reduced substantially. Some researchers have
investigated the level of PM in the air during lockdown periods and compared
the results to pre-COVID-19 situations to assess the changes in air quality.
For example, Mallik et al. [96] estimated the concentration of $PM_{2.5}$ in
Kanpur city, India for three different lockdown phases: pre, during, and post
lockdown conditions in relation to the pandemic. They used four different ML
approaches to estimate $PM_{2.5}$ from remote sensing-based MODIS Aerosol
Optical Depth (AOD) data and meteorological parameters, including temperature,
rainfall, relative humidity, wind speed, and mixing height. A hybrid ML
approach that combines ANN and Multiple Linear Regression (MLR) shows the
highest performance for the estimation of $PM_{2.5}$. The hybrid approach
outperformed ($R^{2}=0.96$) Linear Regression (LR) ($R^{2}=0.016$), MLR
($R^{2}=0.246$) and ANN ($R^{2}=0.895$) models. The authors compared the
estimated $PM_{2.5}$ of this year with the previous year and reported 4% and
47% reduction in $PM_{2.5}$ during the lockdown and post-lockdown condition,
respectively. From the analysis, they concluded that $PM_{2.5}$ is reduced
because of the reduction in emissions from industries and transport vehicles.

The IQAir [115] analyzed $PM_{2.5}$ levels in 10 major and severely affected
cities in the world by observing three weeks of the strictest lockdown
conditions in-between February and April in 2020 (Fig. 9). The figure
indicates that 9 out of 10 global cities have experienced a reduction in
$PM_{2.5}$ during the mentioned period in 2020 compared to 2019. The report
also mentioned that the cities with higher levels of $PM_{2.5}$ concentration
achieved the most substantial reductions (e.g., Delhi, Seoul, Wuhan) due to
restriction on vehicular movement, closure of the educational institutions,
industries, and workplaces, and shutdown of all non-essential businesses. In
contrast, Rome showed a 30% increase in $PM_{2.5}$ concentration due to a
higher reliance on residential heating systems, and the shift away from public
transportation to private cars.

Simulating four hypothetical scenarios (i.e., 10%, 30%, 70%, and 90% of
lockdown), Tadano et al. [103] predicted air pollution levels ($PM_{2.5},\
and\ PM_{10}$) in São Paulo, Brazil. Various data, including the daily number
of COVID-19 cases, partial lockdown level, and meteorological variables, were
feed into four ANN models (Multilayer Perceptron, Radial basis function,
Extreme Learning Machines, Echo State Networks) to simulate pollutant levels.
The result shows that Multilayer Perceptron (MLP) outperformed other ANN
models. The model results reported a decrease in the concentration of
$PM_{2.5},\ and\ PM_{10}$. It is also evident that there is a negative
correlation between lockdown levels and air pollutants (i.e., a higher level
of lockdown measures reduces the amount of PM in the air to a greater extent).

Grabekka et al. [93] has investigated the effect of lockdown on $NO_{2}$
because of the dramatic decrease in mobility and economic activities in
Lombardy, Italy. For that purpose, they built a synthetic counterfactual
estimation of air quality based on meteorological variables using the Extreme
Gradient Boosting Regressor. The model was trained by the atmospheric data
between 2012 and 2019 from 227 weather stations, where, meteorological
variables are the predictor variables and air qualities are the predicted
variables. Like other studies, they also considered calendar variables such as
the day of the year to capture the trend over time. Unlike other studies, they
utilized a ratio between $PM_{2.5}$ and $PM_{10}$ as an additional predictor
variable to avoid the impact of the pollutants transported from a long
distance, such as mass dust from the Caspian Sea. The correlation value over
0.87 between observed and predicted values before lockdown indicates the
strength of the model. The result shows that the lockdown reduced the
concentration of $PM_{2.5}$ and $NO_{2}$ by 3.84 $\mu g/m^{3}$ (16%) and 10.85
$\mu g/m^{3}$ (33%), respectively.

Some other studies also reported the role of lockdown measures for the
reduction of $PM_{2.5}$ concentration in the atmosphere [111, 112]. A study
estimated a reduction of $PM_{2.5}$ by 7% in European countries [109].
Similarly, another study shows an average of 14.2% reduction in $PM_{10}$
compare to estimated concentration in 2020 and 11% reduction compared to the
historical mean value between 2014 and 2019 [97]. In a developing country
context, a study reported a 26% to 54.2% reduction in the level of $PM_{2.5}$
concentration during full and partial lockdown scenario in Dhaka, Bangladesh
[102].

###  IV-B Impacts on the concentration of nitrogen oxides $(NO_{x})$

The main source of nitrogen oxides (i.e., $NO$, $NO_{2}$) is burning of fossil
fuel in the transportation, industries, and power plants [114]. These highly
reactive gases have detrimental effects on public health by affecting the
respiratory systems of people, besides broader environmental consequences.
Many studies in different geographical contexts have reported a reduction in
the concentration of nitrogen oxides during the COVID-19 lockdown periods due
to the limited mobility of people and the closure of workplaces and
industries. Change in traffic emission during lockdown played a major role in
the substantial reduction in ${NO_{2}}$ in six megacities of China [112].
Random forest learning-based models reported a reduction of ambient ${NO_{2}}$
concentration of about 36-53% during a four-month (January to April 2020)
lockdown due to the formation of secondary aerosols, which enhanced the
oxidizing capacity of the atmosphere. Subsequent lifting of level-1 control
action caused ${NO_{2}}$ to drop below 10% in late April.

