Radon is a noble gas that occurs in nature as a decay product of uranium. Radon is the principal contributor to natural background radiation and is considered to be one of the major leading causes of lung cancer. The main concern revolves around indoor environments where radon accumulates and reaches high concentrations. In this paper, a semiparametric random-effect M-quantile model is introduced to model radon concentration inside a building, and a way to estimate the model within the framework of robust maximum likelihood is presented. Using data collected in a monitoring survey carried out in the Lombardy Region (Italy) in 2003–2004, we investigate the impact of a number of factors, such as geological typologies of the soil and building characteristics, on indoor concentration. The proposed methodology permits the identification of building typologies prone to a high concentration of the pollutant. It is shown how these effects are largely not constant across the entire distribution of indoor radon concentration, making the suggested approach preferable to ordinary regression techniques since high concentrations are usually of concern. Furthermore, we demonstrate how our model provides a natural way of identifying those areas more prone to high concentration, displaying them by thematic maps. Understanding how buildings’ characteristics affect indoor concentration is fundamental both for preventing the gas from accumulating in new buildings and for mitigating those situations where the amount of radon detected inside a building is too high and has to be reduced.

Borgoni, R., Carcagnì, A., Salvati, N., & Schmid, T. (2019). Analysing radon accumulation in the home by flexible M-quantile mixed effect regression. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 33(2), 375-394 [10.1007/s00477-018-01643-1].

Analysing radon accumulation in the home by flexible M-quantile mixed effect regression

Borgoni, R
;
CARCAGNÌ, ANTONELLA;
2019

Abstract

Radon is a noble gas that occurs in nature as a decay product of uranium. Radon is the principal contributor to natural background radiation and is considered to be one of the major leading causes of lung cancer. The main concern revolves around indoor environments where radon accumulates and reaches high concentrations. In this paper, a semiparametric random-effect M-quantile model is introduced to model radon concentration inside a building, and a way to estimate the model within the framework of robust maximum likelihood is presented. Using data collected in a monitoring survey carried out in the Lombardy Region (Italy) in 2003–2004, we investigate the impact of a number of factors, such as geological typologies of the soil and building characteristics, on indoor concentration. The proposed methodology permits the identification of building typologies prone to a high concentration of the pollutant. It is shown how these effects are largely not constant across the entire distribution of indoor radon concentration, making the suggested approach preferable to ordinary regression techniques since high concentrations are usually of concern. Furthermore, we demonstrate how our model provides a natural way of identifying those areas more prone to high concentration, displaying them by thematic maps. Understanding how buildings’ characteristics affect indoor concentration is fundamental both for preventing the gas from accumulating in new buildings and for mitigating those situations where the amount of radon detected inside a building is too high and has to be reduced.
Si
Articolo in rivista - Articolo scientifico
Scientifica
Building factors; Environmental radioactivity; Hierarchical mixed models; Lombardy region; Penalised splines; Radon-prone areas;
Environmental radioactivity, Building factors, Radon-prone areas, Hierarchical mixed models, Penalised splines, Lombardy region
English
375
394
20
Borgoni, R., Carcagnì, A., Salvati, N., & Schmid, T. (2019). Analysing radon accumulation in the home by flexible M-quantile mixed effect regression. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 33(2), 375-394 [10.1007/s00477-018-01643-1].
Borgoni, R; Carcagnì, A; Salvati, N; Schmid, T
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/221014
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