[No Title Available]

Rocha, Marcelo Monteiro da; Yamamoto, Jorge Kazuo; Watanabe, Jorge; Fonseca, Priscilla Pinto da

doi:10.1590/s0001-37652012005000017

Cited by 9 publications

(9 citation statements)

References 8 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, the Pearson's correlation coefficient between indoor radon and uranium concentration in topsoil is relatively low (r = 0.2783), which implies that CCK estimations with U as the secondary variable are still mainly based on the primary variable (i.e. AM; Rocha et al, 2012). Therefore, although CCK performs slightly better than OK, spatial predictions are similar (Fig.…”

Section: Indoor Radon Predictionsmentioning

confidence: 96%

“…Another problem with lognormal kriging is that ill assessment of the kriging SD leads to large errors in E[X] and Var[X] due to exponentiation, so that variogram parameters must be estimated very carefully (Armstrong and Boufassa, 1988). Deviations from stationarity and uni-as well as multivariate log-normality are also critical (Cressie, 1993;Roth, 1998). On the other hand, in highly skewed quantities (as is typical for Rn and in fact for many positive-definite environmental quantities such as concentrations) there seems to be little choice but to work with transformed (e.g.…”

Section: Indoor Radon Predictionsmentioning

confidence: 99%

See 1 more Smart Citation

The first version of the Pan-European Indoor Radon Map

Elío

Cinelli

Bossew

et al. 2019

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. A hypothetical Pan-European Indoor Radon Map has been developed using summary statistics estimated from 1.2 million indoor radon samples. In this study we have used the arithmetic mean (AM) over grid cells of 10 km × 10 km to predict a mean indoor radon concentration at ground-floor level of buildings in the grid cells where no or few data (N<30) are available. Four interpolation techniques have been tested: inverse distance weighting (IDW), ordinary kriging (OK), collocated cokriging with uranium concentration as a secondary variable (CCK), and regression kriging with topsoil geochemistry and bedrock geology as secondary variables (RK). Cross-validation exercises have been carried out to assess the uncertainties associated with each method. Of the four methods tested, RK has proven to be the best one for predicting mean indoor radon concentrations; and by combining the RK predictions with the AM of the grids with 30 or more measurements, a Pan-European Indoor Radon Map has been produced. This map represents a first step towards a European radon exposure map and, in the future, a radon dose map.

show abstract

Section: Indoor Radon Predictionsmentioning

confidence: 96%

Section: Indoor Radon Predictionsmentioning

confidence: 99%

The first version of the Pan-European Indoor Radon Map

Elío

Cinelli

Bossew

et al. 2019

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…However, the Pearson's correlation coefficient between indoor radon and uranium concentration in topsoil is relatively low (r = 0.2783), which implies that CoCK estimations with U as secondary variable are still mainly based on the primary variable (i.e. AM; Rocha et al, 2012). Therefore, although CoCK performs slightly better that OK, spatial predictions are similar (Figure 9).…”

Section: Indoor Radon Predictionsmentioning

confidence: 97%

First steps towards an All-European Indoor Radon Map

Elío¹,

Cinelli²,

Bossew³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. A hypothetical All-European Indoor Radon Map has been developed using summary statistics estimated from 1.2 million indoor radon samples. In this study we have used the arithmetic mean (AM) over grid cells of 10 km × 10 km to predict a mean indoor radon concentration at ground floor level in the grid cells where no or few data are available (N < 30). Four interpolation techniques have been tested: inverse distance weighted (IDW); ordinary kriging (OK); collocated cokriging with uranium concentration as secondary variable (CoCK); and regression kriging with topsoil geochemistry and bedrock geology as secondary variables (RK). Cross-validation exercises have been carried out to assess the uncertainties associated with each method. Of the four methods tested, RK has proved to be the best one for predicting mean indoor radon concentrations, and by combining the RK predictions with the AM of the grids with 30 or more measurements, an All-European Indoor Radon Map has been produced. This map represents the first step towards a European radon exposure, and further on a radon dose map.

show abstract

“…Cokriging has several variants and collocated ordinary cokriging is one of them [27]. It can be used when primary data are available in sparsely distributed points while secondary data are present in all points of the mesh.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary Variable

2020

View full text Add to dashboard Cite

In the present study, the influence of the sampling density on the coestimation error of a regionalized, locally stationary and geo-mining nature variable is analyzed. The case study is two-dimensional (2D) and synthetic-type, and it has been generated using a non-conditional Sequential Gaussian Simulation (SGS), with subsequent transformation to Gaussian distribution, seeking to emulate the structural behavior of the aforementioned variable. A primary and an auxiliary variable with different spatial and statistical properties are constructed using the same methodology. The collocated ordinary cokriging method has been applied, in which the auxiliary variable is spatially correlated with the primary one and it is known exhaustively. Fifteen sampling densities are extracted from the target population of the primary variable, which are compared with the simulated values after performing coestimation. The obtained results follow a potential function that indicates the mean global error (MGE) based on the sampling density percentage (SDP) ( M G E = 1.2366 · S D P − 0.224 ).

show abstract

[No Title Available]

Cited by 9 publications

References 8 publications

The first version of the Pan-European Indoor Radon Map

The first version of the Pan-European Indoor Radon Map

First steps towards an All-European Indoor Radon Map

Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary Variable

Contact Info

Product

Resources

About