This work deals with the spatio-temporal analysis of urban air pollution dynamics from the town of Perugia, Italy, using high-frequency and size resolved data on particular matter. Hierarchical Bayesian models are used that allow for an autoregressive term in time. Some preliminary results show that there is a significant spatio–temporal structure with a large first–order temporal correlation coefficient. Future analysis will concern the use of higher–order temporal auto–correlation structures and the introduction of the effect of some covariates.

(2014). Bayesian spatio–temporal modeling of urban air pollution dynamics [conference presentation - intervento a convegno]. Retrieved from http://hdl.handle.net/10446/31664

Bayesian spatio–temporal modeling of urban air pollution dynamics

2014-01-01

Abstract

This work deals with the spatio-temporal analysis of urban air pollution dynamics from the town of Perugia, Italy, using high-frequency and size resolved data on particular matter. Hierarchical Bayesian models are used that allow for an autoregressive term in time. Some preliminary results show that there is a significant spatio–temporal structure with a large first–order temporal correlation coefficient. Future analysis will concern the use of higher–order temporal auto–correlation structures and the introduction of the effect of some covariates.
2014
DEL SARTO, S.; Ranalli, G. M.; SHUVO BAKAR, K.; Cappelletti, D.; Moroni, B.; Crocchianti, S.; Castellini, S.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/31664
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