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

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Abstract

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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Notes

  1. An error of about 20% of the radon concentration measurement has been estimated for the detectors.

  2. It can be noted that the Italian legislation does not define action levels explicitly. In many circumstances, the 90/143/Euratom recommendation is adopted which suggests 200 and 400 Bq/m3 as the reference values for, respectively, the future construction standard and for considering remedial interventions in existing dwellings.

  3. Quite similar rankings are obtained by using different quantiles. For example using the 80th percentile for ranking, the same order is obtained except that profile 3 and 4 are switched as well as 5 and 6.

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Acknowledgements

The author warmly thanks ARPA Lombardia for providing the indoor radon gas monitoring survey data, Daniela de Bartolo for her useful comments on data collection, two anonymous referees and the associated editor whose suggestions helped in improving remarkably the quality of this paper and Denise Kilmartin for editing the paper.

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Correspondence to Riccardo Borgoni.

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Borgoni, R. A Quantile Regression Approach to Evaluate Factors Influencing Residential Indoor Radon Concentration. Environ Model Assess 16, 239–250 (2011). https://doi.org/10.1007/s10666-011-9249-3

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