Petetin et al. [99] estimated the business-as-usual $NO_{2}$ mixing ratio in
more than 50 provinces and islands across Spain and compared it with observed
hourly $NO_{2}$ concentration to evaluate the impact of lockdown measures in
March 2020. A Gradient Boosting ML algorithm was trained on the $NO_{2}$
concentration from January 1 to 23rd April 2020 and meteorological data
including temperature, pressure, wind speed, cloud cover, solar radiation,
ultra violet radiation, and calendar variables. The model shows reliable
performance based on the model uncertainty assessment where overall bias, root
mean square error, and correlation are +4%, 29%, and 0.86, respectively. The
result shows an overall 50% reduction in $NO_{2}$ due to a reduction in road
and air transportation during lockdown periods. Using a similar ML method, a
study [113] estimated the reduction in $NO_{2}$ emission to be 23% to 43% in
100 European cities. They also mentioned that the cities with strict lockdown
measures have experienced a stronger reduction in $NO_{2}$. Another study in a
similar context (i.e., city of Portici, Italy) [111] used multilinear
regression (MLR) model and a shallow neural network (SNN) model to monitor air
quality with the help of IoT intelligent multisensor devices during phase 2 of
the pandemic. Results suggested that due to the reduction of mobility,
particularly from car travel during the lockdown period, the concentration of
${NO_{2}}$ has dropped significantly.

The study in [109] has used gradient boosting machine (GBM) models to quantify
the diminution of primary pollutants from the energy industry (e.g., power
plants, heat plants), manufacturing industry, surface and air transportation
in 30 European countries during the COVID-19 lockdowns. Data collected from
different sources dated from 21 February to 26 April 2020 and historical data
of the previous 5 years (2015-2019) during the same time period to assess the
level of pollutants in the air. The results indicate that severe lockdown at
the EU-30 level countries caused average emission reduction of ${NO_{x}}$,
Non-methane volatile organic compounds (NMVOC), and ${SO_{x}}$ by 33%, 8%, and
7%, respectively. However, the steepest reduction in ${NO_{x}}$ (50%) can be
imputed to the closure of the transportation sector, which is responsible for
about 85% reduction of all pollutants overall. It is also evident that the
drop of ${NO_{2}}$ reached up to 58% in the urban areas, whereas it was only
44% in rural areas; the average contribution of the transportation sector was
86% and 96%, respectively.

Another approach to investigate the impact of lockdown measures on air quality
is to estimate air pollutants for 2020 based on the trend of previous years
and to compare it with the true values of these pollutants. This approach
generates a synthetic estimate of what air quality would have been without a
lockdown effect based on historical data. Lovric et al. [97] investigated the
lockdown effect on air quality at five monitoring stations in Graz, Austria.
Four different pollutants ($NO_{2},O_{3},PM_{10},\ and\ O_{x}$) were estimated
based on meteorological data series, including air temperature, precipitation,
wind speed, wind direction, and air pressure. The Random Forest algorithm was
trained by the historical meteorological variables and pollutants data between
2014 and 2019. Based on the relationship between meteorological variables and
pollutants level, air quality was estimated for the lockdown period. Finally,
the estimated values were compared with observed values at the five monitoring
stations to evaluate the impact of lockdown on air quality. The results show
an average reduction of 36.9% in $NO_{2}$ due to the lockdown effect. Authors
also compared the air quality of 2020 with the historical mean between 2014
and 2019, which also indicates a 38.1% reduction in $NO_{2}$.

Keller et al. [98] developed a bias-corrected model (BCM) for the NASA global
atmospheric composition model (GEOS-CF) using the XGBoost ML algorithm. They
utilized this BCM model to estimate $NO_{2}$ from eight meteorological
parameters, including surface wind components, surface temperature, relative
humidity, cloud coverage, precipitation, pressure, and planetary boundary
layer. The ML predictor was trained on 2018-2019 data to predict the model
bias for the observation sites in 2020 to adjust the predicted concentrations
from the GEOS-CF model. This study estimated $NO_{2}$ at 5756 observation
sites in 46 countries across the globe between January to June 2020. They
found that on average $NO_{2}$ concentrations were 18% lower than the
business-as-usual level.

Similarly, Grabekka et al. [93] found that the lockdown reduced the
concentration of $NO_{2}$ by 10.85 $\mu g/m^{3}$ (33%). A 20.4% to 55.5%
reduction in $NO_{2}$ during full and partial lockdown scenario, respectively,
compared to the pre-COVID situation, was also reported in Dhaka, Bangladesh
[102]. Thus, it is evidenced that the concentration of $NO_{x}$ has reduced
significantly in urban areas due to city-wide and countrywide lockdown
measures and closure of transportation and workplaces [103].

