APPLICATION OF FUZZY LOGIC IN THE PREDICTION OF AIR POLLUTION IN BERLIN, GERMANY

Abstract

Air pollution is the presence of harmful substances in the atmosphere that can adversely impact human health and other living organisms. In urban environments such as Berlin, air pollution—particularly from traffic emissions and industrial activities—continues to be a significant concern. The Air Quality Index (AQI) is commonly used to represent the level of air quality in a given area, based on the average concentrations of pollutants including particulate matter (PM10 and PM2,5), ozone (O₃), carbon dioxide (CO₂), sulphur dioxide (SO₂), and nitrogen dioxide (NO₂). Accurate prediction of AQI values is crucial for mitigating the negative effects of air pollution on both the environment and public health. This paper proposes a model for AQI prediction using a fuzzy data. By applying fuzzy sets and fuzzy logic techniques, an approach is presented to handle inaccurate, incomplete, or subjective data, to achieve more reliable and robust air quality assessments.