###### Abstract

Aim of this paper is the description of a new tool to support institutions in
the implementation of targeted countermeasures, based on quantitative and
multi-scale elements, for the fight and prevention of emergencies, such as the
current Covid-19 pandemic. The tool is a centralized system (web application),
single multi-user platform, which relies on Artificial Intelligence (AI)
algorithms for the processing of heterogeneous data, and which can produce an
output level of risk. The model includes a specific neural network which will
be first trained to learn the correlation between selected inputs, related to
the case of interest: environmental variables (chemical-physical, such as
meteorological), human activity (such as traffic and crowding), level of
pollution (in particular the concentration of particulate matter), and
epidemiological variables related to the evolution of the contagion. The tool
realized in the first phase of the project will serve later both as a decision
support system (DSS) with predictive capacity, when fed by the actual measured
data, and as a simulation bench performing the tuning of certain input values,
to identify which of them lead to a decrease in the degree of risk. In this
way, the authors aim to design different scenarios to compare different
restrictive strategies and the actual expected benefits, to adopt measures
sized to the actual need, and adapted to the specific areas of analysis,
useful to safeguard human health, but also the economic and social impact of
the choices.

###### keywords:

Covid Counteractions, risk levels, Artificial Intelligence (AI), Satellite
Remote Sensing, Sensor Networks, Pollutants, Macroanalysis, Microanalysis, Air
quality, Environmental chemistry, Anthropogenic activities

xx 1 5 Received: date; Accepted: date; Published: date AIRSENSE-TO-ACT: A
Concept Paper for COVID-19 Countermeasures based on Artificial Intelligence
algorithms and multi-sources Data Processing Alessandro Sebastianelli 1, ID ,
Francesco Mauro1,, Gianluca Di Cosmo1,, Fabrizio Passarini 2 ID , Marco
Carminati 3 ID , Silvia Liberata Ullo 1 ID Alessandro Sebastianelli, Francesco
Mauro, Gianluca Di Cosmo, Silvia Liberata Ullo, Fabrizio Passarini, Marco
Carminati Correspondence<EMAIL_ADDRESS>
##  1 Introduction

In this concept paper authors aim to focus on the challenges posed by the
Covid-19 pandemic and present a multidisciplinary and quantitative approach to
respond to the sanitary and economical crisis. The proposed model is named
AIRSENSE-TO-ACT, and is the description of a project submitted to the 2020
FISR Call of MIUR (Italian Ministry of University and Research), issued to
collect solutions, related to the diffusion of the Covid-19 pandemic, able to
contain its effects and offering a novel way for the management of the
reorganization of activities and processes.

The model is based on the fusion of heterogeneous data coming from different
sensors: on board of satellites and/or positioned on ground platforms, both
mobile and fixed, in addition with other public data extracted from databases,
all jointly processed through the application of Machine Learning (ML)
algorithms, and the employment and comparison of macro- and micro-systems of
analysis.

Some data can be indeed extracted from public databases (i.e. epidemiological
information, number of infected, etc.), or retrieved from satellite images
freely downloadable, such as those of the European Space Agency (ESA)
Copernicus mission, since public databases have further increased in number
and type of data, following the COVID-19 emergency.

Yet, to improve the model and the analyses carried out, it is foreseen the
possibility to carry out data collection campaigns through ground-based
networked sensors for instance, to validate the developed model, in particular
in the Italian areas where the emergency has shown more critical issues, such
as in the Po Valley, and specifically in the Emilia-Romagna and Lombardy
regions. Local and punctual measures with better spatial and temporal
resolution will increase the effectiveness of the fight against the spread of
the contagion, allowing to focus the analysis from macro-areas covered by
satellite, to micro-areas in order to increase the "granularity" of the data
and to reduce the reaction time on the decisions to be taken.

It is necessary to underline that a crucial aspect will concern the creation
of the datasets with which the artificial network underlying the DSS needs to
be trained, since based on previous experience, this activity may take up to
3/4 months.

However, it is also worth pointing out that, although developed to combat the
COVID-19 pandemic in Italy, this model can be applied to other emergency
situations, where air quality has health implications, above all in other
countries with similar economic-infrastructural characteristics.

The proposal falls mainly within the scope of risk prevention, developing
solutions to counteract and contain the effects of COVID-19 and any future
pandemics. However, thanks to the versatility of the proposed tool, it is also
of immediate use for the response to other emergencies, to develop tailored
solutions and for the management of the organization of activities and
processes, relating to the phase of overcoming the phenomenon in safety
conditions.