###  IV-C Impacts on the concentration of $CO_{x}$

Similar to nitrogen oxides, carbon oxides (i.e., $CO$, $CO_{2}$) are also
released by burning fossil fuel by vehicles and industries, and by biomass
[114, 106]. These colorless gases can negatively affect human health when
present at a high level of concentration in the air. However, studies have
found that the level of carbon emission dropped significantly during the
COVID-19 pandemic [103, 116]. These studies empirically investigated changes
in carbon emission during the lockdown periods. For example, A study in Dhaka,
Bangladesh, [102] investigated the impact of different lockdown scenarios on
air quality and on COVID-19 transmission using data from weather monitoring
stations. Using generalized additive models (GAMs), wavelet coherence, and
random forest (RF) models, this study found a 8.8% to 23.5% reduction in CO
concentrations during full and partial lockdown scenarios, respectively,
compared to the pre-COVID situation. Similarly, this study reported a 17.5% to
48.1% reduction in $SO_{2}$ during full and partial lockdown scenarios,
respectively, compared to the pre-COVID situation.

After collecting data on energy, human activities, and policy measures,
researchers in [117] estimated daily changes in carbon emissions in 2020
compared to 2019 for three-level of confinement (low, medium, and high) and
for six sectors of the economy (e.g., power, industry, surface transportation,
public building and commerce, residential, and aviation). Fig. 8 shows the
total changes in ${CO_{2}}$ emission in the world as a whole, as well as the
changes in the six economic sectors under three confinement scenarios for the
period of 01 January 2020 to 31 December 2020. The figure demonstrates a
positive association between COVID-19 related confinement and changes in
$CO_{2}$ emission (i.e., a higher level of confinement reduces further
$CO_{2}$ emissions from all economic sectors). The peak reduction in $CO_{2}$
emissions is evidenced in March and April due to the severity of the pandemic,
substantial mobility reduction, and associated fear and anxiety. However, the
reduction in $CO_{2}$ emissions from transportation and industry is relatively
greater than other sectors due to strict restrictions on mobility, closure of
workplaces and industries.

(a) $CO_{2}$ emissions under high level of confinement.

(b) $CO_{2}$ emissions under medium level of confinement.

(c) $CO_{2}$ emissions under low level of confinement.

Figure 8: Longitudinal changes in $CO_{2}$ emissions in the world under
confinement scenarios. Figure 9: Changes in $PM_{2.5}$ levels in 2020 compared
to 2019 in 10 major global cities during COVID-19 related lockdown periods
between February and April 2020 [115].

Researchers in [106] estimated the impacts of COVID-19 lockdown measures on
black carbon (BC) emissions in urban and urban‐industry areas, suburbs, and
rural areas of Hangzhou, China using a multiwavelength Aethalometer model.
Analyzing data collected from nine observation sites, the study showed a city-
wide 44% reduction in BC from 2.30 to 1.29 $\mu g/m^{3}$ due to reduction of
vehicle emissions in the urban areas and biomass burning in the rural areas
during the COVID‐19 lockdown periods. More precisely, this study reported 47%,
49%, 41%, and 38% BC emission in urban, urban industrial, suburban, and rural
areas, respectively. Using a similar method, researchers in [118] found
reductions of 78%, 67%, 53%, 59%, 74%, and 66% in BC during Lockdown-1 (25th
March-14th April, 2020), Lockdown-2 (15th April-3rd May, 2020), Lockdown-3
(4th-17th May, 2020), Lockdown-4 (18th-31st May, 2020), Unlock-1 (June 2020),
and Unlock-2 (July 2020) situations, respectively compared to the pre-Lockdown
period (18th Feb-24th March, 2020). Using Concentration Weighted Trajectories
(CWT) analysis, this study concluded that local sources (e.g., fossil fuel,
biomass burning) were primarily responsible for BC concentration over Delhi,
India.

Thus, the closure of public transport, workplace, and industry due to
government-mandated lockdown measures significantly reduces carbon emission by
decreasing the burning of fossil fuel, solid substances, and biomass in urban
and rural areas [119, 120].

###  IV-D Impacts on the concentration of $O_{3}$

Unlike the other pollutants, most of the studies have mentioned that the
concentration of $O_{3}$ has increased dramatically during the COVID-19
lockdown periods [103, 121, 122, 123]. Experts mentioned that the reduction in
traffic emission is largely responsible for increasing ${O_{3}}$
concentrations [112]. For instance, researchers in [97] observed that $O_{3}$
concentration was increased significantly between 11.6% to 33.8% during the
lockdown period in Graz, Austria, which can be explained by the reduction of
the $NO-O_{3}$ titration cycle when $NO_{x}$ emission was low during the
lockdown. Similarly, $O_{3}$ concentration was increased by up to 50% in some
locations of the world where $NO_{2}$ was also reduced significantly [98].
However, the response of $O_{3}$ is nonlinear to the reduction of $NO_{2}$.
Some cities such as Beijing and Madrid show very little difference in $O_{3}$,
while having declines in $NO_{2}$ comparable with other cities. The study in
Dhaka, Bangladesh [102] also observed a 9.7% to 31% reduction in $O_{3}$
concentration during full and partial lockdown scenarios, respectively,
compared to the pre-COVID situation. Thus, it can be concluded that the
overall level of $O_{3}$ increased significantly during the lockdown periods
in many parts of the world.

