Design of an Urban Monitoring System for Air Quality in Smart

Cities

Andrea Marini

, Patrizia Mariani

, Alberto Garinei

1,2

, Stefania Proietti

, Paolo Sdringola

Massimiliano Proietti

, Lorenzo Menculini

and Marcello Marconi

1,2

Idea-Re S.r.l., Perugia, Italy

Department of Sustainability Engineering, Guglielmo Marconi University, Rome, Italy

ENEA Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy

Keywords: Air Quality, Urban Monitoring, LoRaWAN, Sensors, AHP, Cellular Automata, Smart City.

Abstract: Pollution is one of the main problems faced by cities nowadays, due to the increase in emissions from

anthropogenic sources resulting from economic, industrial and demographic development. High values of

pollutants, such as atmospheric particulate matter, lead to adverse effects on the environment and human

health, causing the spread of respiratory, cardiovascular and neurological problems. For instance, recent

work shows a connection between the spread of the Covid-19 pandemic and environmental pollution. In this

context, urban monitoring of pollutants can allow to evaluate and perform actions aimed at reducing

pollution in order to safeguard citizens’ health. This study proposes a method to design an urban air quality

monitoring system. It uses the AHP multi-criteria decision-making technique to define the initial positioning

of the sensors, and the cellular automata mathematical model for the following optimization, from which the

final configuration of the network is derived. In the present case study, the monitoring concerns atmospheric

particulate matter (PM10 and PM2.5) and is carried out with six sensors that constitute a LoRaWAN

network, as often used for monitoring activities in smart cities.

1 INTRODUCTION

The economic, industrial and demographic

development of the last two centuries has led to a

considerable improvement in the quality of human

life, but it has caused at the same time significant

consequences for the environment. Indeed,

anthropogenic sources such as industrial processes,

vehicular traffic and domestic heating are identified

as the main causes of pollution (Samad & Vogt,

2020). According to World Health Organization

(2006), four main air pollutants can be identified:

particulate matter (PM10, PM2.5), nitrogen dioxide

(NO2), sulfur dioxide (SO2) and ozone (O3). In the

event that the concentrations of these pollutants

reach high values, human health is likely to be

compromised with the insurgence of respiratory,

cardiovascular and neurological problems (Ghorani-

Azam et al.,2016) and the balance of ecosystems is

put at risk (De Marco et al., 2019). During the year

2016, according to the WHO, 91% of the world

population lived in places where air quality did not

meet the levels established by the guidelines; also in

the same year, air pollution caused 4.2 million

deaths worldwide. A reduction of particulate matter

from 70 to 20 micrograms per cubic metre is

estimated to reduce mortality by 15%, also lowering

the incidence of diseases (Ambient (outdoor) air

pollution, 2018). Kurt et al. (2016) studied the

effects of pollution on the respiratory system and

identified ozone and particulate matter as the main

responsible of cardiopulmonary diseases. In

particular, children have been found to be the most

sensitive to pollution-induced effects. A study

conducted on 265 children from two Indian cities

with different levels of pollution found a greater

amount of dysfunction in the respiratory tract in

children with long-term exposure to high pollution

values (De, 2020). Moreover, scientific research

showed the role of pollutants in the spread of

viruses, especially particulate matter. A more

significant presence of the Avian Influenza Virus

was identified in air samples collected during the

days of Asian dust storms, when concentrations of

PM10 and PM2.5 are higher. This showed the role

Marini, A., Mariani, P., Garinei, A., Proietti, S., Sdringola, P., Proietti, M., Menculini, L. and Marconi, M.

Design of an Urban Monitoring System for Air Quality in Smart Cities.

