The fusion of meteorological- and air quality information for orchestrated services using environmental profiling

Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. Thus, there has been a growing demand for personalized information about air quality and weather, i.e., information that is tailored to citizens' specific context and background. A recent poll showed that in almost all EU member states the citizens would like to have easier access to air quality related information. To address this demand, an orchestrated web service (PESCaDO) has been developed. The PESCaDO system is designed to discover reliable environmental data in the web, to process these data in order to convert them into knowledge and to use this knowledge for the provision of human-centered information. In this paper, a description of the abovementioned process is given focusing on the fusion of environmental information into a coherent data block. We will show that with the presented methods, superior temperature forecasts with respect to individual service providers can be provided to the users of PESCaDO. Furthermore, by utilizing a land use mask, a population density mask and an array of historical measurements at various locations, the regionally calibrated fusion method can be used to produce accurate air pollutant concentration maps on a local scale. In particular, we will show that the hourly average NO2 concentration in a remote city center can be predicted accurately using measurements that are no less than 50 kilometers away from the location.