###  IV-E Impacts of air pollutants on COVID-19 transmission and death rate

According to public health researchers, indoor and outdoor air quality
significantly affects human health [124, 125, 126]. Evidence indicates that
prolonged exposure to air pollution could increase the risk of viral
infections, and respiratory illness [108]. Moreover, small particles in the
air may facilitate the viral spread like coronavirus. Thus, the scientific
community has grown more interested in knowing the effect of air quality on
COVID-19 transmission. At the beginning of the COVID-19 pandemic, many
scientists investigated the role of weather variables (e.g., temperature,
pressure, precipitation, humidity) and air quality on the virus transmission
rate. However, the scientist community now believes that weather conditions
may have very little or no effect on virus transmission. Many studies have
already investigated the relationship between local air quality and COVID-19
infection and death rate.

A study [104] explored the relationship between particulate matter ($PM_{2.5}\
and\ PM_{10}$) and COVID-19 death using ANN for three cities in France. They
found different thresholds of the concentration of particulate matter
responsible for fatality in different cities, for instance 17.4 $\mu g/m^{3}$
($PM_{2.5}$) and 29.6 $\mu g/m^{3}$ $PM_{10}$ for Paris. The results indicate
that an increase in $PM_{10}$ concentration beyond 29.6 $\mu g/m^{3}$ could
generate a 63.2% increase in mortality. Similarly, any value above 20.6 $\mu
g/m^{3}$ in $PM_{10}$ would generate an increase in deaths of 56.12% in Lyon,
France. Similarly, a study in the Lima metropolitan area, Peru investigated
the relationship between the COVID-19 infection rate and major air pollutants
($CO$, $NO_{2}$, $SO_{2}$, $PM_{2.5}$, and $PM_{10}$) [100]. The study used
Reduced-spaced Gaussian Process Regression and ANN models. The result
indicates that the industrial area shows a higher number of infections
compared to other land use zones. The industrial zone features the highest air
pollution because of the high concentration of $NO_{2}$, $PM_{2.5}$, and
$PM_{10}$.

Using various ML techniques, including Decision Trees, Linear Regression, and
Random Forest, researchers in [101] computed the correlation between air
pollutants and COVID-19 fatality in Delhi, India. The result shows that
COVID-19 fatality is positively correlated with a few air pollutants such as
$O_{3}$, $SO_{2}$, and $NH_{3}$. Moreover, results from three different ML
algorithms indicate that $NH_{3}$, $NO_{2}$, and $PM_{10}$ are the important
factors of COVID-19 related fatalities. Similar to other studies around the
world, this study also shows that there is a significant increase in ozone and
toluene concentration in the air because of the lockdown measures. Thus, an
increase in surface ozone may augment the fatality rates. A study in [108]
based on the Random Forest algorithm investigated the impact of air pollution,
rather than direct person-to-person contamination, on the spread of SARS-CoV-2
in Italy. Interestingly, compared to lifestyle or socio-economic factors, air
quality played the most significant role in pandemic diffusion and severity. A
5-10% increase in air pollution in Italy may cause an additional rise of
21-32% in the COVID-19 toll, having 4-14% more deaths. These findings were
achieved by analyzing epidemiological data on COVID-19 positivity, mortality,
and air quality index of 20 Italian regions and 99 Italian provinces from 2015
to 2019. Furthermore, about 70% of COVID-19 deaths nationwide might be due to
emissions from industries, farms, and road traffic.

There is a unidirectional causal relationship between pollution and economic
growth. In 25 major Indian cities, Mele and Magazzino [110] investigated the
relationships between economic growth, pollution emissions, and COVID-19
related deaths using time series analysis and ML based complex causality
algorithm (D2C). Time-series econometric analysis indicated that there is a
strong unidirectional correlation between economic growth and concentration of
${PM_{2.5}}$, ${CO_{2}}$, and ${NO_{2}}$. The ML algorithm confirmed a causal
relationship between the concentration of ${PM_{2.5}}$, ${CO_{2}}$,
${NO_{2}}$, and COVID-19 deaths. Thus, air pollutants have a direct
relationship with the COVID-19 pandemic-related death, with the largest
influence from $PM_{2.5}$. Prolonged exposure to $PM_{2.5}$ causes acute
health problems (e.g., asthma, cough, loss of cardiac or lung capacity), which
results in fatalities. The results are consistent with other studies conducted
in the US [127], where the researchers found that a 1 $\mu g/m^{3}$ increase
in $PM_{2.5}$ concentration causes an increase in the mortality rate by 15%.
Thus, exposure to $PM_{2.5}$ aggravates the symptoms of COVID-19 with
heightened risks of mortality.

The above discussion confirms that air pollution has a significant impact on
the diffusion of the COVID-19 pandemic. However, the reduction of air
pollutants during COVID-19 related lockdown has had a mitigating effect, by
substantially increasing air quality, which in turn had the effect of curbing
the severity of the pandemic.

