Fusion of meteorological and air quality data extracted from the web for personalized environmental information services

There is a large amount of meteorological and air quality data available online. Often, different sources provide deviating and even contradicting data for the same geographical area and time. This implies that users need to evaluate the relative reliability of the information and then trust one of the sources. We present a novel data fusion method that merges the data from different sources for a given area and time, ensuring the best data quality. The method is a unique combination of land-use regression techniques, statistical air quality modelling and a well-known data fusion algorithm. We show experiments where a fused temperature forecast outperforms individual temperature forecasts from several providers. Also, we demonstrate that the local hourly NO2 concentration can be estimated accurately with our fusion method while a more conventional extrapolation method falls short. The method forms part of the prototype web-based service PESCaDO, designed to cater personalized environmental information to users. We introduce PESCaDO, a service designed to cater personalized environmental information.We present a novel data fusion method that merges data from different sources for a given area and time.With the presented fusion method the hourly concentration of air quality pollutants can be assessed in varying urban environments.We present a system called AirMerge, which converts image-based concentration maps into numerical data for fusion.