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

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  • Sujit K. Sahu
  • Kanti V. Mardia

Abstract

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
    DOI: 10.1111/j.1467-9876.2005.00480.x
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    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. 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.
    3. 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.
    4. 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.
    5. Xiang Xu, 2020. "Forecasting air pollution PM2.5 in Beijing using weather data and multiple kernel learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 117-125, March.
    6. K. Shuvo Bakar, 2020. "Interpolation of daily rainfall data using censored Bayesian spatially varying model," Computational Statistics, Springer, vol. 35(1), pages 135-152, March.
    7. T. Subba Rao & Gyorgy Terdik, 2017. "A New Covariance Function and Spatio-Temporal Prediction (Kriging) for A Stationary Spatio-Temporal Random Process," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(6), pages 936-959, November.
    8. 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.
    9. Alexander Kreuzer & Luciana Dalla Valle & Claudia Czado, 2022. "A Bayesian non‐linear state space copula model for air pollution in Beijing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 613-638, June.
    10. 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, vol. 7(2), pages 1-12, January.
    11. 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.
    12. Ping Zhang & Bo Hong & Liang He & Fei Cheng & Peng Zhao & Cailiang Wei & Yunhui Liu, 2015. "Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural N," IJERPH, MDPI, vol. 12(10), pages 1-25, September.

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