DOI: 10.5220/0010405200940101

In Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2021), pages 94-101

ISBN: 978-989-758-512-8

of dust storms in the long-range transport of virus

(Chen et al., 2010). Then, following the spread of

the SARS-CoV-2 virus (known as Covid-19

pandemic), numerous studies were carried out to

evaluate the role of pollution in the spread of the

disease and its consequences on the severity of the

effects caused and on mortality rates. This disease,

similar to the severe acute respiratory syndrome

(SARS) that occurred in 2002, broke out in Wuhan

(China) in December 2019 and then spread

worldwide. In Italy, the first cases of infection were

officially reported at the end of February 2020,

particularly in the northern regions. In March, a

relationship was hypothesized between air pollution

and the spread of SARS-Cov-2 infections. A

position paper (Setti, Passarini, De Gennaro, Di

Gilio et al., 2020) on this topic was published by

some experts of the Società Italiana Medicina

Ambientale (SIMA) together with researchers from

Italian universities. The authors analysed the daily

concentration of PM10 and the number of infections

by Covid-19, for each province. They found a

relationship between the exceedances of PM10 limit

values recorded in the period February 10

February 29

2020 and the number of COVID-19

cases updated to March 3

, considering that the

infection is diagnosed with a latency time of 14

days. One month after the publication of the position

paper, SIMA claimed to have ascertained the

presence of the new coronavirus in particulate matter

from the extraction of SARS-Cov-2 RNA (Setti,

Passarini, De Gennaro, Barbieri et al., 2020). The

analysis was carried out on 34 samples of PM10

collected for three weeks (from February 21

March 13

2020) in industrial sites located in the

province of Bergamo. The results were confirmed on

12 samples for the three genes E, N, RdRP used as

molecular markers. European Public Health Alliance

(2020) stated that those who live in cities with high

concentrations of pollutants are more exposed to the

risks deriving from Covid-19. This hypothesis was

made on the basis of statements made by the

European Respiratory Society (ERS): people with

chronic lung and heart diseases caused by long-term

exposure to poor air quality are less able to fight

lung infections and therefore also Covid-19. To

confirm the hypothesis, results of a study conducted

in 2003 on SARS (Cui et al., 2003) were also used.

This study found that people living in regions with a

moderate air pollution index present an 84% higher

chance of death than inhabitants of regions with a

low index. Research by Wu et al. (2020a, 2020b)

showed that, in the long run, a difference of one

microgram in the average of PM2.5 is sufficient to

increase the mortality rate of Covid-19 by 11%. The

analysis compares the levels of particulate matter

recorded in 3089 American counties with deaths for

Covid-19 until June 18th 2020 and examines several

variables: population size, hospital beds, weather,

socioeconomic and behavioural conditions. A study

on Italian territory (Fattorini & Regoli, 2020)

focused on the role of chronic exposure to air

pollutants. From the analysis of NO2, PM2.5 and

PM10 values detected in Italy in the last 4 years, it

was found that Northern Italy has been constantly

exposed to high levels of atmospheric pollution and

there is a correlation between these data and the

Covid-19 cases for 71 provinces.

In order to assess the level of atmospheric

pollution and take action to ensure good air quality,

limiting the spread of Covid-19 and other diseases,

we intend to define the design of an urban

monitoring system for air quality in smart cities of a

size similar to that of the case study. The method

takes into account the main anthropogenic sources of

air pollution and it is applied in the smart city on the

basis of the specific urban characteristics of the

place under study and with the involvement of

citizen science, creating a participatory process.

2 LITERATURE REVIEW

Collecting air quality data through monitoring

networks allows to assess pollution levels and,

where appropriate, suggests actions that are to be

taken in order to avoid the adverse effects of

pollution on the environment and human health

(Kainuma et al., 1990). The chosen measurement

points must ensure the best possible

representativeness of the area's air quality and also

take into account the location of point sources such

as industrial sites (Kibble & Harrison, 2005).

Hacıoğlu et al. (2020) pinpointed the locations of

two air quality monitoring stations among potential

urban and rural sites by using two techniques:

Analytic Hierarchy Process (AHP) and Elimination

Et Choix Traduisant la Realité III (ELECTRE III).

This was done on the basis of seven criteria:

pollution levels, security, availability of electricity,

collaborations, staff support, easy access, distance.

Both methods have identified the same positions,

thus validating each other. Mofarrah et al. (2011)

divided the study area into a grid where each square

represented a possible position for the air quality

monitoring network sensor. With the criteria of air

quality, location sensitivity, cost, population

sensitivity and population density, a fuzzy matrix of

Design of an Urban Monitoring System for Air Quality in Smart Cities

pairwise comparisons was formed and a score was

assigned to each potential position. The optimal

positions for the sensors were identified through the

values obtained from the Fuzzy Analytical

Hierarchy Process (FAHP) plus the degree of

representativeness of the area. FAHP method was

also used to evaluate the atmospheric environmental

quality in five cities in China (Lv & Ji, 2019),

achieving better results with an index system than

the standard air pollution index.

