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European Population Exposure to Airborne Pollutants Based on a Multivariate Spatio-Temporal Model

Author

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  • Alessandro Fassò

    (University of Bergamo)

  • Francesco Finazzi

    (University of Bergamo)

  • Ferdinand Ndongo

    (University of Bergamo)

Abstract

In this paper, we estimate the distribution of population by exposure to multiple airborne pollutants, taking into account the spatio-temporal variability of daily air quality and the high-resolution spatial spread of human population around Europe. In particular, we consider monitoring network data for five pollutants, namely carbon monoxide, nitrogen dioxide, ozone, coarse and fine particulate matters. The spatial information contained in the large dataset of daily continental air quality is exploited using a multivariate spatio-temporal model capable to cover cross correlation among pollutants, covariates, and missing data as well as spatial and temporal variability and correlation. At the same time, the model is simple enough to be feasible for the large dataset of daily continental air quality over three years. Maximum likelihood estimation is performed using the EM algorithm, and kriging-like spatial estimates are used to compute high-resolution exposure distribution. Moreover, a novel semi-parametric bootstrap technique is used to assess the exposure distribution uncertainty. In this way, we compare the daily population exposure of 33 European countries and three important metropolitan areas in years 2009–2011 using a single flexible model. Extensive tabulations and graphs are reported in the supplementary material.

Suggested Citation

  • Alessandro Fassò & Francesco Finazzi & Ferdinand Ndongo, 2016. "European Population Exposure to Airborne Pollutants Based on a Multivariate Spatio-Temporal Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 492-511, September.
    • Handle: RePEc:spr:jagbes:v:21:y:2016:i:3:d:10.1007_s13253-016-0260-7
    DOI: 10.1007/s13253-016-0260-7
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    References listed on IDEAS

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    1. Piercesare Secchi & Simone Vantini & Valeria Vitelli, 2015. "Rejoinder to the discussion of “Analysis of Spatio-Temporal Mobile Phone Data: a Case Study in the Metropolitan Area of Milan”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 335-338, July.
    2. S. De Iaco & M. Palma & D. Posa, 2013. "Prediction of particle pollution through spatio-temporal multivariate geostatistical analysis: spatial special issue," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(2), pages 133-150, April.
    3. M. Bevilacqua & A. Fassò & C. Gaetan & E. Porcu & D. Velandia, 2016. "Covariance tapering for multivariate Gaussian random fields estimation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 21-37, March.
    4. Piercesare Secchi & Simone Vantini & Valeria Vitelli, 2015. "Analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 279-300, July.
    5. Gavin Shaddick & Haojie Yan & Ruth Salway & Danielle Vienneau & Daphne Kounali & David Briggs, 2013. "Large-scale Bayesian spatial modelling of air pollution for policy support," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(4), pages 777-794.
    6. Crescenza Calculli & Alessandro Fassò & Francesco Finazzi & Alessio Pollice & Annarita Turnone, 2015. "Maximum likelihood estimation of the multivariate hidden dynamic geostatistical model with application to air quality in Apulia, Italy," Environmetrics, John Wiley & Sons, Ltd., vol. 26(6), pages 406-417, September.
    7. Finazzi, Francesco & Fassò, Alessandro, 2014. "D-STEM: A Software for the Analysis and Mapping of Environmental Space-Time Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i06).
    8. Francesco Finazzi & E. Marian Scott & Alessandro Fassò, 2013. "A model-based framework for air quality indices and population risk evaluation, with an application to the analysis of Scottish air quality data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(2), pages 287-308, March.
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    Cited by:

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    2. J. Mateu & E. Porcu, 2016. "Guest Editors’ Introduction to the Special Issue on “Seismomatics: Space–Time Analysis of Natural or Anthropogenic Catastrophes”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 403-406, September.

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