Assessment of Particulate Matter Concentration in Underground Transport Work Environment

Abstract

Air quality sensors networks are an interesting topic for research and development as they can provide valuable information about pollution sources and levels. Metro systems are a vital component of large cities by enabling affordable and fast transportation. The air input for the subways is coming from street level admission points that are affected by traffic pollution so, a system for monitoring and assessment of the air in the underground infrastructure is also crucial. This paper describes an IoT network that was designed to monitor the environment in hazardous work areas and to estimate the pollutants concentrations values in the underground transport infrastructure. The goal of the paper is defining a system capable of collecting as much information as possible about the air quality level in underground infrastructures. For the data acquisition part, a total of five equipment was installed in the pilot location both for monitoring but also for communication purpose between the devices. Two models for prediction of the PM10 and PM2.5 concentrations were elaborated, tested and validated on a data set represented by 200 values of hourly concentrations for each of the PM10 and PM2.5 pollutants. The results show that the main pollution source is represented by the urban road traffic pollution and the model predicts very well the concentration inside the underground environment.

Keywords

• air quality
• iot
• pms
• models