A mathematical model that can be used to

describe and simulate environmental phenomena

varying in time and space is the cellular automaton.

Benjavanich et al. (2017) modelled and simulated

the flow of pollution with cellular automaton in an

area of 3x3 km. A variable number of sensors was

considered, and each cell was provided with updated

levels of pollution and wind action. Marín et al.

(2020) used cellular automata to simulate the spread

of air pollution considering gravity, diffusion and

wind transport as calibration factors. Lauret et al.

(2016) combined cellular automata with artificial

neural networks to evaluate the atmospheric

dispersion of methane in 2D. In particular, the neural

networks were used for making predictions and

cellular automata for space-time simulation.

3 CASE STUDY

The case study of this research is the town of Santa

Maria degli Angeli (43°03′32″N 12°34′41″E), a part

of the Municipality of Assisi (Italy) with 8470

inhabitants. It is one of the main tourist destinations

in the region, due to the presence of important

religious sites. Over the years, the area has

experienced an important urban development,

becoming equipped with all the services necessary

for residential settlement and, in addition, also with

Figure 1: View of study area.

industrial activities, favoured by the presence of the

railway line and the proximity to the highway. These

industrial activities are mainly concentrated in the

south-west area, but there is also a foundry near the

inhabited centre. Together with the road traffic,

which concentrates on the three main axes of

connection with important road arteries and with the

nearby urban centers, these activities are the main

sources of pollution for the town (Figure 1).

4 METHODOLOGY

Based on the studies in the literature and the

importance of air pollution assessment in order to

safeguard the health of citizens, we want to propose

a design method for a low-cost urban monitoring

system of air quality that can be implemented in any

small-to-medium-sized smart city. In particular, we

propose to create a LoRaWAN network, with the

location of the sensors determined through the

application of the Analytic Hierarchy Process (AHP)

multi-criteria decision-making technique between

many potential positions and optimized through the

application of the mathematical model of cellular

automata in order to ensure the best overall coverage

of the polluted area. For the case study, the

configuration of the LoRaWAN network, which

consist of six sensors, is initially established among

twelve alternatives by use of the AHP method.

These positions are corrected using cellular

automata, assigning a transition probability

determined by the level of pollution present in the

neighbourhood of the sensor. The sensors will detect

the amount of PM10 and PM2.5 which, as discussed

in the introduction, have been showed to play a key

role in the spread of viruses.

4.1 Analytic Hierarchy Process

Analytic Hierarchy Process (AHP) is a multi-criteria

decision-making technique, developed by Thomas

Lorie Saaty in the 1970s, which allows to assign

priorities to a series of decision-making alternatives

and define them on a single scale, relating

parameters that are not directly comparable, such as

qualitative and quantitative evaluations. The method

is applied in three steps: definition of a hierarchy of

the problem, comparison of judgments and

calculation of the priority vector, hierarchical

recomposition (Analytic Hierarchy Process, n.d.).

As regards the hierarchy, the final objective is

placed at the highest level, then come the various

criteria that contribute to the objective and finally

SMARTGREENS 2021 - 10th International Conference on Smart Cities and Green ICT Systems

the different alternatives to be evaluated. In the

second phase, in order to evaluate how much each

criterion affects the final decision, a pairwise

comparison matrix is constructed by assigning the

judgments according to the values of the

fundamental scale (Table 1). The matrix is square

and of size equal to the number of elements of the

hierarchical level being considered. For n criteria,

with 𝑖,𝑗=1,2,..𝑛, the matrix of pairwise

comparisons is:

𝐴

=𝑎





(1)

where 𝑎



indicates how much the i-th criterion

is more important than the j-th. If 𝑎



1, the

element 𝑖 is preferred to 𝑗; if 𝑎



1, the opposite is

true. In order to make consistent judgments, it must

be established that:

• 𝑎



=1/𝑎



for 𝑖,𝑗=1,2,..𝑛.

• 𝑎



=Σ





𝑎



⋅𝑎



for all 𝑖,𝑘=1,2,..𝑛.

The same type of pairwise comparison is carried out

among the alternatives referred to each criteria.