The idea was born because the actual measures of lockdown taken by Governments
and local Institutions (Regions and Municipalities) are always a-posteriori
decisions, where increasing levels of lockdown are activated, based on the
number of infected, hospitalized and dead people, up to a generalized
lockdown, like the one imposed in Italy from the beginning of March until
almost the end of June, and which seems to come in the next weeks of October
again in Italy. These measures imitated what was decided in Wuhan area, in
China, to contain COVID-19, having proved the positive impact of lockdown on
the number of infections Lau et al. (2020), Wang et al. (2020).

Yet, to protect human health, but at the same time safeguard socio-economic
aspects, these methods of intervention are not appropriate for their negative
implications Nicola et al. (2020). The type of interventions must learn from
the past to take a priori decisions, which have a minimal impact on commercial
activities but are effective in limiting the spread of the virus. An
interesting analysis of responses to COVID, carried out in Indonesia during
the first months of the 2020, is presented in Djalante et al. (2020), where
five recommendations are given to face efficiently the pandemic situation.
Micro actions are highlighted and interventions step-by-step analysed. The
only limit is related to the methodology, based on traditional tools.

Artificial Intelligence algorithms instead can lend a hand in this sense,
managing to capture the hidden interactions between data, and providing the
possibility of their corresponding use for a micro- and macro-analysis of the
phenomenon, allowing localized and targeted interventions.

As already mentioned above, the proposed model aims to combine satellite data,
and data acquired through ground platforms, both mobile and fixed, related to
the concentration of some pollutants such as NOx, PM10, PM2.5, meteorological
data, air mass displacement, chemical-physical parameters such as temperature
and humidity, with other reference data such as population mobility,
epidemiological data (number of infected), number of places still available in
intensive care (global values or per-hospitalization points, distributed
throughout the regions, nationally, etc.), the concentration of residents per
$km^{2}$, the degree of implemented lockdown, and so on. Several studies
analyze the impact and correlation of the mentioned factors on COVID-19 Nicola
et al. (2020); Djalante et al. (2020); Tosepu et al. (2020); Lau et al.
(2020); Bashir et al. (2020); Kraemer et al. (2020); Bashir et al. (2020); Kim
(2020).

The idea is to create a Decision Support System (DSS) able to produce as
output the level of risk, based on the correlation between the selected data.
The choice to base the proposed model on Artificial Intelligence algorithms
stems from the enormous amount of data which must be taken into account, and
the need to analyze the correlations, sometimes hidden, between this
information. In fact, completely different causality structures emerge from
the literature when the various factors are considered in combination. For
example, the rise in temperature seems to lead to a reduction in the diffusion
of COVID-19, but it depends also on the humidity values, since it has been
found that high temperature values with high humidity values do not stop the
spread of COVID, but on the contrary facilitate it. In Xu et al. (2020) the
Covid Risk Weather (CRW) parameter is introduced, an index to evaluate the
relative COVID-19 risk due both to weather and air pollution. The CRW can be
used to compare the relative changes in reproductive number for the disease
due to the weather factors (average and diurnal temperature, ultraviolet (UV)
index, humidity, pressure, precipitation) and air pollutants (SO2 and Ozone).
The authors highlighted in Xu et al. (2020) that warmer temperature and
moderate outdoor UV exposure may offer a modest reduction in reproductive
number. However, UV exposure can not fully contain the transmission of
COVID-19. If on one hand both high temperature and solar radiation are able to
speed up the inactivation rate, on the other high relative humidity may
promote the diffusion rate Xu et al. (2020); Després et al. (2012). Still from
the literature, in the diffusion of respiratory diseases, the interaction
between multiple elements: pollution, high population density, overcrowding,
is analyzed and recent studies have shown a correlation between high
concentrations of fine particulate matter and COVID-19 diffusion Li et al.
(2020); Jordan et al. (2020). However, the subject is still much debated, and
further multidisciplinary investigations are in progress. Some additional
considerations will be given ahead in this work.

Based on all the above considerations, the proposed approach aims to develop a
novel model for the cooperative fusion of extremely heterogeneous data, both
in terms of nature and source (epidemiological data, environmental data, and
data related to the human activities), and in terms of spatial (km to m) and
temporal (days to seconds) sampling, to capture the extremely complex
dynamics, difficult to identify with other traditional methodological tools.
As highlighted before, the algorithms of AI are able to extract the features
related to the hidden correlation between several elements, such as for
instance the concentrations of atmospheric particulate matter, the
meteorological trends and the virus spreading, to estimate the level of risk.