###  IV-F AI and ML tools in air pollutants monitoring

Nowadays, ML techniques are used extensively for their computational benefits
and intelligence in ‘big-data’ analytics of air pollutants monitoring.
Supervised ML algorithms predict or classify data based on existing labeled
data (Table V). ML algorithms are routinely used to estimate the business-as-
usual concentration of criteria pollutants using large historical datasets. A
number of studies also use ML algorithms to discover the relationship between
air quality and COVID-19 related death, including all its nuances. Their
distinctive advantage rests with the capability to handle large datasets and
with their predictive reliability.

Researchers in [111] used Raspberry Pi 3b+ in the IoT stationary architecture
to build an air quality monitoring system. Their IoT-based model could be
exploited to get a deeper understanding of the influence of air pollution on
the pandemic. It can also be helpful to anticipate the increased
susceptibility of individuals due to exposure and eventually to predict new
outbreaks. Recently, Cole et al. [107] have developed a meteorological
normalization technique based on the random forest algorithm in combination
with the augmented synthetic control method to quantify the impact of COVID-19
lockdown on air pollution and public health in Wuhan, China. Investigating the
hourly data of four pollutants (${SO_{2}},{NO_{2}},CO,PM_{10}$) in 30 cities
of China between January 2013 and February 2020, the model reported a 63%
reduction (a drop of 24 $\mu g/m^{3}$) of ${NO_{2}}$ with respect to pre-
lockdown levels. Even though the study found no significant impact of lockdown
on ${SO_{2}}$ or CO, it indicates the reduction of ${NO_{2}}$ could have
prevented a total of 3,368 deaths in the City of Wuhan and 10,822 deaths in
the whole of P.R. China.

Mirri et al. [105] used eight different ML models to predict the possibility
of a resurgence (second wave) of the COVID-19 pandemic in all nine provinces
of Emilia-Romagna, Italy for the period of September–December 2020; Emilia-
Romagna was one of the most severely afflicted regions during the first phase
of the pandemic from February to April 2020. They trained the models with data
on COVID-19 confirmed cases from February to July 2020, and the daily
measurements of $PM_{2.5}$, $PM_{10}$ and $NO_{2}$ in the periods of
September–December 2017/2018/2019. The results from the models demonstrated
that the gradient boosting model performs better, with an accuracy of 90%, to
predict the possibility of a second wave of the pandemic. Conducting a
sensitivity analysis, the study commented that the use of personal protective
measures has a significant impact to downgrade the likelihood of a second wave
in the nine provinces of Emilia-Romagna.

In the light of the potential benefits of analysis on complex data structures
with high computational efficiency, many studies have used different ML
techniques to monitor air quality and explore the effects of air pollutants on
disease transmission. One important question arising here is that of the
motivation for using ML to predict air quality rather than statistical or
econometric models. The answer may lie in the fact that these studies are
investigating the effect of lockdown measures on air quality in the current
situation instead of the simple comparison between the current value and
historical values. Since air quality is significantly influenced by
meteorological factors, a simple statistical or econometric model may not
capture the true effect of lockdown on the concentration of air pollutants.
Thus, machine learning to estimate air quality considering all these factors
may provide a better understanding of the lockdown and confinement effects
[97].

