Artificial Intelligence for the Pollution Source Identification - Nicoletta Lotrecchiano; Diego Barletta; Massimo Poletto; Daniele Sofia (Chemical Engineering Transactions, 2022)
The pollutants dispersion in the environment and the positioning of the emission sources, identified from the measured air quality data, are the main knowledge for defining the environmental status. This study aims to locate the pollution source in the urban environment starting from experimental measurements obtained from the monitoring networks. In detail, the source identification algorithm uses artificial intelligence for source identification (AISI), which was applied to the case study of an industrial paper mill located in a region of southern Italy. In this case study, the air quality monitoring network consists of four smart measuring devices arranged in such a way as to obtain triangular meshes, capable of providing real-time 24/24 h measurements with a resolution of 1 min, information on NO2, PM10, and PM2.5 concentrations, temperature, pressure, relative humidity, wind direction, and intensity. The developed AISI algorithm allows combining the information on the concentrations of pollutants and the wind intensity and direction with their positions to identify the probable pollution source position. Some interesting days were analyzed identifying the pollution sources' locations that were external to the network.