Abstract
Radon is a natural radioactive gas known to be the main contributor to natural background radiation exposure and the major leading cause of lung cancer second to smoking. Indoor radon concentration levels of 200 and 400 Bq/m3 are reference values suggested by the 90/143/Euratom recommendation, above which mitigation measures should be taken in new and old buildings, respectively, to reduce exposure to radon. Despite this international recommendation, Italy still does not have mandatory regulations or guidelines to deal with radon in dwellings. Monitoring surveys have been undertaken in a number of western European countries in order to assess the exposure of people to this radioactive gas and to identify radon prone areas. However, such campaigns provide concentration values in each single dwelling included in the sample, while it is often necessary to provide measures of the pollutant concentration which refer to sub-areas of the region under study. This requires a realignment of the spatial data from the level at which they are collected (points) to the level at which they are necessary (areas). This is known as change of support problem.In this paper, we propose a methodology based on geostatistical simulations in order to solve this problem and to identify radon prone areas which may be suggested for national guidelines.
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References
Apte MG, Price PN, Nero AV, Revzan KL (1999) Predicting New Hampshire indoor radon concentrations from geologic information and other covariates. Environ Geol 37: 181–194
Arbia G (1989) Statistical effect of data transformations: a proposed general framework. In: Goodchild M, Gopal S (eds) The accuracy of spatial data bases. Taylor and Francis, London, pp 249–259
Arbia G, Lafratta G (2002) Exploring nonlinear spatial dependence in the tails. Geogr Anal 37: 423–437
Bochicchio F, Campos Venuti G, Nuccetelli C, Piermattei S, Risica S, Tommasino L, Torri G (1996) Results of the representative Italian National Survey on radon indoors. Health Phys 17(5): 741–748
Bossew P, Dubois G, Tollefsen T (2008) Geological classes as categorical external drift for spatial modelling of the radon potential. J Environ Radioact 99(1): 81–97
Chilès D, Delfiner P (1999) Geostatistics modeling spatial uncertainty. Wiley, New York
Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 74: 829–836
Cramér CH (1930) On the mathematical theory of risk, Forsakringsaktiebolaget Skandias Festskrift. Centraltryckeriet, Stockholm
Cressie N (1993) Statistics for spatial data. Wiley, New York
de Bartolo D, Alberici A, Gallini R, Maggioni T, Arrigoni S, Cazzaniga P, Cugini A, Olivieri F, Romanelli M, Gallinari G (2005) Piano di monitoraggio per l’individuazione della radon prone areas nella regione Lombardia. Atti del convegno AIRP, Catania 15–17 settembre 2005
Diggle JD, Ribeiro PJ (2007) Model-based geostatistics. Springer, New York
Dubois G (2005) An overview of radon surveys in Europe. EUR 21892 EN, EC. Office for Official Publication of the European Communities, Luxembourg, p 168
Dubois G, Bossew P, Friedmann H (2007) A geostatistical autopsy of the Austrian indoor radon survey (1992–2002). Sci Total Environ 377(2–3): 378–395
Emery X (2005) Variograms of order ω: a tool to validate a bivariate distribution model. Math Geol 37: 163–181
Emery X (2006) Ordinary multigaussian kriging for mapping conditional probabilities of soil properties. Geoderma 132: 75–88
Emery X, Ortiz JM (2005) Histogram and variogram inference in the multigaussian model. Stoch Environ Res Risk Assess 19: 48–58
Franco-Marina F, Villalba-Caloca J, Segovia N, Tavera L (2003) Spatial indoor radon distribution in Mexico City. Sci Total Environ 317: 91–103
Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York
Gotway CA, Young LJ (2002) Combining incompatible spatial data. J Am Stat Assoc 97(458): 632–648
Gunby JA, Darby SC, Miles JCH, Green BMR, Cox DR (1993) Factors affecting indoor radon concentrations in the United Kingdom. Health Phys 64: 2–12
Hall P, DiCiccio TJ, Romano JP (1989) On smoothing and the bootstrap. Ann Stat 17: 692–704
Hastie TJ, Tibshirani RJ (1990) Generalized additive models. Chapman & Hall, London
Matheron G (1971) The theory of regionalised random variables and its applications. Report, vol 5. Centre de Morphologie Mathématique de Fontainebleau, Ecole des Mines de Paris
Murphy P, Organo C (2008) A comparative study of lognormal, gamma and beta modelling in radon mapping with recommendations regarding bias, sample sizes and the treatment of outliers. J Radiol Prot 28(3): 293–302
Nero AV, Schwehr MB, Nazaroff WW, Revzan KL (1986) Distribution of airborne radon-222 concentrations in US homes. Science 234: 992–997
Price PN, Nero AV, Gelman A (1996) Bayesian prediction of mean indoor radon concentrations for Minnesota counties. Health Phys 71: 922–936
Regione Veneto, ARPAV (2000) Indagine regionale per l’individuazione delle aree ad alto potenziale di radon nel territorio veneto. http://www.arpa.veneto.it/pubblicazioni/htm/scheda_pub.asp?id=34. Last accessed on July 9 2009.
Sesana L, Polla G, Facchini U, de Capitani L (2005) Radon-prone areas in the Lombard plain. J Environ Radioact 82: 51–62
Smith BJ, Cowles MK (2007) Correlating point-referenced radon and areal uranium data arising from a common spatial process. JRSS Ser C 56: 313–326
Smith BJ, Field RW (2007) Effect of housing factor and suficial uranium on the spatial prediction of residential radon in Iowa. Environmetrics 18: 481–497
Tuia D, Kanevski M (2008) Indoor radon distribution in Switzerland: lognormality and extreme value theory. J Environ Radioact 99(4): 649–657
Verly G (1983) The multi-gaussian approach and its applications to the estimation of local reserves. J Int Assoc Math Geol 15: 259–286
Zhu H-C, Charlet JM, Poffijn A (2001) Radon risk mapping in southern Belgium: an application of geostatistical and GIS techniques. Sci Total Environ 272: 203–210
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Borgoni, R., Quatto, P., Somà, G. et al. A geostatistical approach to define guidelines for radon prone area identification. Stat Methods Appl 19, 255–276 (2010). https://doi.org/10.1007/s10260-009-0128-x
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DOI: https://doi.org/10.1007/s10260-009-0128-x