Knowing in advance how air quality will change over time can help prevent the harmful effects of air pollution on health. Therefore, an entirely self-developed forecasting system consisting of a strong statistical model evaluated using different regression methods was built for this purpose. Data is provided by a wirelessly interconnected, self-sufficient measuring station built using a nanocomputer and sensors. The intelligent system I designed learns what air quality disturbs a specific user since people from different age groups with different diseases don’t endure the same pollution level. Thus, the platform tries to implicitly diagnose the capacity of a certain user to resist to air pollution so that adequate precautions can be provided.