TABLE V: Data and ML methods used in past studies on COVID-19 and air quality. Author |  Data and sources |  ML methods
---|---|---
[70] |  COVID-19 death cases from US Facts, Pollutants data (e.g., $PM_{2.5}$, benzene, formaldehyde, acetaldehyde, carbon tetrachloride) from Environmental Protection Agency and Centers for Disease Control and Prevention, weather (e.g., temperature, precipitation, sunlight and UV exposure), land cover, health status (e.g., disabled, obese, overweight) from Centers for Disease Control and Prevention, socioeconomics (e.g., health insurance, poverty, income) and commuting information (e.g., travel modes, time) from the US Census |  Geographical weighted RF (GW-RF)
[71] |  COVID-19 epidemiology data from [128] and New York Times COVID-19, Daily air traffic (people/day) from International Air Transport Association. |  Susceptible-exposed-infectious-recovered (SEIR) models, Bayesian Interference models
[83] |  Flight data from Bureau of Transportation Statistics, ground traffic data from NYC Open Data, air pollutant data (e.g., CO, $NO_{2}$, Ozone, and $SO_{2}$) from Aura Satellite (OMI instrument) and Environmental Protection Agency |  Support vector machine (SVM)
[91] |  $NO_{2}$, $PM_{2.5}$, and $O_{3}$ concentrations from the Secretary of the Environment of the Municipality of the Metropolitan District of Quito |  Parametric analysis
[96] |  Ground-based $PM_{2.5}$ concentration from central pollution control board (CPCB), satellite-derived MODIS Aerosol Optical Depth (AODs) data, and meteorological data (wind speed, temperature, rainfall, relative humidity, and mixing height) from Indian Meteorological Department. |  Artificial neural network (ANN)
[97] |  Meteorological data (e.g., temperature, precipitation, wind speed, wind direction, and air pressure), air quality data for the year 2014-2020, and lockdown data from the Austrian government |  Principal component analysis (PCA), random forest (RF)
[98] |  Eight meteorological parameters (e.g., surface wind components, surface temperature, relative humidity, cloud coverage, precipitation, pressure, and planetary boundary layer) from government agencies |  Extreme Gradient Boosting Decision Tree (XGBDT)
[93] |  Daily minimum and maximum temperature, average wind speed and direction, average relative humidity, daily cumulative precipitation, and $PM_{2.5}$ and $PM_{10}$ concentration from ARPA Lombardia, and time and seasonal variables |  XGBDT
[99] |  Meteorological data (e.g., temperature, pressure, wind speed, cloud cover, solar radiation, ultra-violate radiation) from ERA5 reanalysis dataset, and $NO_{2}$ data from Earth Sciences Department of the Barcelona Supercomputing Center. |  Gradient Boosting Decision Tree (GBDT)
[100] |  CO, $NO_{2}$, $O_{3}$, $SO_{2}$, $PM_{10}$, and $PM_{2.5}$ data from six monitoring stations between March and April |  Reduced-spaced Gaussian Process Regression and ANN
[101] |  $CO$, $NO_{2}$, $O_{3}$, $SO_{2}$, $NH_{3}$, $PM_{1}0$, $PM_{2.5}$, Toluene, benzene, and NH3 data from the Central Pollution Control Board and Ministry of Health and Family Welfare (MoHFW) |  Decision tree (DT), RF
[102] |  $PM_{2.5}$ data from monitoring station of the US Embassy, Dhaka, $SO_{2}$, $NO_{2}$, CO, and $O_{3}$ from AirNow, $NO_{2}$ measured by the Copernicus Sentinel-5 Precursor Tropospheric Monitoring Instrument |  Generalized additive models (GAMs), wavelet coherence, RF
[103] |  Daily COVID-19 cases and lockdown level from Statistical Portal of São Paulo State, and meteorological variables (e.g., relative humidity, maximum temperature, atmospheric pressure, wind speed, and global solar radiation), CO, $O_{3}$, $NO_{2}$, NO, $PM_{2.5}$, and $PM_{10}$ from Environmental Company of São Paulo State database (CETESB) |  ANN models (Multilayer Perceptron overview, Radial basis function, Extreme Learning Machines, Echo State Networks)
[104] |  The daily average of $PM_{2.5}$ and $PM_{10}$ from environmental monitoring stations located in the cities, COVID death, resuscitations, and hospitalization from the French National Public Health Agency |  ANN
[105] |  Italian Civil Protection, Regional Environmental Protection Agencies (ARPA) |  SVM, K-Nearest Neighbor (KNN), RF, GBDT, Classification and regression tree (CART), Multilayer perceptron (MLP), Ada boosting with decision tree (AdaBoost), Extra tree (ET)
[106] |  Nine observation sites in Hangzhou, China |  RF
[107] |  City-level hourly data of 4 pollutants from Qingyue Open Environmental Data Center, meteorological data (e.g., temperature, relative humidity, wind direction, wind speed, and air pressure) from “worldmet” R package |  RF, New augmented synthetic control method
[108] |  COVID-19 positivity, mortality, and total case count from Italian Civil Protection, air pollutants (i.e., $PM_{2.5}$, $PM_{10}$, $NO_{2}$, $SO_{2}$, CO, Benzene, and $O_{3}$) from Italian Ministry of Agriculture, Food and Forestry and Regional Environmental Protection Agency (ARPA), air pollution from Italian National Institute of Statistics |  RF
[109] |  Emission factors from different sources (open access and near-real-time measured activity data, proxy indicators and other available reports), stringency index from Oxford COVID-19 Government Response Tracker (OxCGRT) |  GBDT
[110] |  Pollutant ($CO_{2}$, $PM_{2.5}$, and $NO_{2}$) emission from Open Government Data (OGD) and World Bank, per capita GDP from Federal Reserve Bank of St. Louis (FRED) |  ML-based complex causality algorithm (D2C)
[111] |  Pollutants (e.g., $NO_{2}$, CO, and $O_{3}$) data were captured by MONICA (a cooperative air quality monitoring station) and transmitted via a Bluetooth serial interface to a Raspberry Pi Mod. 3 + based datasink with Raspbian OS |  Shallow neural network (SNN)
[112] |  Pollutants (e.g., $NO_{2}$, $O_{3}$, $PM_{2.5}$, and CO) data from China National Environmental Monitoring Center, meteorological data (e.g., wind direction and speed, temperature, relative humidity, and pressure) from NOAA Integrated Surface Database |  RF
[113] |  $NO_{2}$ data from operational Copernicus Sentinel 5 Precursor (S5P) TROPOMI, CAMS regional air quality models, European Centre for Medium-range Weather Forecasts (ECMWF) |  GBDT

##  V Conclusions, policy implications, and directions for future research

###  V-A Conclusions

The COVID-19 pandemic is one of the most devastating tragedies that humanity
as a whole has experienced in the past centuries. It has adversely affected
public health, social cohesion, health infrastructure, economic progress,
transportation systems, and environmental quality. A range of preventive
measures (e.g., lockdown and confinement measures, testing, vaccination),
personal protection actions (e.g., face covering, hand washing), and financial
assistance schemes (e.g., income support, debt relief) have been implemented
to control the pandemic and reduce associated threats. As another dimension,
the social distancing measures implemented nation-wide as a defensive response
affect travel patterns of people and thereby influence air quality.

