Modelling daily multivariate pollutant data at multiple sites

Shaddick, G ORCID: https://orcid.org/0000-0002-4117-4264 and Wakefield, J 2002. Modelling daily multivariate pollutant data at multiple sites. Journal of the Royal Statistical Society: Series C 51 (3) , pp. 351-372. 10.1111/1467-9876.00273

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Abstract

This paper considers the spatiotemporal modelling of four pollutants measured daily at eight monitoring sites in London over a 4-year period. Such multiple-pollutant data sets measured over time at multiple sites within a region of interest are typical. Here, the modelling was carried out to provide the exposure for a study investigating the health effects of air pollution. Alternative objectives include the design problem of the positioning of a new monitoring site, or for regulatory purposes to determine whether environmental standards are being met. In general, analyses are hampered by missing data due, for example, to a particular pollutant not being measured at a site, a monitor being inactive by design (e.g. a 6-day monitoring schedule) or because of an unreliable or faulty monitor. Data of this type are modelled here within a dynamic linear modelling framework, in which the dependences across time, space and pollutants are exploited. Throughout the approach is Bayesian, with implementation via Markov chain Monte Carlo sampling.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	?? VCO ??
Publisher:	Royal Statistical Society
ISSN:	0035-9254
Date of Acceptance:	1 January 2001
Last Modified:	09 Aug 2024 13:45
URI:	https://orca.cardiff.ac.uk/id/eprint/170778

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