×

European population exposure to airborne pollutants based on a multivariate spatio-temporal model. (English) Zbl 1347.62242

Summary: 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.

MSC:

62P12 Applications of statistics to environmental and related topics

Software:

Stem; D-STEM
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Beelen R., Hoek G., Pebesma E., Vienneau D., de Hoogh K., Briggs D. (2009), Mapping of background air pollution at a fine spatial scale across the European Union, Sci. Total Environ. 407(6), 1852-1867. · doi:10.1016/j.scitotenv.2008.11.048
[2] Bevilacqua M., Fassò A., Gaetan C., Porcu E., Velandia D. (2016), Covariance tapering for multivariate Gaussian random fields estimation. Statistical Methods and Applications. 25(1), 21-37. · Zbl 1416.62550 · doi:10.1007/s10260-015-0338-3
[3] Bourotte M., Allard D., Porcu E. (2016), A flexible class of non-separable cross-covariance functions for multivariate space-time data. Spatial Statistics. doi:10.1016/j.spasta.2016.02.004
[4] Box G.E., Cox D.R. (1964). An analysis of transformations. Journal of the Royal Statistical Society. Series B, 26(2) 211-252. · Zbl 0156.40104
[5] Calculli C., Fassò A., Finazzi F., Pollice A. and Turnone A. (2015), Maximum likelihood estimation of the multivariate hidden dynamic geostatistical model with application to air quality in Apulia, Italy. Environmetrics, 26(6), 406-417. · Zbl 1525.62086 · doi:10.1002/env.2345
[6] Calder C. A. (2008), A dynamic process convolution approach to modeling ambient particulate matter concentrations. Environmetrics, 19(1), 39-48. · doi:10.1002/env.852
[7] Cameletti M., Ignaccolo R., Bande S. (2011), Comparing spatio-temporal models for particulate matter in Piemonte. Environmetrics, 22, 985-996. · doi:10.1002/env.1139
[8] Chunfeng H., Haimeng Z., Scott M.R. (2009), On the validity of commonly used covariance and variogram functions on the sphere. Mathematical Geosciences, 43, 721-733. · Zbl 1219.86015
[9] De Iaco S., Palma M., Posa D. (2013). Prediction of particle pollution through spatio-temporal multivariate geostatistical analysis: spatial special issue. Advances in Statistical Analysis, 97(2), 133-150. · Zbl 1443.62402 · doi:10.1007/s10182-012-0199-0
[10] De Oliveira V., Kedem B., Short D. A. (1997), Bayesian prediction of transformed Gaussian random fields. Journal of the American Statistical Association, 92(440), 1422-1433. · Zbl 0919.62020
[11] Dobson J.E., Edward A. Brlght, Coleman P.R., Durfee R, and Worley B.A. (2000), LandScan: A Global Population Database for Estimating Populations at Risk, Photogrammetric Engineering & Remote Sensing, 66(7) 849-857.
[12] EEA (2015), Air quality in Europe - 2015 report, European Environmental Agency publications, doi:10.2800/62459.
[13] Fassò A., Finazzi F. (2011) , Maximum likelihood estimation of the dynamic coregionalization model with heterotopic data, Environmetrics, 22(6), 735-748. · doi:10.1002/env.1123
[14] Finazzi F., Fassò A. (2014), D-STEM: A Software for the Analysis and Mapping of Environmental Space-Time Variables, Journal of Statistical Software. 62(6), 1-29. · doi:10.18637/jss.v062.i06
[15] Finazzi F., Scott M.E., Fassò A. (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, 62(2), 287-308. · doi:10.1111/rssc.12001
[16] Jahn H.K., Kraemer A., Chen X.C., Chan C.Y., Engling G., Ward T.J. (2013), Ambient and personal PM2.5 exposure assessment in the Chinese megacity of Guangzhou, Atmospheric Environment, 74, 402-411. · Zbl 1219.86015
[17] McBride S.J., Williams R.W., Creason J. (2007), Bayesian hierarchical modeling of personal exposure to particulate matter. Atmospheric Environment, 41(29), 6143-6155. · Zbl 1441.62865
[18] McMillan N. J., Holland D. M., Morara M., Feng J. (2010). Combining numerical model output and particulate data using Bayesian space-time modeling. Environmetrics, 21(1), 48-65.
[19] McLachlan G., Krishnan T. (2008), The EM Algorithm and Extensions, 2nd Edition, Wiley, New York. · Zbl 1165.62019 · doi:10.1002/9780470191613
[20] Pollice A., Jona Lasinio G. (2010), A multivariate approach to the analysis of air quality in a high environmental risk area. Environmetrics, 21(7-8), 741-754. · doi:10.1002/env.1059
[21] Rister K., Lahiri S.N. (2013) , Bootstrap based Trans-Gaussian Kriging, Statistical Modelling, 13, 509-539. · Zbl 07257470 · doi:10.1177/1471082X13494614
[22] Sahu S. K., Gelfand A. E., Holland D. M. (2006), Spatio-temporal modeling of fine particulate matter. Journal of Agricultural, Biological, and Environmental Statistics, 11(1), 61-86. · doi:10.1198/108571106X95746
[23] Secchi P., Vantini S., Vitelli V. (2015) , Analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan, Statistical Methods and Applications, 24, 279-300. · Zbl 1441.62865 · doi:10.1007/s10260-014-0294-3
[24] Shaddick G., Yan H., Salway R., Vienneau D., Kounali D. and Briggs D. (2013) , Large-scale Bayesian spatial modelling of air pollution for policy support, Journal of Applied Statistics, 40: 4,777-4,794 · Zbl 1514.62857 · doi:10.1080/02664763.2012.754851
[25] Wolff G. T., Kahlbaum D. F., Heuss, J. M. (2013), The vanishing ozone weekday/weekend effect. Journal of the Air & Waste Management Association, 63(3), 292-299. · doi:10.1080/10962247.2012.749312
[26] Zidek J. V., Shaddick G., White R., Meloche J., Chatfield C. (2005), Using a probabilistic model (pCNEM) to estimate personal exposure to air pollution. Environmetrics, 16(5), 481-493. · doi:10.1002/env.716
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.