A geostatistical approach to define guidelines for radon prone area identification

Borgoni, Riccardo; Quatto, Piero; Somà, G; Bartolo, D De

doi:10.1007/s10260-009-0128-x

Cited by 16 publications

(10 citation statements)

References 26 publications

(25 reference statements)

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“…Firstly this research can help in recommending some guidelines for new buildings. In Lombardy, as in many other regions around the world, mapping IRC is nowadays a common practice (see [3] for IRC mapping on the same data). Such maps inform people of where higher concentrations can be expected.…”

Section: Discussionmentioning

confidence: 99%

A Quantile Regression Approach to Evaluate Factors Influencing Residential Indoor Radon Concentration

Borgoni

2011

Environ Model Assess

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Indoor radon concentrations depend on building characteristics such as building materials, ventilation and water supply. In this paper, a quantile regression approach is proposed to evaluate the effect of some buildings factors potentially influencing indoor radon concentration. Many of the considered factors, such as soil connection, age of construction and being a single family building, are found to have a statistically significant effect; however, this is far from being constant across the entire support of indoor radon concentration. A potential impact due to geological and geo-physical reasons is also found using the altitude of building locations as a surrogate variable. In addition, a clear local spatial effect is detected by a spatial autoregression approach.

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Section: Discussionmentioning

confidence: 99%

A Quantile Regression Approach to Evaluate Factors Influencing Residential Indoor Radon Concentration

Borgoni

2011

Environ Model Assess

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“…The radon-prone area issue points at what is known as change of support problem (COSP) in geostatistical terminology (Arbia 1989), since a realignment of radon data is required from the point level, where the measurement process takes place, to the area or block level, i.e. the administrative unit of a town, where the remediation actions should be implemented (Borgoni et al 2010). A solution to point-to-block COSP is provided by block-kriging (Chiles and Delfiner 1999), a technique that allows for predicting the average of a spatial random field on a given area.…”

Section: Geostatistical and Bayesian Methods For Radon Mappingmentioning

confidence: 99%

Residential radon concentration in the Abruzzo region (Italy): a different perspective for identifying radon prone areas

Sarra

Nissi

Palermi³

2011

Environ Ecol Stat

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Indoor radon is an important risk factor for human health. Indeed radon inhalation is considered the second cause of lung cancer after smoking. During the last decades, in many countries huge efforts have been made in order to measuring, mapping and predicting radon levels in dwellings. Various researches have been devoted to identify those areas within the country where high radon concentrations are more likely to be found. Data collected through indoor radon surveys have been analysed adopting various statistical approaches, among which hierarchical Bayesian models and geostatistical tools are worth noting. The essential goal of this paper regards the identification of high radon concentration areas (the so-called radon prone areas) in the Abruzzo Region (Italy). In order to accurately pinpoint zones deserving attention for mitigation purpose, we adopt spatial cluster detection techniques, traditionally employed in epidemiology. As a first step, we assume that indoor radon measurements do not arise from a continuous spatial process; thus the geographic locations of dwellings where the radon measurements have been taken can be viewed as a realization of a spatial point process. Following this perspective, we adopt and compare recent cluster detection techniques: the simulated annealing scan statistic, the case event approach based on distance regression on the selection order and the elliptic spatial scan statistic. The analysis includes data collected during surveys carried out by the Regional Agency for the Environment Protection of Abruzzo (ARTA) in 1,861 random sampled dwellings across 277 municipalities of the Abruzzo region. The radon prone areas detected by the selected approaches are provided along with the summary statistics of the methods. Finally, the methodologies considered in this paper are tested on simulated data in order to evaluate their power and the precision of cluster location detection.

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“…3a). Several studies show a clear relationship between geological factors and indoor radon concentration; however other factors, to a lesser extent, such as soil structure, groundwater conditions, ventilation and condition of the building, and so on, defi nitely infl uence the measurements [16]. For our purposes, all these factors can be considered as a source of spatially uncorrelated noise.…”

Section: Simulated Radon Concentration Datasetmentioning

confidence: 99%

“…Although crucial to limit the uncertainty on the estimation of expected concentration, it was shown that identify the correlation trend and hence the parameters R and S from an indoor concentration measurement campaign is not a trivial process, in particular for sparse and noisy sampling. In the next Section, we present a new tool for the variogram computation based on the spatial variability of the Gini coeffi cient [15][16][17].…”

Section: The Semi-variance and The Variogrammentioning

confidence: 99%

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

et al. 2020

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AbstractThe 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.

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A geostatistical approach to define guidelines for radon prone area identification

Abstract: Radon prone areas, Kriging, Geostatistical conditional simulation, Change of support problem,

Cited by 16 publications

References 26 publications

A Quantile Regression Approach to Evaluate Factors Influencing Residential Indoor Radon Concentration

A Quantile Regression Approach to Evaluate Factors Influencing Residential Indoor Radon Concentration

Residential radon concentration in the Abruzzo region (Italy): a different perspective for identifying radon prone areas

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

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