Recently, the interest of many environmental agencies is on short-term air pollution predictions referred at high spatial resolution. This permits citizens and public health decision-makers to be informed with visual and easy access to air-quality assessment. We propose a hierarchical spatiotemporal model to enable use of different sources of information to provide short-term air pollution forecasting. In particular, we combine monitoring data and numerical model output in order to obtain short-term ozone forecasts over the Emilia Romagna region where the orography plays an important role on the air pollution; thus, the elevation is also included in the model. We provide high-resolution spatial forecast maps and uncertainty associated with these predictions. The assessment of the predictive performance of the model is based upon a site-one-out cross-validation experiment. © Springer International Publishing Switzerland 2014.

Bruno, F., Paci, L., Springer Proceedings in Mathematics and Statistics, in Lanzarone, E., Ieva Francesc, I. F. (ed.), The contribution of Young Researchers to Bayesian Statistics - Proceedings of BAYSM2013, Springer International Publishing, Cham 2014: 91- 94. 10.1007/978-3-319-02084-6__18 [http://hdl.handle.net/10807/98613]

Springer Proceedings in Mathematics and Statistics

Bruno, Francesca
Primo
;
Paci, Lucia
2014

Abstract

Recently, the interest of many environmental agencies is on short-term air pollution predictions referred at high spatial resolution. This permits citizens and public health decision-makers to be informed with visual and easy access to air-quality assessment. We propose a hierarchical spatiotemporal model to enable use of different sources of information to provide short-term air pollution forecasting. In particular, we combine monitoring data and numerical model output in order to obtain short-term ozone forecasts over the Emilia Romagna region where the orography plays an important role on the air pollution; thus, the elevation is also included in the model. We provide high-resolution spatial forecast maps and uncertainty associated with these predictions. The assessment of the predictive performance of the model is based upon a site-one-out cross-validation experiment. © Springer International Publishing Switzerland 2014.
Inglese
The contribution of Young Researchers to Bayesian Statistics - Proceedings of BAYSM2013
9783319020839
Springer International Publishing
Bruno, F., Paci, L., Springer Proceedings in Mathematics and Statistics, in Lanzarone, E., Ieva Francesc, I. F. (ed.), The contribution of Young Researchers to Bayesian Statistics - Proceedings of BAYSM2013, Springer International Publishing, Cham 2014: 91- 94. 10.1007/978-3-319-02084-6__18 [http://hdl.handle.net/10807/98613]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10807/98613
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