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Spatial–temporal modellization of the $$\hbox {NO}_{2}$$ NO 2 concentration data through geostatistical tools

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  • Raquel Menezes

    (University of Minho)

  • Helena Piairo

    (University of Minho)

  • Pilar García-Soidán

    (University of Vigo)

  • Inês Sousa

    (University of Minho)

Abstract

The nitrogen dioxide is a primary pollutant, regarded for the estimation of the air quality index, whose excessive presence may cause significant environmental and health problems. In the current work, we suggest characterizing the evolution of $$\hbox {NO}_{2}$$ NO 2 levels, by using geostatistical approaches that deal with both the space and time coordinates. To develop our proposal, a first exploratory analysis was carried out on daily values of the target variable, daily measured in Portugal from 2004 to 2012, which led to identify three influential covariates (type of site, environment and month of measurement). In a second step, appropriate geostatistical tools were applied to model the trend and the space–time variability, thus enabling us to use the kriging techniques for prediction, without requiring data from a dense monitoring network. This methodology has valuable applications, as it can provide accurate assessment of the nitrogen dioxide concentrations at sites where either data have been lost or there is no monitoring station nearby.

Suggested Citation

  • Raquel Menezes & Helena Piairo & Pilar García-Soidán & Inês Sousa, 2016. "Spatial–temporal modellization of the $$\hbox {NO}_{2}$$ NO 2 concentration data through geostatistical tools," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 107-124, March.
    • Handle: RePEc:spr:stmapp:v:25:y:2016:i:1:d:10.1007_s10260-015-0346-3
    DOI: 10.1007/s10260-015-0346-3
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    References listed on IDEAS

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    1. 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.
    2. Michael L. Stein, 2005. "Space-Time Covariance Functions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 310-321, March.
    3. 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.
    4. Cesare, L. De & Myers, D. E. & Posa, D., 2001. "Estimating and modeling space-time correlation structures," Statistics & Probability Letters, Elsevier, vol. 51(1), pages 9-14, January.
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