The combined application of several soft-computing and statistical techniques is proposed for the characterization of atmospheric conditions in two European regions: Madrid (Spain) and Prague (Czech Republic). The resulting Hybrid Artificial
Intelligence System (HAIS) combines projection models for dimensionality reduction and clustering, combining neural and
fuzzy paradigms, in a decision support tool. In present article, this proposed HAIS is applied to analyse the air quality in
these two geographical regions and get a better understanding of its circumstances and evolution. To do so, real-life data
from six data-acquisition stations are analysed. The main pollutants recorded at these stations between 2007 and 2014, their
geographical locations and seasonal changes are all studied, in a research that shows how such factors determine variations in
air-borne pollutants. Furthermore, neural projections of the clustering results from data on atmospheric pollution are studied.
