Hierarchical spatio-temporal models for short-term predictions of air pollution data

Recently, many national Environmental Agencies are interested to provide the citizens and public health decision-makers with visual and easy access to air quality information. Short-term air pollution predictions are usually needed as high resolution spatial maps for environmental regulatory purposes. We develop a hierarchical spatio-temporalmodel to enable use of different sources of information in order to provide short-term air pollution forecasting.