A geostatistical approach to define guidelines for radon prone area identification
AbstractRadon 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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Bibliographic InfoArticle provided by Springer in its journal Statistical Methods and Applications.
Volume (Year): 19 (2010)
Issue (Month): 2 (June)
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Web page: http://link.springer.de/link/service/journals/10260/index.htm
Other versions of this item:
- Riccardo Borgoni & Piero Quatto & Giorgio Somà & Daniela de Bartolo, 2007. "A Geostatistical Approach to Define Guidelines for Radon Prone Area Identification," Working Papers 20071102, Università degli Studi di Milano-Bicocca, Dipartimento di Statistica.
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
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