For each matrix considered, the priority vector is

obtained from the components of the main

eigenvector 𝑤 corresponding to the main eigenvalue



of the matrix 𝐴:

𝐴

∙𝑤=



∙𝑤

(2)

At this point, the consistency of the assessment is

verified by calculating the Consistency Index (𝐶.𝐼.):

𝐶.𝐼.=



𝑛

n1

(3)

if this is less than 10% of the Random

Inconsistency (𝑅.𝐼.) value for the corresponding is

Table 1: The fundamental scale for pairwise comparison.

Intensity of

importance

Definition

1 Equal importance

2 Weak importance

3 Moderate importance

4 Moderate plus importance

5 Strong importance

6 Strong plus importance

7 Very strong importance

8 Very, very strong importance

9 Extreme importance

Table 2: Values of Random Inconsistency (𝑅.𝐼.).

𝑛 𝑅.𝐼.

1 0.00 6 1.24

2 0.00 7 1.32

3 0.58 8 1.41

4 0.90 9 1.45

5 1.12 10 1.49

number of elements 𝑛 (Table 2), the decision

acceptable (Analytic Hierarchy Process (AHP),

n.d.). Otherwise, the reasons for the inconsistency

should be analysed and the judgments reviewed in

order to reduce the inconsistency. In the last phase,

the global weights of the alternatives are defined by

applying the principle of hierarchical composition,

determining their order of importance: the local (i.e.

within a given level) weights of each alternative are

multiplied by those of the corresponding higher-

order criteria and the products thus obtained are

added together (Latora et al., 2018).

4.2 Cellular Automata

The concept of cellular automaton was introduced

by J. von Neumann in 1947 and then applied in

practice by J.H. Conway in "Game of life" in 1968.

A cellular automaton is a discrete dynamic system:

in such model space, time and properties of the

automata can only assume a finite and countable

number of states. It consists of a set of elements,

called cells, organized in a regular spatial grid and

taking on a finite number of states. The state of each

cell at a certain moment evolves according to a

given transition rule, with the updated state of a cell

depending on the previous state of the cell itself and

the states of the neighbourhood. The latter can be of

various kinds, with most common examples

including the von Neumann, Moore and Margolus

neighbourhoods ((D’Ambrosio, 2003).

5 RESULTS

The application of the AHP method has made

possible to identify the initial configuration of the

sensor network for urban monitoring of air quality.

In defining the AHP hierarchy, the final objective

was placed at the top level, i.e. the identification of

the most significant points for the monitoring

activity, then the various criteria that contribute to

the objective and therefore determine atmospheric

pollution: home heating, traffic and presence of

Design of an Urban Monitoring System for Air Quality in Smart Cities

industrial activities. Potential sensor positions were

located at the lowest level of the hierarchy (Figure

2). For the case study, these are twelve and were

chosen in barycentric points of each urban sector (A-

L) identified by the three main roads axes and the

roads of major importance that lead into them

(Figure 3). Generally, the composition of the

matrices, then the attribution of judgments, and the

resulting final output are determined by a single

individual or a group decision. In this case, a mixed

approach was used: a participatory process, with the

direct involvement of citizens through

questionnaires, was used to determine the hierarchy

of the criteria and a more objective method, with a

single judgment, to evaluate the different sensor

positioning alternatives.

In the distributed questionnaire it was asked to

express which is believed to be the main source of

atmospheric pollution among home heating, traffic

and the presence of industrial activities. In addition,

it was asked how much the indicated source of

pollution was more decisive than the other two,

expressing a value in the scale from 1 to 9. The

anonymous questionnaires were distributed to a

heterogeneous sample of citizens, inhabitants of the

study area, of different ages and gender. 38

questionnaires were collected, mostly from people

over the age of 60, 19 males and 19 females, who

have been living in that area for more than 10 years

and spend the whole day there. Of 38 questionnaire

replies, 25 indicated industrial activities as the main

source of pollution, 13 indicated traffic and 0 home

heating. Given the values with which they expressed

the importance of the main source of pollution

compared to the other two, the geometric mean was

calculated and approximated to the nearest integer

number in order to compose the matrix of pairwise

comparisons. In particular, it was obtained that the

presence of industrial activities has a very, very

strong importance (value 8) compared to home

heating and strong importance (value 5) compared to

traffic; instead, traffic has a very strong importance

(value 7) compared to home heating. The same

matrix is composed of the values 1 in the main

diagonal, because it concerns the pairwise

comparison of an element with itself, and of the

reciprocal values of those already indicated,

disallowing inconsistent judgments (Table 3). The

eigenvector of the matrix was calculated and the

weight of each criterion was found: 0.0544 for home

heating, 0.2331 for traffic and 0.7125 for industrial

activities. The Consistency Index (𝐶.𝐼.) is equal to

0.12 and therefore higher than 10% of the Random

Inconsistency (𝑅.𝐼.) value for three elements. Being

Figure 2: AHP hierarchy for the selection of sensor

positions for the case study.