The designed model is expected to operate on two levels of analysis:

  * •

Macro analysis: mainly through satellites (not limited to) with the data
collection on wide areas

  * •

Micro analysis: through the use of fixed or dynamic networks for local-focus
data collection

and initially it has been developed with particular attention to the most
critical areas of Italy.

To the best of our knowledge, this proposal represents a significant progress
compared to the state of the art. The only studies in progress, related to
similar issues, concern the creation of the ESA RACE dashboard ESA ()
(European Space Agency), published on the 5th of June 2020, which allows the
use of satellite data to support the monitoring of commercial and productive
activities, and on the other hand, the development of national and
international projects such as PULVIRUS Ambiente , EpiCovAir and RESCOP res ,
on the study of the correlation between suspended atmospheric particulate
matter concentration and COVID-19. Our proposal integrates the above, and goes
beyond their aims, by proposing a Decision Support System capable of producing
the level of risk, but also useful to be used for simulations, for tuning the
input values to establish which inputs to vary and how in order to obtain a
lower level of risk.

Based on recent events and the studies of the researchers working on COVID-19
diffusion after that the contagion took place, it came out that there is a
time lag between the contagion moment and the manifestation of COVID-19
symptoms in the infected person (and its positive test results), a period
evaluated between 14 and 21 days. Similarly, the closure of road traffic and
the lockdown restrictions may have an effect on the reduction of pollution,
but this happens only after some days, and the elapsed time is different in
different areas. Situation is worse particularly in some parts of Italy, such
as the Po Valley, which suffers from weather and orographic conditions that
make it very problematic to shuffle air masses. This latter point has
intrigued us, and some simulations and data analysis have been carried out to
identify the number of days which best represents the elapsed time in this
case for specific areas and critical regions. The authors started working on
two case studies, that will be presented ahead in this work, where trends of
NO2 have been analyzed in the Lombardy region (Italy) and Hubei region
(China), where Wuhan is located, after the activation of the lockdown
measures. Sentinel-5P data have been used to measure some pollutants and plot
the NO2 values over time, and some analysis on the corresponding COVID-19
daily evolution has also been carried out, with considerations on the
correlation between NO2 and COVID-19 evolution.

To take into consideration the presence of a delay in the cause-effect
phenomena, the type of the neural network necessary for our model should be
able to take into account time series deferred in time. Therefore, a
particular network architecture called Long Short-Term Memory (LSTM) has been
chosen for the creation of the DSS, as previously described. The final goal is
using historical data series to predict not only the level of risk at time T,
but also in other subsequent instants, so that after being trained the network
can also be used as a transfer function to calculate the output (the expected
risk level) by using new input data.

The complementarity of the three research groups, which will cover satellite
monitoring and the development of data fusion and crowd monitoring models
(Univesity of Sannio), miniaturization and networking of high-resolution
sensors also on drones (Politecnico di Milano) and biochemical, health and
environmental issues (Univesity of Bologna), demonstrates the strong
interdisciplinary nature of the project and represents a guarantee of success
for the creation, and subsequent validation, of the proposed system.

##  2 The Involved Factors in the DSS Design

Each country and its Government has a Department of Civil Protection managing
the situations in case hazardous events happen. For instance, in Italy the
Department of Civil Protection Department of Civil Protection in Italy
activates specific interventions when several risks, such as seismic,
volcanic, health, environmental, fire, and other, occur. In the specific case
of COVID-19 pandemic, the problem and its solutions have involved three main
acting factors:

  * •

Pollution$\&$Population: pollution, overpopulation, anomalies in climate
conditions can lead to the disease

  * •

Spread of diseases: the spread of diseases involves issues related to
population and some geo-physical changes, but it can be mitigated by taking
correct decisions

  * •

Decisions taken: decisions are taken by humans based on previous experience,
but to take correct decisions the right information and robust decision
support systems are needed

Usually decisions are taken by humans, based on past experience and selected
data, but to take a correct decision an objective and scientific method is
needed. We have learned that under Covid pandemic, behaviors and/or needs such
as gatherings, mobility, sharing of work spaces, etc. can heavily affect the
number of infected people, and all the countries to reduce the pandemic have
activated a posteriori interventions, based on the number of infected and
dead, through several attempts of consecutive lockdowns, each time more
restrictive, but not always effective, or in any case with dramatic
consequences on the social and economic framework.
Hence the idea of making a tool able to support Decision Makers when multiple
parameters are involved, realizing an Artificial Intelligence (AI) Decision
Support System (DSS), receiving information from multiple sources, and able to
produce as output the degree of risk. The proposed system aims to provide an a
priori tool to take the right decisions, and the general scheme is represented
in the Fig. 1, where human activity contribution and economic operators’
activity are highlighted, bot interacting under factors such as climate
conditions, pollution density, mobility data, etc., resulting in the number of
infected and other effects, whose mutual interactions can be captured through
AI-based paradigms.