A considerable volume of studies have been conducted to predict COVID-19
transmission rates, evaluate the impacts of the pandemic on mobility and air
quality, and assess the effectiveness of lockdown measures and the role of air
quality on COVID-19 diffusion (Tables I and III). As a departure from
pandemics that swept the world in previous decades, many of these studies have
used ML techniques to understand the complex relationships between them
(Tables II and V), leveraging the convergence of the widespread availability
of huge volumes of structured and unstructured data enabled by wireless and
mobile information technologies, and of ML techniques to analyze them with
little to no restriction on distributional properties of data. Considering the
distinctive ability of ML techniques to deal with multifaceted and wicked
problems, this study aimed at reviewing the burgeoning body of past research
that applied different ML tools to understand the intersecting relationships
between the COVID-19 pandemic, lockdown measures, human mobility, and urban
air quality.

Similar to other strands of studies (e.g., transportation, environment, and
public sentiment), a large body of literature is using ML techniques in
epidemiology research, which is a major breakthrough in medical history and
has made significant contributions in transforming public healthcare systems
[129, 130]. Many ML researchers have seized the opportunity to make their
analytics relevant to the pressing needs of this time due to the convergence
of data through cloud storage and data sharing via the interactive platform,
and powerful analytic tools [131]. ML is very effective and well suited to
handling notoriously wicked problems of epidemiology (i.e., multiscalarity,
non-linearity, feedback effects, and so on), and it has proved itself
worthwhile in previous research [132, 133, 134]. ML techniques predict
outcomes with a higher rate of accuracy and reveal the hidden patterns in the
data compared to traditional data processing systems. ML allows to integrate
multiple modeling approaches to handle complex and large data sets contrary to
conventional methods, which are more in line with the ”one size fits all”
principle. So, ML explicitly captures the context, whether spatial, social or
environmental, as well as contextual changes; thus, predictions are adjusted
accordingly, which is challenging to handle using traditional statistical and
econometric approaches. Considering the enormous demand and potentials, the
U.S. National Science Foundation (NSF) has initiated a series of
transdisciplinary conversations in the form of webinars at the intersection of
public health, behavioral and social sciences, smart technologies and data
analytics to catalyze ideas that can further the research and development at
the frontier of predictive intelligence for pandemic prevention [135].

After critically analyzing the past studies on the emergence and evolution of
the COVID-19 pandemic, this study observed the circumstances that amplify or
mitigate the pandemic situation. In particular, factors of urban form,
socioeconomic and physical conditions of the people, social cohesion, and
social distancing measures significantly influence human mobility and
stimulate the diffusion of the virus. Thus, social, physical, and
institutional structures and the tendency of the people to follow social
distancing measures sufficiently change travel patterns and determine the
severity of the pandemic in a city or larger national territory. Human
mobility and COVID-19 pandemic exhibit bidirectional relationships. During the
COVID-19 period, people tend to travel less and are more likely to use private
transportation for necessary travel purposes to mitigate coronavirus-related
health problems. This review study also found that COVID-19 related lockdown
measures significantly reduce the concentration of air pollutants and improve
air quality due to reduced energy consumption in transportation and
industries. This improved air quality also ameliorates the COVID-19 situation
by reducing respiratory disease in the population. As a core approach to
predictive analytics, ML is well positioned to bring newly learned inductive
knowledge to the practice of pandemic prevention and management, but also to
transform theories of pandemics through abductive inference.

###  V-B Policy implications

Several policy implications can be drawn from the above discussion, as stated
below.

  * •

AI and ML techniques can be used in future multiscalar and non-linear disease
modeling which can yield more accurate prediction results and formulate
relevant premises for diseases diagnostic and prescribing treatment measures
accurately [133].

  * •

Studies have found that COVID-19 related lockdown measures improved air
quality by restricting human activities and altering the travel patterns.
Thus, transportation policies (e.g., driving restriction on certain days or
portion of the day, congestion pricing, emission standards for vehicles) to
restrict auto driving, encourage public and active transportation, improve the
fuel efficiency of the vehicles, and alternate working provisions, could be
effective policy options to reduce air pollutants [136, 137, 138, 139].

  * •

Due to people’s concerns on travel safety and hygiene, recent initiatives
(e.g., dedicated lanes for bicycles and scooters), and changes in behaviors
(e.g., teleworking, online shopping, and deliveries), mobility providers are
re-evaluating the future of tomorrow’s mobility [140]. Policy makers should
take initiatives to promote micro-mobility (e.g., cycling, e-scooters) and
future transportation options (e.g., connected and autonomous vehicles,
electric vehicles, shared mobility) should consider safety and safety issues
to address the changes in consumer preferences.