Figure 3: Potential positions of the air quality monitoring

sensors in the study area.

a value deriving from a group decision and having

used the geometric mean, it was still considered

acceptable, without going to review the judgments.

In fact, in the case of group decisions, three

conditions must be verified: symmetry, linear

homogeneity and concordance: the use of the

geometric mean allows to respect all three and also

to have reciprocity and separability (Analytic

Hierarchy Process, n.d.). The evaluation of the

twelve alternatives for the home heating criterion

was made on the basis of the population data in each

sector, recorded in the Municipality database. A

higher population corresponds to a higher use of

home heating. Sector B has the highest number of

inhabitants while sector L has the lowest one. The

population of each sector was compared with that of

the others and the pairwise comparisons were made

objectively, assigning the values in the fundamental

scale. Regarding the traffic criterion, the analysis

was carried out considering how each sector is

enclosed by very busy roads, therefore by the

connecting axes with the nearby urban centres and

by the highway. The values associated with each

sector were compared in pairs and the matrix was

again formed using the fundamental scale of the

AHP. The evaluation of each of the twelve

alternatives with regard to the criterion of the

SMARTGREENS 2021 - 10th International Conference on Smart Cities and Green ICT Systems

Table 3: Matrix of pairwise comparisons of the criteria.

Home

heating

Traffic

Industrial

activities

Home heating 1 1/7 1/8

Traffic 7 1 1/5

Industrial activities 8 5 1

presence of industrial activities was made

considering the average distance of each sector from

the foundry and the industrial area to the south-west

of the town. Similarly to the two previous criteria,

the values to include in the matrix were identified in

a very objective way. The eigenvector of each

matrix was calculated and the weights of each

alternative relating to each criterion were obtained

with the subsequent normalization. All the matrices

were found to be consistent, having obtained in the

order the following Consistency Indices ( 𝐶.𝐼.):

0.1071, 0.099 and 0.1028, all less than 10% of the

Random Inconsistency (𝑅.𝐼.) value.

Finally, the last step of the AHP method was

carried out, namely the hierarchical recomposition,

adding for each of the twelve alternatives the

products of the local weights and the weights of the

relative criteria (Table 4). The six alternatives to

which correspond the highest global weights, that is,

F, G, H, J, K and L, identify the initial configuration

of the LoRaWAN network.

In this work, cellular automata are used to

establish the final positions of the air quality

monitoring sensors, optimizing the configuration

obtained with the AHP method with the aim of

maximizing the coverage of polluted areas. Firstly,

the dimensions of the grid cells to superimpose on

the study area were established. They were defined

to be 200x200 m, thus obtaining an 11x8 grid.

Twofold information was assigned to each cell: one

variable takes into account the presence or absence

of a sensor in the cell under scrutiny, and another

one is related to the level of pollution. More in

detail, the first variable was determined from the

results of the AHP method, and the second derives

from the answers of citizens to the questionnaires.

This information forms the initial state of the

cellular automaton (Figure 4). The transition rules

guiding the system’s dynamics are defined using

Moore’s neighbourhood, which is made of eight

cells plus the starting one. At a given step during the

system evolution, the configuration determines the

set of positions of the sensors in the grid. At each

iteration the sensor can move to one of the eight

surrounding cells or remain in its current position.

Table 4: Results of AHP for the localization of monitoring

sensors.

