Bachelor Thesis Co-Relator - Morena Gismondi

Bachelor Degree in Electronic Engineering for Automation and Telecommunications, University of Sannio, Engineering Department, 2020

Thesis title: Use of Sentinel-5P data for the analysis of the correlation between NO2 levels and mobility data in areas with a high number of infections due to Covid-19. Case study: Lombardy Region.


2020, for our country, but above all on a global level, represented a year that will go down in history; this is because humanity has found itself facing something new, unknown.

It is specifically a virus belonging to the Coronavirus family, called Coronavirus SARS-CoV-2 or more commonly known as COVID-19; virus certainly different from simple flu, with a very high degree of virulence, capable of significantly affecting the infected subject at the pulmonary level with serious repercussions for the organism as a whole and that in the worst case, often in patients suffering from secondary diseases, it can be completely deleterious and therefore cause death.

It broke out on December 31, 2019 in China, precisely in Wuhan, Hubei province, and in less than a month it spread to the rest of the world; therefore, it had an exponential growth. The contagions per day concerned very high numbers, thousands of people were positive every day, something that was literally getting out of hand and that one was absolutely unable to control.

Precisely this chaos generated terror and in a very short time a decision was made that put a strain on the entire system, namely a worldwide lockdown. The virus was analyzed and in a short time the man was able to understand what was in front of him, in addition a very important thing was noticed, concerning the fact that this infection did not spread in the same way and with the same speed in every part of the world.

This was a question to which many scholars had to find an answer and it was found that the areas most affected by Covid-19 were those characterized by a greater presence of fine dust and some pollutants in the air. Put simply, the spread of the virus is therefore closely related to pollution.

Having said that, starting from this basic knowledge, I wrote my thesis work: using Sentinel-5P data, a satellite launched into orbit in autumn 2017 by the European Space Agency (ESA), I searched to find a correlation between areas with higher concentrations of pollutants and the spread of the virus.

For the processing of the satellite images I used an online platform that allows anyone to access the data collected by the satellites and understand the impact on social and economic changes caused by the coronavirus pandemic. This initiative is the result of the joint cooperation between ESA and the European Commission.

Finally, through a code in Phyton, I went to acquire the data. Finally, through code written in Python, the satellite data were analyzed and correlated with those relating to mobility indices and COVID-19 infections.