Indoor PM2.5 concentrations and students’ behavior in primary school classrooms

It is important to understand changes in PM2.5 concentrations to evaluate and control air pollution in the classroom. In this paper, the PM2.5 concentrations in classrooms of a primary school in North China were studied, where the particulate matter, temperature and humidity were monitored continuously from February 2018 to February 2019. Student behavior has been recognized as an important influential factor of indoor air quality; however, modeling the relationship between student behavior and PM2.5 concentrations in classrooms remains an unsolved problem. In this paper, a novel intelligent data-driven symbolic regression method is applied to model the relationships between various factors and PM2.5 concentrations. The advantage of this method is that it can automatically distill knowledge from data and discover the structures and parameters of relationship models simultaneously. By considering the annual schedules and daily behavior, a symbolic regression model is incorporated with mass conservation to describe the change in PM2.5 concentrations caused by student behaviors. The experimental results show that outdoor PM2.5 concentrations can influence the concentrations of indoor PM2.5 through meteorological variations. Moreover, student behaviors play an important role and can lead to rapid changes in indoor PM2.5 over a short timeframe. This paper provides a solid theoretical analysis of the relationship between indoor PM2.5 concentrations and student behaviors in classrooms. The numerical models proposed by this research can assist in the analysis of PM2.5 concentrations and the improvement of air quality in the classroom.