Sector Home

heating

(0.0544)

Traffic

(0.2331)

Industrial

activities

(0.7125)

Global

weights

A 0.171 0.0214 0.0141 0.0244

B 0.3174 0.0149 0.0114 0.0288

C 0.1315 0.0546 0.0434 0.0508

D 0.0364 0.0434 0.0114 0.0202

E 0.0251 0.0159 0.0411 0.0344

F 0.08 0.0346 0.129 0.1043

G 0.1034 0.2832 0.2008 0.2147

H 0.0156 0.0271 0.1515 0.1151

I 0.0482 0.0546 0.0674 0.0634

J 0.0431 0.0689 0.2008 0.1614

K 0.0156 0.1685 0.1017 0.1126

L 0.0127 0.2128 0.0275 0.0698

The displacement of each sensor is determined

stochastically according to the following procedure:

1) The coefficient 𝑘 of polluted areas

coverage is calculated for the current

location of the sensor and for the other

future possible positions, that is, the eight

cells in its neighborhood. Given a certain

cell, the 𝑘 coefficient is defined as the

weighted sum of polluted cells within the

Moore neighborhood of the cell under

consideration; the weights are chosen to

decrease exponentially with the distance

from the central cell in which the sensor is

located. The matrix of weights is therefore

the following:



0.24 0.37 0.24

0.37 1 0.37

0.24 0.37 0.24



(4)

2) A probability 𝑝



is assigned to each

possible displacement on the basis of the

calculated coefficients:

𝑝



𝑒





∑

𝑒







(5)

3) The future position of the sensor is

determined by random extraction among

the nine possibilities, according to the

probabilities 𝑝



The new sensor configuration of is then compared

with the previous one in order to assess whether it

determines a greater overall coverage of polluted

areas. The overall coverage is computed as the sum

of the 𝑘



coefficients of all sensors, also adding

Design of an Urban Monitoring System for Air Quality in Smart Cities

negative penalties if pairs of sensors lie in adjacent

cells or in the same cell. The new configuration is

accepted if it results in an increase of global

coverage, otherwise it is discarded and the system

remains in the previous configuration. According to

this rule, the positions of the sensors in the case

study were changed compared to the initial state and

the configuration shown in Figure 4 was determined,

ensuring a wider coverage of the polluted area.

6 CONCLUSIONS

This study focuses on the definition of a design

method for an air quality urban monitoring system,

useful for assessing pollution levels which derive

from different sources. The method allows to

identify the most significant positions for monitoring

within the study area. Due to the nature of the

problem, which required to evaluate different

alternatives and to take into account more criteria,

we applied the AHP multi-criteria decision-making

technique. Citizens were involved in the decision-

making through questionnaires, where they were

asked to fill in the pairwise comparison matrix of the

criteria. The group decision has identified the

following scale of criteria: industrial activities

(0.7125), traffic (0.2331) and home heating

(0.0544). The twelve sensor position alternatives

were evaluated with regard to the three criteria, in an

objective way and with a single judgment,

considering the specific features of each sector:

number of inhabitants, exposure to very busy roads

and average distance from industrial activities. The

hierarchical recomposition produced the global

weights and determined the order of preference of

the alternatives. The first six sectors, namely sectors

F, G, H, J, K and L, are the one where the six

LoRaWAN sensors for urban monitoring of

atmospheric particulate matter (PM10 and PM2.5)

should be placed. However, in order to maximize

global coverage of polluted areas, an optimization of

the mentioned AHP configuration was carried out

using a cellular automaton. After defining the grid

and the type of neighbourhood, a procedure was

devised, allowing the evolution of the state

(presence/absence of sensor) of the cells based on a

transition probability determined as a function of

coverage coefficients 𝑘 of the cells in the

neighbourhood. Using this model, the positions of

the sensors that had been found with the AHP

method were corrected to achieve greater coverage

of the polluted area, thus establishing the final

configuration of the network.

Figure 4: Evolution of sensor positions from the initial

state (a) to the final configuration (b) through the cellular

automaton.

The future development of this work will deal

with a more refined optimization of the sensors

positioning, considering levels of pollution

determined not only by the replies to the

questionnaires but also by the data actually detected

by the sensors and, importantly, the epidemiological

data regarding respiratory and cardiovascular

diseases associated with long-term exposure to high

levels of pollution. Therefore, when the sensors will

be installed in the final configuration determined in

the present study, and when a significant amount of

measurements of pollutants detected by those

sensors will have been collected, the cellular

automaton will be run again. It is important to stress

that the method to define an urban air quality

monitoring system proposed in this study lends itself

to be implemented in other smart cities, with

variable numbers of sensors and the possibility of

taking into account more pollutants.

ACKNOWLEDGEMENTS

The authors would like to thank the Municipality of

Assisi for their collaboration. The study presented in

this paper is part of the PLANET project financed to

Idea-re S.r.l. by Regione Veneto (IT) POR FESR

2014-2020 Asse I Azione 1.1.1.

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