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A geostatistical approach to assess the spatial association between indoor radon concentration, geological features and building characteristics: the case of Lombardy, Northern Italy.

Borgoni R, Tritto V, Bigliotto C, de Bartolo D - Int J Environ Res Public Health (2011)

Bottom Line: Firstly, we mapped indoor radon concentration in a large and inhomogeneous region using a geostatistical approach which borrows strength from the geologic nature of the soil.Secondly, knowing that geologic and anthropogenic factors, such as building characteristics, can foster the gas to flow into a building or protect against this, we evaluated these effects through a multiple regression model which takes into account the spatial correlation of the data.This allows us to rank different building typologies, identified by architectonic and geological characteristics, according to their proneness to radon.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of Milan-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milan, Italy. riccardo.borgoni@unimib.it

ABSTRACT
Radon is a natural gas known to be the main contributor to natural background radiation exposure and second to smoking, a major leading cause of lung cancer. The main source of radon is the soil, but the gas can enter buildings in many different ways and reach high indoor concentrations. Monitoring surveys have been promoted in many countries in order to assess the exposure of people to radon. In this paper, two complementary aspects are investigated. Firstly, we mapped indoor radon concentration in a large and inhomogeneous region using a geostatistical approach which borrows strength from the geologic nature of the soil. Secondly, knowing that geologic and anthropogenic factors, such as building characteristics, can foster the gas to flow into a building or protect against this, we evaluated these effects through a multiple regression model which takes into account the spatial correlation of the data. This allows us to rank different building typologies, identified by architectonic and geological characteristics, according to their proneness to radon. Our results suggest the opportunity to differentiate construction requirements in a large and inhomogeneous area, as the one considered in this paper, according to different places and provide a method to identify those dwellings which should be monitored more carefully.

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Building profiles with the lowest and highest estimated IRC.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3108118&req=5

f10-ijerph-08-01420: Building profiles with the lowest and highest estimated IRC.

Mentions: Finally, we classified the different typologies of buildings according to their proneness to IRC on the basis of the expected value of IRC, as estimated by the regression model. The combination of building factors and geologic classes identifies 2 × 2 × 2 × 11 = 88 different building profiles assuming the distance from the closest tectonic lineament fixed to any predefined value. This can be done without loss of generality, given the additive nature of the model. These profiles are ranked from the least to the most prone to IRC. We have not reported the whole list of ranked profiles here, but we have summarised the results in Figure 10 where the top and bottom five profiles are reported for two values of the distance from the closest tectonic fault namely 500 m and 5,000 m [48].


A geostatistical approach to assess the spatial association between indoor radon concentration, geological features and building characteristics: the case of Lombardy, Northern Italy.

Borgoni R, Tritto V, Bigliotto C, de Bartolo D - Int J Environ Res Public Health (2011)

Building profiles with the lowest and highest estimated IRC.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3108118&req=5

f10-ijerph-08-01420: Building profiles with the lowest and highest estimated IRC.
Mentions: Finally, we classified the different typologies of buildings according to their proneness to IRC on the basis of the expected value of IRC, as estimated by the regression model. The combination of building factors and geologic classes identifies 2 × 2 × 2 × 11 = 88 different building profiles assuming the distance from the closest tectonic lineament fixed to any predefined value. This can be done without loss of generality, given the additive nature of the model. These profiles are ranked from the least to the most prone to IRC. We have not reported the whole list of ranked profiles here, but we have summarised the results in Figure 10 where the top and bottom five profiles are reported for two values of the distance from the closest tectonic fault namely 500 m and 5,000 m [48].

Bottom Line: Firstly, we mapped indoor radon concentration in a large and inhomogeneous region using a geostatistical approach which borrows strength from the geologic nature of the soil.Secondly, knowing that geologic and anthropogenic factors, such as building characteristics, can foster the gas to flow into a building or protect against this, we evaluated these effects through a multiple regression model which takes into account the spatial correlation of the data.This allows us to rank different building typologies, identified by architectonic and geological characteristics, according to their proneness to radon.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of Milan-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milan, Italy. riccardo.borgoni@unimib.it

ABSTRACT
Radon is a natural gas known to be the main contributor to natural background radiation exposure and second to smoking, a major leading cause of lung cancer. The main source of radon is the soil, but the gas can enter buildings in many different ways and reach high indoor concentrations. Monitoring surveys have been promoted in many countries in order to assess the exposure of people to radon. In this paper, two complementary aspects are investigated. Firstly, we mapped indoor radon concentration in a large and inhomogeneous region using a geostatistical approach which borrows strength from the geologic nature of the soil. Secondly, knowing that geologic and anthropogenic factors, such as building characteristics, can foster the gas to flow into a building or protect against this, we evaluated these effects through a multiple regression model which takes into account the spatial correlation of the data. This allows us to rank different building typologies, identified by architectonic and geological characteristics, according to their proneness to radon. Our results suggest the opportunity to differentiate construction requirements in a large and inhomogeneous area, as the one considered in this paper, according to different places and provide a method to identify those dwellings which should be monitored more carefully.

Show MeSH
Related in: MedlinePlus