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
|
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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