IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v25y2016i1d10.1007_s10260-015-0346-3.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-015-0346-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10260-015-0346-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Michael L. Stein, 2005. "Space-Time Covariance Functions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 310-321, March.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Villez, Kris & Del Giudice, Dario & Neumann, Marc B. & Rieckermann, Jörg, 2020. "Accounting for erroneous model structures in biokinetic process models," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    4. Alexandre Rodrigues & Peter J. Diggle, 2010. "A Class of Convolution‐Based Models for Spatio‐Temporal Processes with Non‐Separable Covariance Structure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 553-567, December.
    5. Marco Minozzo & Luca Bagnato, 2021. "A unified skew‐normal geostatistical factor model," Environmetrics, John Wiley & Sons, Ltd., vol. 32(4), June.
    6. Maria Lucia Parrella & Giuseppina Albano & Cira Perna & Michele La Rocca, 2021. "Bootstrap joint prediction regions for sequences of missing values in spatio-temporal datasets," Computational Statistics, Springer, vol. 36(4), pages 2917-2938, December.
    7. Moreno Bevilacqua & Alfredo Alegria & Daira Velandia & Emilio Porcu, 2016. "Composite Likelihood Inference for Multivariate Gaussian Random Fields," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 448-469, September.
    8. Frank Davenport, 2017. "Estimating standard errors in spatial panel models with time varying spatial correlation," Papers in Regional Science, Wiley Blackwell, vol. 96, pages 155-177, March.
    9. Daniel Griffith, 2010. "Modeling spatio-temporal relationships: retrospect and prospect," Journal of Geographical Systems, Springer, vol. 12(2), pages 111-123, June.
    10. Marcus L. Nascimento & Kelly C. M. Gonçalves & Mario Jorge Mendonça, 2023. "Spatio-Temporal Instrumental Variables Regression with Missing Data: A Bayesian Approach," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 29-47, June.
    11. Guillermo Ferreira & Jorge Mateu & Emilio Porcu, 2018. "Spatio-temporal analysis with short- and long-memory dependence: a state-space approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 221-245, March.
    12. Paolo Maranzano & Alessandro Fassò & Matteo Pelagatti & Manfred Mudelsee, 2020. "Statistical Modeling of the Early-Stage Impact of a New Traffic Policy in Milan, Italy," IJERPH, MDPI, vol. 17(3), pages 1-22, February.
    13. Christopher Wikle & Mevin Hooten, 2010. "A general science-based framework for dynamical spatio-temporal models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 417-451, November.
    14. Richardson, Robert & Kottas, Athanasios & Sansó, Bruno, 2017. "Flexible integro-difference equation modeling for spatio-temporal data," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 182-198.
    15. P. Gregori & E. Porcu & J. Mateu & Z. Sasvári, 2008. "On potentially negative space time covariances obtained as sum of products of marginal ones," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 865-882, December.
    16. McDonald, C.P. & Bennington, V. & Urban, N.R. & McKinley, G.A., 2012. "1-D test-bed calibration of a 3-D Lake Superior biogeochemical model," Ecological Modelling, Elsevier, vol. 225(C), pages 115-126.
    17. Paolo Maranzano & Matteo Maria Pelagatti, 2022. "Spatio-temporal Event Studies for Air Quality Assessment under Cross-sectional Dependence," Papers 2210.17529, arXiv.org.
    18. Alain, Boudou & Sylvie, Viguier-Pla, 2014. "Structure of the random measure associated with an isotropic stationary process," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 111-128.
    19. Ma, Chunsheng, 2003. "Nonstationary covariance functions that model space-time interactions," Statistics & Probability Letters, Elsevier, vol. 61(4), pages 411-419, February.
    20. Anup Suryawanshi & Debraj Ghosh, 2015. "Wind speed prediction using spatio-temporal covariance," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 1435-1449, January.

    Corrections

    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:spr:stmapp:v:25:y:2016:i:1:d:10.1007_s10260-015-0346-3. See general information about how to correct material in RePEc.

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.