Figure 1: Data sources

Clearly, such a system to be effective must relies on the availability of data
almost on real time, or with a low revisit time (i.e. Sentinel-5P and others),
in order to help in realizing a continuous monitoring system, able to produce
the right alert to manage jointly multiple risks such as in the case
discussed, sanitary, environmental, and economic.

##  3 Proposed architecture

The proposed paradigm is based on a Long Short-Term Memory (LSTM) network,
which allows the model to learn the temporal features from the training data.
This type of network was invented to solve the problems of vanishing and
explosion gradient of which the Recurrent Neural Networks (RNNs), a precursor
of LSTMs, suffer Yan .

Figure 2: Block diagram of the adopted solution

The general block diagram of the adopted solution is presented in the Fig. 2,
while the elementary building block of the LSTM network is shown in the Fig.
3.

Figure 3: Elementary building block of the LSTM network

As it can be seen from the Fig.3, each elementary building block of the LSTM
network receives three inputs: the input at the current time step, the output
from the previous unit and the “memory” of the previous unit. Each single
building block of the LSTM network makes a decision knowing the current input,
previous output and previous memory, and it generates a new output and updates
its memory.

The functioning of the proposed model can be splitted, as commonly it happens
for neural networks, into two main phases: the training and the prediction.
During the training phase the model is fed with input data and output data,
both contained into the training set. That means that the model will be
trained with a Supervised Learning approach. During the supervised learning,
the model needs both input and output data. Defined $X\in R^{n}$ a vector of
inputs and $Y\in R^{m}$ a vector of outputs, then the model has to be trained
in order to find a correlation between X and Y, as shown by the equation 1.

$F\rightarrow Y=F(X)$ (1)

For the model under analysis, we suppose that the input is a matrix $X\in
R^{n,m}$, where $n$ represents the number of the different data sources and
$m$ represents the size of the acquisitions in the time dimension. The same
considerations can be done on the output, indeed $Y\in R^{p,q}$ where $p$
represents the dimensionality of the output in a fixed instant of time and $q$
represents the time dimension of the output. In other words, both the input
and the output are matrices, the inputs will contain sensed data for a fixed
time interval and the output will contains the prediction of the model for a
fixed time interval.

The training process can be done with the well known Back Propagation and
Gradient Descent techniques Hecht-Nielsen (1992); LeCun et al. (1990); Ruder
(2016). In few words, for each training input, the model is updated by using
the error and its derivative. The error is calculated trough an a priori
defined Loss Function. The model will be trained in order to minimize this
error. From this last statement, it can be better understood why both inputs
and outputs are required in the training phase. For example, given a model $F$
and a set of data (X, Y) with $X\in R^{n,m}$ and $Y\in R^{p,q}$, during the
training, for simplicity, a sample of the dataset is selected. Defined $X_{i}$
and $Y_{i}$ the sample, the training phase can be roughly represented by the
equations:

$y_{pred}=F(X_{i})$ (2)

$e=loss\\_function(y_{pred},Y_{i})$ (3)

$\dot{e}=\partial_{X}loss\\_function(y_{pred},Y_{i})$ (4)

Using the error and the the derivative of the error, respectively given by
equations 3 and 4, the model weights are updated, until reaching the minimum.
After the training phase, the model should be able to generalize, and given
new inputs it can generate the new outputs, using the internal state $F$.

The final output of the model will be a time series of risk maps, as shown in
the Fig. 2, with a resolution that can vary from low resolution intended as
risk for a country to high resolution intended as risk for a city, or a
smaller area. An example of a single risk map at low resolution is proposed in
Fig. 4.