  * •

Environmental policies (e.g., polluters pay, energy-efficient production,
promotion of renewable energy) to restrict anthropogenic activities could
reduce environmental pollution and respiratory illness in post-pandemic
situations [81, 141, 139, 142]. A comprehensive plan comprising of land use
distribution, transportation, and environmental policies should be developed
and continued to execute during and after the crisis, which are essential to
achieve desired air quality standard [139].

  * •

Reducing people’s exposure to air pollutants through proper urban planning
could be an effective policy option. For example, developing residential
areas, healthcare facilities, educational institutions away from major sources
of pollution (e.g., highway, industrial areas) are more likely to reduce
exposure of people to the air pollutants [137]. Moreover, city authorities
could establish advanced pollutants monitoring stations and air filters
particularly in the highly polluted areas to limit the emissions at the
sources, decrease exposure to toxic air pollutants and protect public health
[137, 143, 144].

  * •

Outbreak and transmission of pathogenic diseases such as COVID-19 could be
attributed to man-made and climate change-induced habitat alterations and
interactions [138, 139]. Thus, governments and policy makers across the globe
should incorporate pandemic risk management strategies in climate action plans
and adaptation schemes.

  * •

Considering the massive disruption of supply chains and the downfall of
tourism sectors, policy makers should take appropriate initiatives locally and
internationally to achieve a sustainable and resilient logistic management
system and tourism industry [138].

###  V-C Limitations of past studies and directions for future research

Despite the well-developed models we have on hand, convincing findings, and
significant contributions to the literature in COVID-19 related mobility and
air quality studies, the overall achievements of past studies are somewhat
subdued by some recognizable reasons and hence there is scope for future
research. We state some areas below.

First, some studies used a limited number of variables to predict coronavirus
cases and deaths, which may overlook other important factors that could
influence disease transmission [54, 60, 63]. Thus, future research should
combine data from other sources such as social media, mass media, people’s
contacts with a special call center for COVID-19, mobile contract tracing,
environmental and climate factors, and screening registries [54]. Moreover,
some researchers predicted COVID-19 cases for a limited period (i.e., only for
the next 12 days), which seems insufficient to assess the performance of a
model as a predictive tool [83].

Second, some studies only investigated the impact of mobility patterns on the
infection rate [45, 63, 64]. However, other factors (e.g., late government
action, international travel, social structure, people’s socioeconomic
factors) may influence coronavirus cases and deaths. Thus, for accurate and
meaningful prediction, these factors should be included in the models.
Moreover, these studies mostly used mobility data (i.e., mobile device
location based on GPS) from Google, Apple, and SafeGraph [53, 62, 61, 59, 57,
55, 7]. These mobility reports provide inadequate information about people’s
mobility since many of them may disable location settings in their mobile
devices or may not have Android/iOS supported mobile. Moreover, some countries
may not have reliable internet access and do not support Google (e.g., Iran,
China), which may affect the quality and completeness of the data
infrastructure. Researchers in [52] suggested exploring additional information
(e.g., vehicle miles traveled, number of miles traveled per person, trips per
person, travel mode share) may explicitly consider those uncertainties to
develop a more trusted model.

Third, researchers are unable to determine an association between COVID-19
transmission and population characteristics in some high-incidence areas due
to the difficulty to identify whether the cases have a local or international
origin [50]. Moreover, the true number of incidence is unknown due to
asymptomatic individuals, differences in testing and reporting, and
misdiagnosis, which make the modeling very challenging. Some studies did not
report the accuracy level, which call into question the validity of the model
[49]. Thus, studies should incorporate some baselines (e.g., traditional
fitting models) and other metrics for model evaluation (e.g., RMSE, summary
statistics across bins, confusion matrix, cross-validation, etc.) [55].

Fourth, some studies did not consider the impacts of seasonal variations on
air quality [103, 100, 91, 101, 102, 104]. It is evident that air quality
improves gradually from winter to summer. Therefore, there is conventional
seasonal air quality. It is difficult to establish the effect of lockdown
measures without considering the seasonal change. Thus, it is recommended to
include seasonal variables in models to estimate the effects of lockdown
measures on air quality to avoid any spurious and misleading relationships.

Fifth, the impact of time traveling in shared mobility for coronavirus
transmission is rarely studied in the literature. However, long duration
travels in shared and crowded modes (e.g., air, bus, train, etc.) may transmit
the contagious disease easily. Hence, this is a potential direction for future
research.

Sixth, despite huge data sharing during the COVID-19 pandemic, the application
of AI and ML tools in epidemiology and other areas is still in its infancy due
to limited access to reliable data infrastructure (i.e., platform, storage,
network, collaboration). However, an efficient data infrastructure increases
data acquisition, business collaborations, and operations by integrating data
at various granularities and from diverse sources including unstructured data
like social media data. Thus, a priority should be given to developing
trustworthy data infrastructure to make data available to researchers for
data-driven decision making.

Seventh, a hybrid ML model (i.e., integration of multiple ML techniques) has
enormous analytical ability to handle complex data and solve real-world
problems such as COVID-19 compared to single ML techniques and simulation
models. Thus, future studies should be directed to implement hybrid ML
techniques for efficient and better performing models for real-world events
[145, 146].

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