IDEAS home Printed from
   My bibliography  Save this article

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


  • Sujit K. Sahu
  • Kanti V. Mardia


Summary. 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.

Suggested Citation

  • Sujit K. Sahu & Kanti V. Mardia, 2005. "A Bayesian kriged Kalman model for short‐term forecasting of air pollution levels," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 223-244, January.
  • Handle: RePEc:bla:jorssc:v:54:y:2005:i:1:p:223-244

    Download full text from publisher

    File URL:
    Download Restriction: no


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Cui, Can & Wu, Teresa & Hu, Mengqi & Weir, Jeffery D. & Li, Xiwang, 2016. "Short-term building energy model recommendation system: A meta-learning approach," Applied Energy, Elsevier, vol. 172(C), pages 251-263.
    2. repec:taf:japsta:v:44:y:2017:i:1:p:89-108 is not listed on IDEAS
    3. repec:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9150-3 is not listed on IDEAS
    4. repec:eee:intfor:v:34:y:2018:i:4:p:566-581 is not listed on IDEAS
    5. Yi Liu & Gavin Shaddick & James V. Zidek, 0. "Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 0, pages 1-23.
    6. Moisan, Stella & Herrera, Rodrigo & Clements, Adam, 2018. "A dynamic multiple equation approach for forecasting PM2.5 pollution in Santiago, Chile," International Journal of Forecasting, Elsevier, vol. 34(4), pages 566-581.
    7. Sotirios Bersimis & Stavros Degiannakis & Dimitrios Georgakellos, 2017. "Real-time monitoring of carbon monoxide using value-at-risk measure and control charting," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 89-108, January.
    8. Jonas Wallin & David Bolin, 2015. "Geostatistical Modelling Using Non-Gaussian Matérn Fields," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 872-890, September.
    9. repec:bla:jtsera:v:38:y:2017:i:6:p:936-959 is not listed on IDEAS
    10. Sujit K. Sahu & Alan E. Gelfand & David M. Holland, 2010. "Fusing point and areal level space–time data with application to wet deposition," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 77-103, January.
    11. Dorel Paraschiv & Cristiana Tudor & Radu Petrariu, 2015. "The Textile Industry and Sustainable Development: A Holt–Winters Forecasting Investigation for the Eastern European Area," Sustainability, MDPI, Open Access Journal, vol. 7(2), pages 1-12, January.

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssc:v:54:y:2005:i:1:p:223-244. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley Content Delivery). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.