Figure 4: Example of risk map at low resolution

##  4 State of the work

As previously discussed, our proposal was born in response to a MIUR call on
COVID and aims to create a multiscale analysis system based on AI algorithms,
to provide a priori tool to decision-makers. This system is based on the
combination of multi-source data with measurements obtained from fixed and
mobile network sensors, from satellite information and proximity surveys, if
necessary. Such a proposal required the integration of multi-disciplinary
skills ranging from the development of sensor networks and satellite data
processing to particle detectors, biochemical analysis of particulate matter,
innovative strategies in the environmental field, and sophisticated
computational technologies for treatment, analysis, and interpretation of data
using Big Data technologies. This paper intends to be a "Concept" paper, and
much of the work must still be done, at least in reference to the realization
of the neural network and to the creation of all necessary dataset. Yet, some
progress has been already done, and in the next sections the idea is to
present the state of the work and give useful considerations and reasoning
behind it.

##  5 Measuring pollutants, air conditions and pandemic data

Since the core of the idea involves the use of pollutants measurements and the
infection data, beyond the involvement of other information, as already
highlighted, the authors first selected a list of possible pollutants, such as
nitrogen dioxide (NO2), sulphur dioxide (SO2), formaldehyde (HCHO), ozone
(O3), which can be retrieved through the use of Sentinel-5P, and other data
(for instance, PM2.5, PM10, etc.) which can be retrieved by using other
sources, at different multi-scale levels.

The purpose of the measurement of pollutants is twofold. As it will be
discussed ahead, some pollutants favor the transmission of the respiratory
diseases and therefore by measuring them and feeding the model with their
values, a contribution to the final risk level is obtained. In addition, some
pollutants give us information on the degree of lockdown. Again, the model is
able to trace the goodness of the lockdown by using as input certain types of
pollutants, and to capture the inter-relationships among the data. What is
important is to understand that the analysis wants to highlight two different
levels of vision: a macro high-level vision (i.e. through the use of
satellites, for instance, or data at a wide geographical level, national,
etc.), and a micro low-level vision where local understanding of the
phenomenon is carried out. The proposed tool based on AI and the specific
neural network can be used at each level, once trained, and give important
insights. An interesting analysis has been done in Lary et al. (2016), which
discusses how ML algorithms can help characterizing airborne particulates for
applications related to remote sensing.
An example of data retrieved both through ground and satellite platforms is
shown in Fig. 5 and Fig. 6, where an air quality map at European level is
presented (retrieved through the European Environmental Agency website
European Environment Agency website ) together with some pollutants’ levels
obtained through the Sentinel-5P satellite, while regarding the COVID-19, the
use of the public Johns Hopkins Dashboard (see Fig. 7) has been proposed, that
allows to download the contagion data organized per state, region, cities, and
with much more information, such as the daily new cases, daily new deaths and
so on, in all the world COVID-19 Johns Hopkins Dashboard .

Figure 5: Air quality map retrieved from the
European Environment Agency website European Environment Agency website
Figure 6: Sample of Sentinel-5P pollution data Figure 7: COVID-19 Johns
Hopkins Dashboard COVID-19 Johns Hopkins Dashboard .

###  5.1 Characterizing airborne particulates

About the relationship between pollution and COVID diffusion, there are
several schools of thought, and among these, those who consider the causal
relationship between pollution and COVID confirmed, therefore believing that
it plausible that the coronavirus can be transported in the air and the
contagion may happen also at a less than 2 meter distance. Other schools of
thought believe that the respiratory system is already put in great difficulty
by pollution in some areas, so when COVID arrives it is more likely to affect
in a severe way the population and kill it. Moreover, there are two sub-
classes of thought: those who believe that there can be a chronic factor (i.e.
those who are used to being in polluted air are more exposed to serious
effects of the virus), and those who instead think that the problem is
contextual (i.e. during strong episodes of pollution, the organism is acutely
exposed to severe stress, which makes it more vulnerable to the virus).

At the moment none of these hypotheses has been incontrovertibly demonstrated,
so they could still be all valid, and therefore various factors could be
responsible for the spread of the infection.

In any case, we considered very important to analyze the NOx presence in the
air, and we are following the idea that particles can carry on the virus,
stabilizing it over time, and spreading it in space. For this reason, some
work has been done to find a correlation between the virus and the NOx, since
there is a virus/dust correlation (the dust may cause the virus transport),
and NOx is part of the elements captured and transported by the dust.

