The Use of Multilayer Perceptrons to Model PM_2.5 Concentrations at Air Monitoring Stations in Poland

Abstract

The biggest problem facing air protection in Poland is the high levels of suspended particular matter concentrations. Air monitoring reports show that air quality standards, related to PM10 and PM2.5 concentrations, are exceeded every year in many Polish cities. The PM2.5 aerosol fraction is particularly dangerous to human and animal health. Therefore, monitoring the level of PM2.5 concentration should be considered particularly important. Unfortunately, most monitoring stations in Poland do not measure this dust fraction. However, almost all stations are equipped with analyzers measuring PM10 concentrations. PM2.5 is a fine fraction of PM10, and there is a strong correlation between the concentrations of these two types of suspended dust. This relationship can be used to determine the concentration of PM2.5. The main purpose of this analysis was to assess the accuracy of PM2.5 concentration prediction using PM10 concentrations. The analysis was carried out on the basis of long-term hourly data recorded at several monitoring stations in Poland. Artificial neural networks in the form of a multilayer perceptron were used to model PM2.5 concentrations.

Keywords

• air monitoring
• air quality modeling
• particular matter
• PM_2.5
• concentration
• prediction