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Modelling daily multivariate pollutant data at multiple sites

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  • Gavin Shaddick
  • Jon Wakefield

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.

Suggested Citation

  • Gavin Shaddick & Jon Wakefield, 2002. "Modelling daily multivariate pollutant data at multiple sites," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(3), pages 351-372, July.
    • Handle: RePEc:bla:jorssc:v:51:y:2002:i:3:p:351-372
    DOI: 10.1111/1467-9876.00273
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    Cited by:

    1. Serena Arima & Lorenza Cretarola & Giovanna Jona Lasinio & Alessio Pollice, 2012. "Bayesian univariate space-time hierarchical model for mapping pollutant concentrations in the municipal area of Taranto," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 75-91, March.
    2. Yi Liu & Gavin Shaddick & James V. Zidek, 2017. "Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 559-581, December.
    3. David O'Donnell & Alastair Rushworth & Adrian W. Bowman & E. Marian Scott & Mark Hallard, 2014. "Flexible regression models over river networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 47-63, January.
    4. Guowen Huang & Patrick E. Brown & Sze Hang Fu & Hwashin Hyun Shin, 2022. "Daily mortality/morbidity and air quality: Using multivariate time series with seasonally varying covariances," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 148-174, January.
    5. C. Forlani & S. Bhatt & M. Cameletti & E. Krainski & M. Blangiardo, 2020. "A joint Bayesian space–time model to integrate spatially misaligned air pollution data in R‐INLA," Environmetrics, John Wiley & Sons, Ltd., vol. 31(8), December.
    6. Duncan Lee & Gavin Shaddick, 2010. "Spatial Modeling of Air Pollution in Studies of Its Short-Term Health Effects," Biometrics, The International Biometric Society, vol. 66(4), pages 1238-1246, December.

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