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A spatiotemporal model for Mexico City ozone levels

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  • Gabriel Huerta
  • Bruno Sansó
  • Jonathan R. Stroud

Abstract

Summary. We consider hourly readings of concentrations of ozone over Mexico City and propose a model for spatial as well as temporal interpolation and prediction. The model is based on a time‐varying regression of the observed readings on air temperature. Such a regression requires interpolated values of temperature at locations and times where readings are not available. These are obtained from a time‐varying spatiotemporal model that is coupled to the model for the ozone readings. Two location‐dependent harmonic components are added to account for the main periodicities that ozone presents during a given day and that are not explained through the covariate. The model incorporates spatial covariance structure for the observations and the parameters that define the harmonic components. Using the dynamic linear model framework, we show how to compute smoothed means and predictive values for ozone. We illustrate the methodology on data from September 1997.

Suggested Citation

  • Gabriel Huerta & Bruno Sansó & Jonathan R. Stroud, 2004. "A spatiotemporal model for Mexico City ozone levels," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(2), pages 231-248, April.
    • Handle: RePEc:bla:jorssc:v:53:y:2004:i:2:p:231-248
    DOI: 10.1046/j.1467-9876.2003.05100.x
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    Cited by:

    1. Andrew Hoegh & Scotland Leman, 2015. "A spatio-temporal model for assessing winter damage risk to east coast vineyards," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 834-845, April.
    2. Kyu Jong Lee & Hyungu Kahng & Seoung Bum Kim & Sun Kyoung Park, 2018. "Improving Environmental Sustainability by Characterizing Spatial and Temporal Concentrations of Ozone," Sustainability, MDPI, vol. 10(12), pages 1-11, December.
    3. 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.
    4. Temiyasathit, Chivalai & Kim, Seoung Bum & Park, Sun-Kyoung, 2009. "Spatial prediction of ozone concentration profiles," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3892-3906, September.
    5. Meraz, M. & Alvarez-Ramirez, J. & Echeverria, J.C., 2017. "Asymmetric correlations in the ozone concentration dynamics of the Mexico City Metropolitan Area," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 377-386.
    6. Marcelo Cunha & Dani Gamerman & Montserrat Fuentes & Marina Paez, 2017. "A non-stationary spatial model for temperature interpolation applied to the state of Rio de Janeiro," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(5), pages 919-939, November.
    7. Stefano F. Tonellato, 2005. "Identifiability Conditions for Spatio-Temporal Bayesian Dynamic Linear Models," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 81-101.
    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. GEMOETS, DARREN Ethan, 2022. "Hierarchical Bayesian Approach to Fitting Discretized Partial Differential Equation Models to Spatial-temporal Data: The Effects of Numerical Instability on Parameter Estimates," OSF Preprints srg2t, Center for Open Science.
    10. Rivera, Roberto, 2016. "A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data," Tourism Management, Elsevier, vol. 57(C), pages 12-20.

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