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.
Hierarchical spatio-temporal models for short-term predictions of air pollution data / Bruno F.; Paci L.. - ELETTRONICO. - (2013), pp. 1-4. (Intervento presentato al convegno SIS 2013 Statistical Conference. Advances in Latent Variables - Methods, Models and Applications tenutosi a Brescia - Department of Economics and Management nel June 19, 2013 – June 21, 2013).
Hierarchical spatio-temporal models for short-term predictions of air pollution data
BRUNO, FRANCESCA;PACI, LUCIA
2013
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
