The University of Southampton
University of Southampton Institutional Repository

A Bayesian kriged Kalman model for short-term forecasting of air pollution levels.

Sahu, Sujit K. and Mardia, Kanti V. (2005) A Bayesian kriged Kalman model for short-term forecasting of air pollution levels. Journal of the Royal Statistical Society. Series C: Applied Statistics, 54, (1), pp. 223-244. (doi:10.1111/j.1467-9876.2005.00480.x).

Record type: Article


Short-term forecasts of air pollution levels in big cities are now reported in news-papers and other media outlets. Studies indicate that even short-term exposure to high levels of an air pollutant called atmospheric particulate matter can lead to long-term health effects. Data are typically observed at fixed monitoring stations throughout a study region of interest at different time points. Statistical spatiotemporal models are appropriate for modelling these data. We consider short-term forecasting of these spatiotemporal processes by using a Bayesian kriged Kalman filtering model.
The spatial prediction surface of the model is built by using the well-known method of kriging for optimum spatial prediction and the temporal effects are analysed by using the models underlying the Kalman filtering method. The full Bayesian model is implemented by using Markov chain Monte Carlo techniques which enable us to obtain the optimal Bayesian forecasts in time and space. A new cross-validation method based on the Mahalanobis distance between the forecasts and observed data is also developed to assess the forecasting performance of the model implemented.

Full text not available from this repository.

More information

Published date: 2005
Keywords: bending energy, gibbs sampler, kalman filter, kriging, markov chain monte carlo methods, spatial temporal modelling, state space model
Organisations: Statistics


Local EPrints ID: 30047
ISSN: 0035-9254
PURE UUID: fa0dcf1b-075f-481c-9a8b-f1ba15c81557
ORCID for Sujit K. Sahu: ORCID iD

Catalogue record

Date deposited: 12 May 2006
Last modified: 17 Jul 2017 15:55

Export record



Author: Sujit K. Sahu ORCID iD
Author: Kanti V. Mardia

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.