Air pollution can affect health and well-being of people and eco-systems. Due to the health risk posed for sensitive population groups, it is important to provide with hourly and daily forecasts of air pollution. One way to assess air pollution is to make use of the Common Air Quality Index (CAQI) of the European Environment Agency (EEA). In this paper we employ a number of Computational Intelligence algorithms to study the forecasting of the hourly and daily CAQI. These algorithms include artificial neural networks, decision trees and regression models combined with different datasets. The results provide with a satisfactory CAQI forecasting performance that may be the basis of an operational forecasting system.