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 \(\hbox {NO}_{2}\) 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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bivand R, Pebesma E, Gómez-Rubio V (2008) Applied spatial data analysis with R. Springer, New York
Bogaert P (1996) Comparison of kriging techniques in a space–time context. Math Geol 28:73–86
Bruno F, Guttorp P, Sampson P, Cocchi D (2009) A simple non-separable, non-stationary spatiotemporal model for ozone. Environ Ecol Stat 16:515–529
Calculli C, Fasso A, Finazzi F, Pollice A, Turnone A (2015) Maximum likelihood estimation of the multivariate hidden dynamic geostatistical model with application to air quality in Apulia, Italy. Environmetrics 26:406–417
Cameletti M, Ignaccolo R, Bande S (2011) Comparing spatio–temporal models for particulate matter in piemonte. Environmetrics 22:985–996
Carslaw DC (2005) Evidence of an increasing no2/nox emissions ratio from road traffic emissions. Atmos Environ 39:4793–4802
Cressie N (1990) The origins of kriging. Math Geol 22:239–252
Cressie N, Huang H (1999) Classes of nonseparable, spatio–temporal stationary covariance functions. J Am Stat Assoc 94:1330–1340
Cressie N, Wikle C (2011) Statistics for spatio–temporal data. Wiley, New York
De Cesare L, Myers D, Posa D (2001) Estimating and modeling space–time correlation structures. Stat Probab Lett 51:9–14
De Gruijter J, Brus D, Bierkens M, Knotters M (2006) Sampling for natural resource monitoring. Springer, Germany
De Iaco S, Posa D (2012) Predicting spatio–temporal random fields: some computational aspects. Comput Geosci 41:12–24
Dimitrakopoulos R, Luo X (1994) Spatiotemporal modeling: covariances and ordinary kriging systems. In: Dimitrakopoulos R (ed) Geostatistics for the next century. Kluwer Academic Publishers, Dordrecht, pp 88–93
Dimitrakopoulos R, Luo X (1997) Spatiotemporal modeling: covariances and ordinary kriging systems. In: Baafi E, Scofield N (eds) Geostatistics Wollongong’96. Kluwer Academic Publishers, Dordrecht, pp 138–149
Fernández-Casal R, González-Manteiga W, Febrero-Bande M (2003) Flexible spatio–temporal stationary variogram models. Stat Comput 13:127–136
Fox J (2008) Applied regression analysis and generalized linear models. SAGE Publications, Thousand Oaks
Gneiting T, Genton MG, Guttorp P (2007) Statistical methods for spatio–temporal systems. Chapman and Hall, Cambridge
Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York
Grice S, Stedman J, Kent A, Hobson M, Norris J, Abbott J, Cooke S (2009) Recent trends and projections of primary no2 emissions in europe. Atmos Environ 43:2154–2167
Heuvelink G, Griffith D (2010) Space-time geostatistics for geography: a case study of radiation monitoring across parts of Germany. Geogr Anal 42:161–179
Host G, Omre H, Switzer P (1995) Spatial interpolation errors for monitoring data. J Am Stat Assoc 90:853–861
Isaaks E, Srivastava R (1989) An introduction to applied geostatistics. Oxford University Press, New York
Kyriakidis P, Journel A (1999) Geostatistical space–time models: a review. Math Geol 31:651–684
Lewne M, Cyrys J, Meliefste K, Hoek G, Brauer M, Fischer P, Gehring U, Heinrich J, Brunekreef B, Bellander T (2004) Spatial variation in nitrogen dioxide in three European areas. Sci Total Environ 332:217–230
Lindley S, Walsh T (2005) Inter-comparison of interpolated background nitrogen dioxide concentrations across greater manchester, uk. Atmos Environ 39:2709–2724
Myers D (2004) Estimating and modeling space-time variograms. In: McRoberts R (ed) Proceedings of the joint meeting of TIES-2004 and ACCURACY-2004
Porcu E, Mateu J, Saura F (2008) New classes of covariance and spectral density functions for spatio–temporal modelling. Stoch Environ Res Risk Assess 22:65–79
R Team D (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing
Rodriguez-Iturbe I, Mejía J (1974) The design of rainfall networks in time and space. Water Resour Res 10:713–728
Rouhani S, Hall T (1989) Space–time kriging of groundwater data. In: Armstrong M (ed) Geostatistics. Kluwer Academic Publishers, Dordrecht, pp 639–651
Saiz-Lopez A, Adame J, Notario A, Poblete J, Bolívar J, Albaladejo J (2009) Year-round observations of no, no2, o3, so2 and toluene. Water Air Soil Pollut 200:277–288
Shaddick G, Yan H, Salway R, Vienneau D, Kounali D, Briggs D (2013) Large-scale bayesian spatial modelling of air pollution for policy support. J Appl Stat 40:777–794
Stedman J, Goodwin J, King K, Murrells T, Bush T (2001) An empirical model for predicting urban roadside nitrogen dioxide concentrations in UK. Atmos Environ 35:1451–1463
Stein M (2005) Space-time covariance functions. J Am Stat Assoc 469:310–320
Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc B 36:111–133
WHO (2003) Health aspects of air pollution with particulate matter, ozone and nitrogen dioxide. World Health Organization, Germany
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Menezes, R., Piairo, H., García-Soidán, P. et al. Spatial–temporal modellization of the \(\hbox {NO}_{2}\) concentration data through geostatistical tools. Stat Methods Appl 25, 107–124 (2016). https://doi.org/10.1007/s10260-015-0346-3
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10260-015-0346-3