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

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.