Modelling Daily Multivariate Pollutant Data at Multiple Sites
- 1 July 2002
- journal article
- Published by Oxford University Press (OUP) in Journal of the Royal Statistical Society Series C: Applied Statistics
- Vol. 51 (3), 351-372
- https://doi.org/10.1111/1467-9876.00273
Abstract
Summary: 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.Keywords
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