In this paper we analyze a data set of daily PM10 concentrations in north Italy for four months of 2003. The data set contained observations from two PM10 monitoring networks one measuring Low Volume sampler Gravimetric (LVG) and the other a tapered element oscillating microbalance (TEOM). We develop a flexible hierarchical Bayesian spatio-temporal model which includes seasonal (winter and summer) e_ects. The fully Bayesian model is implemented, using MCMC techniques, which enables full inference with regard to process unknowns, calibration, validation and predictions in time and space.
A spatio-temporal model for particulate matter monitored from a heterogeneous network
NICOLIS, Orietta
2006-01-01
Abstract
In this paper we analyze a data set of daily PM10 concentrations in north Italy for four months of 2003. The data set contained observations from two PM10 monitoring networks one measuring Low Volume sampler Gravimetric (LVG) and the other a tapered element oscillating microbalance (TEOM). We develop a flexible hierarchical Bayesian spatio-temporal model which includes seasonal (winter and summer) e_ects. The fully Bayesian model is implemented, using MCMC techniques, which enables full inference with regard to process unknowns, calibration, validation and predictions in time and space.File allegato/i alla scheda:
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