Extended Abstract The problem of assessing, managing and forecasting air pollution (AP) has been in the top of the environmental agenda for decades, and contemporary urban life has made this problem more intense and severe in terms of quality of life degradation. A number of computational methods have been employed in an effort to model and simulate air quality (AQ). Air pollution is related to various substances, is affected by physical and chemical mechanisms of various spatial and temporal

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... ales, and is regulated in terms of target values that are different to each other. Thus, AP requires for computational and knowledge management tools that are able to deal with its complex (and exiting from the scientific point of view) nature. Moreover, such methods should be able to deal with missing observation data, data of mixed nature (be it nominal, categorical, binary or other), and imitate the behavior and the "intelligence" of the phenomena that need to be modeled and simulated. This means that deterministic modeling, employing fluid mechanics, atmospheric chemistry and physics (the "traditional way for modeling AQ") are not able to "catch" all the aspects of the AP problem. Other methods should be employed, that are able to deal with knowledge extraction and management, and are able to map knowledge into the "intelligence" of the algorithms that they apply. On this basis, Artificial Intelligence should be used. This is a thesis recognized already from the 90ties, where the first sets of scientific publications in areas like neural networks and fussy logic have appeared with applications in AQ.