###  5.2 Particulate Matter (PM)-virus correlation

More than 240 scientists recently signed a Commentary, appealing to the
medical community and highlighting the need of dealing with the airborne
transmission of SARS-CoV-2 Morawska and Milton (2020). Many researches have
proven that the previously indicated “safe distance” of 6 feet cannot be
considered sufficient, since different way of diffusion can occur, in indoor
and outdoor environments Setti et al. (2020). Since the beginning of the
COVID-19 pandemic, several studies have been carried out to investigate the
reasons of the uneven distribution of infections and fatalities within the
different Countries, and positive correlations with air pollution,
particularly with suspended fine particles, have been found Frontera et al.
(2020); Setti et al. (2020); Magazzino et al. (2020); Yongjian et al. (2020);
Sanità di Toppi et al. (2020). Some researchers explain these results
considering acute and chronic effects towards the respiratory system, that
could make it more susceptible to pathogen infection, while others suggest
that different biotic and abiotic factors could be inhaled adhering to the
already suspended fine particles Ma et al. (2020).
Even though the modes of COVID-19 transmission are still under discussion Al
Huraimel et al. (2020); Farhangrazi et al. (2020); Tung et al. (2020), the
possibility of considering the presence of SARS-CoV-2 RNA on PM10 in outdoor
environments has also been suggested Setti et al. (2020). Many previous
studies already demonstrated that different varieties of microorganisms are
present on the suspended particulate Cao et al. (2014), and recently early
warnings were given about the possibility that they could play a role of
carrier for the coronavirus Qu et al. (2020), supported by the finding of
SARS-CoV-2 or viral nucleic acid on particulate samples in outdoor
environments Liu et al. (2020), Setti et al. (2020).
According to the cited researches, both PM10 and PM2.5 could be relevant in
improving viral infectivity. However, it has been hypothesised that the effect
is not linear at all conditions, but that the prolonged high concentration of
fine particulate (for instance, more than the daily limit of 50 $\mu$g/m3 of
PM10) could trigger a ’boost’ effect on the spread of virus Setti et al.
(2020).

###  5.3 Wireless Sensors Networks for PM Mapping

Given the above considerations, and by taking into account that not all the
pollutants can be recovered through a satellite analysis, and that however
different levels of investigations may become necessary, it is important to
discuss the characteristics that the sensor networks must hold in order to
guarantee the collection of the pollutant values in a way that is useful for
the intended purposes.

In regards to the sensors used at the ground level, the main challenge, and a
significant element of innovation, concerns exactly the identification of dust
sensors, in particular for PM2.5, of a certain cost, consumption, and
footprint for a widespread installation in both fixed and mobile (for example
with drones) wireless networks, to investigate geographical areas of
particular interest, while maintaining peculiar characteristics such as
selectivity and sensitivity in the measurement.
Traditionally, PM is measured by means of the laser scattering technique.
Particles are hydro- dynamically focused to flow in a single stream on which a
laser beam is focused. The presence and size of each single particle can be
determined from the intensity of the scattered light pulse, assuming a
spherical shape and average optical properties. The granulometric distribution
is thus obtained with reasonable accuracy, and the smallest detectable
diameter is diffractionl-imited, in the order of the light wavelength, i.e.
hundreds of nm. The laser scattering technique represents the state of the art
for particles in the 0.3-10 $\mu$m size range. However, due to their cost and
bulkiness these instruments are not suitable for massive deployment in the
environment. They are typically installed in a few fixed monitoring stations
controlled by local environmental protection agencies.

Given the relevance of air pollution for human health and the need for better
spatio-temporal resolution in mapping complex phenomena, such as dust
generation, concentration and transport, several efforts were carried out in
the last decade, to develop compact and affordable devices for measuring PM in
a distributed, pervasive way. Recent implementations of wireless sensors
networks at city scale have demonstrated the feasibility of this paradigm
Johnston et al. (2019), and specific technologies have been leveraged to
address several challenges, in particular to preserve the environment and
human health, spanning from monitoring air, water Carminati et al. (2020) and
the surrounding environment to control natural disasters or preserving
landscape resources Carminati et al. (2019).

The employment of miniaturized devices has spread out, with different
characteristics, mainly grouped into two classes. Micro-machined silicon-based
sensors represent the ultimate degree of miniaturization and leverage micro-
fabrication capabilities to enhance the detection sensitivity Carminati et al.
(2017). Two main approaches have been proposed for solid-state detection: the
use of mechanical resonance in oscillating micro- and nano-weighting scales
and high-resolution capacitance measurements of the single-particle impedance
on chip Ciccarella et al. (2016). Despite very promising preliminary results
and potential for nanoparticles detection as well as ubiquitous integration in
handheld devices, such as smartphones, thanks to their millimetric size, they
appear still far from a commercial maturity. A critical aspect, in addition to
clogging, cleaning, lifetime and power dissipation, when shrinking down the
sensor size, remains the active fluidics necessary to capture and collect dust
particles in the chip.

