Classification-driven air pollution mapping as for environment and health analysis

This article presents an air pollution modelling approach and its use in healthapplications within the EO2HEAVEN project. The model is based on a multidimensionalInverse DistanceWeighting and makes use of artificial distances between area attributes.The number and type of attributes used is fully customizable and can be adapted accordingto specific application fields and data preconditions. It is kept flexible and simple andthus, suitable to be used within a Spatial Data Infrastructure to provide access to realtimeair pollution information via the internet. In a prototypical implementation the modelis applied to estimate the concentration of particular matter and ozone in the FederalState of Saxony, Germany.