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.

Bruno F., Paci L. (2014). Spatiotemporal Model for Short-Term Predictions of Air Pollution Data. Cham : Springer International Publishing [10.1007/978-3-319-02084-6_18].

Spatiotemporal Model for Short-Term Predictions of Air Pollution Data

BRUNO, FRANCESCA;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.
2014
The Contribution of Young Researchers to Bayesian Statistics
91
94
Bruno F., Paci L. (2014). Spatiotemporal Model for Short-Term Predictions of Air Pollution Data. Cham : Springer International Publishing [10.1007/978-3-319-02084-6_18].
Bruno F.; Paci L.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/231271
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