Another, more consolidated class of PM sensors is that of low-cost optical
sensors, named Optical Particle Counter (OPC). They represent an evolution of
smoke detectors in which a photodetector measures the amount of scattered
light from the dust when illuminated by a LED. Several sensors of this class
are available in the market from companies such as Alphasense, Honeywell,
Plantower, Sharp and Shinyei. The latters are very compact (credit-card sized,
Fig. 8, (a) and (b)) and consolidated, enabling new versatile scenarios (Fig.
8, (c)) in pervasive monitoring of dust concentration, also capable of coping
with emergency situations such as the acute phases of pandemics.

Different works have compared their performance with reference
instrumentation, both in the lab Austin et al. (2015); Wang et al. (2015) and
in the field Johnson et al. (2018), by finding good agreements. In the
majority of conditions, they can quantify the concentration of particles with
a size range similar to that of laser scattering, with a full scale of about
1000 $\mu g/cm^{3}$, and a sensitivity of a few tens of $\mu g/cm^{3}$, well
matched with the regulatory limit of 50 $\mu g/cm^{3}$. Furthermore, the
response time is in the orders of seconds, fast enough to capture the dynamics
of human and air transport. Thus, they are suitable for the purpose of this
project, enabling the deployment of thousands of sensing nodes capable of
quickly detecting exceeding of PM10 and PM2.5 concentration limits. Indeed,
Artificial Intelligence can leverage the high level of redundancy and overlap
in spatial mapping to compensate for the intrinsic limits of this class of
devices.

Figure 8: Examples of low-cost credit-card sized PM sensors from Sharp (a) and
Shinyei (b) enabling new versatile scenarios (c) in pervasive monitoring of
dust concentration, also capable of coping with emergency situations such as
the acute phases of pandemics.

###  5.4 Case studies: correlation between NO2 data and COVID-19 related data

As highlighted before, it is extremely important to change the level of
analysis, from a macro to a micro-level, to better understand and monitor the
evolution of the phenomenon, and to get the right input values for the DSS in
such a way that the initial diffusion of the virus can be detected and stopped
with targeted levels of lockdown.

In a first part of the analysis, a study has been carried out to evaluate the
correlation between one of the pollutant, the NO2, and the COVID-19. The data
relating to the NO2 concentration have been acquired through the Google Earth
Engine (GEE), recorded through the use of the Sentinel-5P satellite,
processed, averaged and plotted by using Python scripts. In the Fig. 9 and
Fig. 10 the concentration of NO2 is shown for Italy and China respectively,
over two similar periods. It is possible to see that in the worst period of
pandemic for both countries, the highest concentration of NO2 laid in the
regions where the COVID-19 had its highest values.

Figure 9: $NO_{2}$ concentration in Italy (from Jan. 1st to Jan. 5th 2020)
Figure 10: $NO_{2}$ concentration in China (from Jan. 1st to Jan: 8th 2020)

For this reason, rather than analyzing the data relating to entire nations, we
have chosen to focus on the regions of Italy and China where the spread of the
virus has been most rapid and emblematic (Lombardy and the Wuhan area).
Sentinel-5P covers wide areas, therefore, by starting from the national
concentrations of nitrogen dioxide, a subset to obtain the concentrations
related to the chosen case studies has been created. Some statistics on the
subset have been calculated, including the maximum, the minimum, the standard
deviation, for instance, and maximum values of the pollutant have been
collected in the following Table 1 and Table 2 for further discussion. In the
two tables, to manage the absence of values in Sentinel-5P data, the average
values of concentrations for the NO2 over five days have been calculated, by
starting from January 1rst until June 30th, 2020.

The correlation between the peaks of the average NO2 concentration and the
number of new COVID-19 positives has been analyzed. To match the satellite and
epidemiological data, it was necessary to make an average over 5 days for the
number of new infections. A scatter plot was constructed using these data,
positioning the number of infected people on the abscissa axis and the
$NO_{2}$ concentration on the ordinates.

