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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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Related in: MedlinePlus

(a) Exponential semi-variograms related to first and last iteration of IRWGLS algorithm. (b) Effect of the distance of a point to the closest tectonic lineament.
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f9-ijerph-08-01420: (a) Exponential semi-variograms related to first and last iteration of IRWGLS algorithm. (b) Effect of the distance of a point to the closest tectonic lineament.

Mentions: In order to evaluate the effect of building characteristics and geologic factors on IRC, we applied the methodology described in Section 3.2 which was implemented by an R code [45]. The GLS regression includes the explicative variables described in section 2, namely whether the building is in direct contact with the ground, whether it is a single unit and whether it has stone walls, the geologic type of the soil and the distance from the closest tectonic lineament. Since the latter was expected to have a non linear effect on IRC, it was entered in the model through a linear B-spline [46]. Using a B-spline transform for the distance from the nearest tectonic fault seems a natural solution given the approach followed in this paper. In fact, in addition to increasing model flexibility, it is also computationally convenient as it amounts to extend the set of the explanatory variables of the regression by adding the value of the basis function evaluated at knots. With this expanded set of covariates, the regression can be fitted in the least squares framework, described in the previous section. The model considered is then a semi-parametric one which preserves the additive nature of the predictor. We adopted an exponential specification for the variogram. The IRWGLS algorithm converged reasonably quickly, after 5 iterations. The estimated variograms, after the first and last iteration, are reported in Figure 9(a), which shows that only moderate adjustments occurred during the procedure.


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)

(a) Exponential semi-variograms related to first and last iteration of IRWGLS algorithm. (b) Effect of the distance of a point to the closest tectonic lineament.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f9-ijerph-08-01420: (a) Exponential semi-variograms related to first and last iteration of IRWGLS algorithm. (b) Effect of the distance of a point to the closest tectonic lineament.
Mentions: In order to evaluate the effect of building characteristics and geologic factors on IRC, we applied the methodology described in Section 3.2 which was implemented by an R code [45]. The GLS regression includes the explicative variables described in section 2, namely whether the building is in direct contact with the ground, whether it is a single unit and whether it has stone walls, the geologic type of the soil and the distance from the closest tectonic lineament. Since the latter was expected to have a non linear effect on IRC, it was entered in the model through a linear B-spline [46]. Using a B-spline transform for the distance from the nearest tectonic fault seems a natural solution given the approach followed in this paper. In fact, in addition to increasing model flexibility, it is also computationally convenient as it amounts to extend the set of the explanatory variables of the regression by adding the value of the basis function evaluated at knots. With this expanded set of covariates, the regression can be fitted in the least squares framework, described in the previous section. The model considered is then a semi-parametric one which preserves the additive nature of the predictor. We adopted an exponential specification for the variogram. The IRWGLS algorithm converged reasonably quickly, after 5 iterations. The estimated variograms, after the first and last iteration, are reported in Figure 9(a), which shows that only moderate adjustments occurred during the procedure.

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