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A new geostatistical tool for the analysis of the geographical variability of the indoor radon activity

Treść / Zawartość
Identyfikatory
Warianty tytułu
Konferencja
III International Conference „Radon in the Environment” (3 ; 27-31 May 2019 ; Krakow, Poland)
Języki publikacji
EN
Abstrakty
EN
The population is continuously exposed to a background level of ionizing radiation due to the natural radioactivity and, in particular, with radon (222Rn). Radon gas has been classified as the second leading cause of lung cancer after tobacco smoke [1]. In the confined environment, radon concentration can reach harmful level and vary accordingly to many factors. Since the primary source of radon in dwellings is the subsurface, the risk assessment and reduction cannot disregard the identification of the local geology and the environmental predisposing factors. In this article, we propose a new methodology, based on the computation of the Gini coefficients at different spatial scales, to estimate the spatial correlation and the geographical variability of radon concentrations. This variability can be interpreted as a signature of the different subsurface geological conditions. The Gini coefficient computation is a statistical tool widely used to determine the degree of inhomogeneity of different kinds of distributions. We generated several simulated radon distributions, and the proposed tool has been validated by comparing the variograms based on the semi-variance computation with those ones based on the Gini coefficient. The Gini coefficient variogram is shown to be a good estimator of the inhomogeneity degree of radon concentration. Indeed, it allows to better constrain the critical distance below which the radon geological source can be considered as uniform at least for the investigated length scales of variability; it also better discriminates the fluctuations due to the environmental predisposing factors from those ones due to the random spatially uncorrelated noise.
Czasopismo
Rocznik
Strony
99--104
Opis fizyczny
Bibliogr. 17 poz., rys.
Twórcy
  • Advanced Biomedical Science Department University of Naples, Federico II Corso Umberto I, 40-80138 Naples, Italy
  • National Institute of Nuclear Physics (INFN) Strada Comunale Cinthia, 80126, Naples, Italy
  • Department of Physics, “E. Pancini” University of Naples, Federico II Corso Umberto I, 40-80138 Naples, Italy
  • Department of Physics, “E. Pancini” University of Naples, Federico II Corso Umberto I, 40-80138 Naples, Italy
  • Advanced Biomedical Science Department University of Naples, Federico II Corso Umberto I, 40-80138 Naples, Italy
autor
  • Advanced Biomedical Science Department University of Naples, Federico II Corso Umberto I, 40-80138 Naples, Italy
  • National Institute of Nuclear Physics (INFN) Strada Comunale Cinthia, 80126, Naples, Italy
Bibliografia
  • 1. International Agency for Research on Cancer. (1988 Manmade mineral fibres and radon. (IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, Vol. 43). Lyon, France: IARC.
  • 2. United Nations Scientifi c Committee on the Effects of Atomic Radiation. (2000). Sources and effects of ionizing radiation. United Nations Scientifi c Committee on the Effects of Atomic Radiation UNSCEAR 2000 Report to the General Assembly, with Scientific Annexes. Vol. 1: Sources. New York: United Nations.
  • 3. Kavasi, N., Somlai, J., Szeiler, G., Szabo, B., Schafer, I., & Kovacs, T. (2010). Estimation of effective doses to cavers based on radon measurements carried out in seven caves of the Bakony Mountains in Hungary. Radiat. Meas., 45, 1068–1071. https://doi.org/10.1016/j.radmeas.2010.07.017.
  • 4. Quarto, M., Pugliese, M., Loffredo, F., Zambella, C., & Roca, V. (2014). Radon measurements and effective dose from radon inhalation estimation in the neapolitan catacombs. Radiat. Prot. Dosim., 158, 442–446. https://doi.org/10.1093/rpd/nct255.
  • 5. Kendall, G. M. (2004). Controls on radioactivity in water supplies in England and Wales, with especial reference to radon. J. Radiol. Prot., 24, 409–412. DOI: 10.1088/0952-4746/24/4/005.
  • 6. Demoury, C., Ielsch, G., Hemon, D., Laurent, O., Laurier, D., Clavel, J., & Guillevic, J. (2013). A statistical evaluation of the influence of housing characteristics and geogenic radon potential on indoor radon concentrations in France. J. Environ. Radioact., 126, 216–225. https://doi.org/10.1016/j.jenvrad.2013.08.006.
  • 7. Quarto, M., Pugliese, M., Loffredo, F., & Roca, V. (2016). Indoor radon concentration and gamma dose rate in dwellings of the Province of Naples, South Italy, and estimation of the effective dose to the inhabitants. Radioprotection, 51(1), 31–36. DOI: 10.1051/radiopro/2015021.
  • 8. Bossew, P., Zunić, Z. S., Stojanovska, Z., Tollefsen, T., Carpentieri, C., Veselinovic, N., Komatina, S., Vaupotic, J., Simovic, R. D., Antignani, S., & Bochicchio, F. (2014). Geographical distribution of the annual mean radon concentrations in primary schools of Southern Serbia – application of geostatistical methods. J. Environ. Radioact., 127, 141–148. https://doi. org/10.1016/j.jenvrad.2013.09.015.
  • 9. Menzler, S., Piller, G., Gruson, M., Rosario, A. S., Wichmann, H. E., & Kreienbrock, L. (2008). Population attributable fraction for lung cancer due to residential radon in Switzerland and Germany.Health Phys., 95(2), 179–189. DOI: 10.1097/01.HP.0000309769.55126.03.
  • 10. McBratney, A. B., Webster, R., & Burgess, T. M. (1981). The design of optimal sampling schemes for local estimation and mapping of regionalized variables-I: Theory and method. Comput. Geosci., 7(4), 331–334. https://doi.org/10.1016/0098-3004(81)90077-7.
  • 11. Zhu, H. C., Charlet, J. M., & Poffi jn, A. (2001). Radon risk mapping in southern Belgium: an application of geostatistical and GIS techniques. Sci. Total Environ.,272(1/3), 203–210. https://doi.org/10.1016/S0048-9697(01)00693-3.
  • 12. Vitale, S., & Ciarcia, S. (2013). Tectono-stratigraphic and kinematic evolution of the southern Apennines/Calabria–Peloritani Terrane system (Italy). Tectonophysics, 583, 164–182. https://doi.org/10.1016/j.tecto.2012.11.004.
  • 13. Pandey, M. D., & Nathwani, J. S. (1996). Measurement of socio-economic inequality using the lifequality index. Soc. Indic. Res., 39, 187–202.
  • 14. Chiles, J. P., & Delfiner, P. (1999). Geostatistics: Modeling spatial uncertainty. New York: Wiley.15. Lark, R. M. (2000). Estimating variograms of soil properties by the method-of-moments and maximum likelihood. Eur. J. Soil Sci., 51(4), 717–728. https://doi.org/10.1046/j.1365-2389.2000.00345.x.
  • 16. Borgoni, R., Quatto, P., Somà, G., & de Bartolo, D. (2010). A geostatistical approach to define guidelines for radon prone area identification. Stat. Methods Appl., 19, 255–276. DOI: 10.1007/s10260-009-0128-x.
  • 17. Gini, C. (1912). Memorie di metodologia statistica.Vol. 1. Variabilita concentrazione. Rome: Libreria Eredi Virgilio Veschi.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ac4da8ac-cfac-49e3-b5c3-230b61a92392
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