In the Fig. 11 and Fig. 13, the scatter plots present a time sequence of data,
where the time is defined by the number near the dot. This representation
helps to graphically catch a correlation between NO2 and COVID-19 and also to
identify emblematic cases like the Wuhan ones. In fact, in this latter case
the trend, at a certain moment, starts oscillating. This phenomenon can be
better explained by analyzing the Fig. 12 and Fig. 14, where it is evident
that for the Lombardy region both the NO2 and COVID-19 have a decreasing trend
in time (from a high number of infected and a high concentration of pollutant
to a low number of infected and a low concentration of pollutant), while for
the Wuhan region the situation is quite different and unexpected, since after
a certain period the NO2 starts increasing again while the new infections are
stable and close to a very low value.

To take into account the delay between the instant when infection occurs and
the actual evidence of the transmitted contagion a delay analysis has been
carried out: the COVID-19 data were moved forward in time, obtaining scatter
plots with different delays. The correlation was calculated using the Pearson
correlation coefficient (PCC), a statistic that measures the linear
correlation between two variables X and Y, with a value between -1 and +1. A
value of +1 indicates total positive linear correlation, 0 indicates no linear
correlation, and -1 indicates total negative linear correlation.

Delay unit | PCC Lombardia | PCC Wuhan
---|---|---
0 | 0.0770 | -0.2474
1 | 0.2773 | -0.2514
2 | 0.4983 | -0.2496
3 | 0.6629 | -0.1775
4 | 0.7180 | 0.0271
5 | 0.7918 | 0.1934
6 | 0.8774 | 0.3842
7 | 0.8092 | 0.4857
8 | 0.7532 | 0.3905
9 | 0.8969 | 0.2969
10 | 0.8334 | 0.2905
11 | 0.8403 | 0.1813
12 | 0.8736 | -0.0652
13 | 0.7830 | -0.2000
14 | 0.8302 | 0.0291
15 | 0.8854 | 0.1762
Table 1: Pearson Correlation Coefficient for Lombardia and Wuhan

From Table 1 it can be seen that the maximum correlation (positive) was
recorded with a delay of 9 units (Fig. 11 and Fig. 12) in the case of Lombardy
region.

Figure 11: New daily cases VS average concentration of $NO_{2}$ in Lombardy
Figure 12: New daily cases VS average concentration of $NO_{2}$ in Lombardy

Similarly, from Table 1 it can be seen that the maximum correlation (positive)
was recorded with a delay of 7 delay units (Fig. 13 and Fig. 14) in the case
of Wuhan.

Figure 13: New daily cases VS average concentration of $NO_{2}$ in Wuhan.
Figure 14: New daily cases VS average concentration of $NO_{2}$ in Wuhan

Several considerations can be made.

First of all, if the delay units are converted back to number of days, the
maximum value of the correlation between NO2 and COVID-19 is found after 35
days for Wuhan region, and for 45 days for Lombardy region. Somehow, it seems
that restrictions in Wuhan brought to better results in terms of COVID-19
reduction than in Italy. Moreover, it is clear that the a-posteriori measures
of lockdown cannot be an efficient mode of intervention, because before the
effects on infection reduction become significant more than one month, one
month and half must pass. Lastly, if the graphs related to Wuhan are analyzed
(as underline before) an anomaly appears evident. In Fig. 14, when the NO2
values begin to increase (since the lockdown has been removed), the number of
new infected remains very low, and this trend raises some doubts about the
veracity of the data on the number of infected communicated by Wuhan in this
second phase. Clearly a further analysis might give additional insights.

##  6 Conclusions

In this paper we have presented the cross-disciplinary AIRSENSE-TO-ACT
project, aiming at the creation of a Decision Support System for the timely
and effective activation of targeted countermeasures during virus pandemics,
based on a model merging data from very heterogeneous sources, including
ground wireless networks of low-cost dust monitors, and satellite data,
spanning from meteorological and pollution data to crowd sensing. The
correlation between virus diffusion and concentration of NO2 has been
analyzed, and the further analysis of the correlation between virus diffusion
and PM in the air would be the main focus of future investigations. Moreover,
work is in progress on the integration of mobility data Google  inside the
model.

The "second wave" currently spreading in Europe demonstrates the urgent need
for such a tool as proposed in this paper, in order to limit the economical
damage of generalized lockdowns and restrictions.

##  7 Acknowledgments

Authors want to thank Maria Pia Del Rosso and Chiara Zarro for their useful
insights and support.

All the authors contributed equally to this work

The authors declare no conflict of interest.

References

yes

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