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

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.

Mechanisms of Diffusion of Radon in Buildings and Mitigation Techniques

“…Depending on the mechanisms of radon diffusion from the ground, the premises of buildings located in the basement or on the ground floor are generally those particularly affected by the phenomenon [99]. Indeed, the main factors determining the risk of exposure to radon are geological (subsoil structure composition); technological, referring to the building (cellar floor permeability, cellar aeration, air-tightness of the homes); and behavioral, referring to the inhabitants (aeration habits of the occupants, tendency to spend time in environments in contact with the ground) [100][101][102][103]. In addition to the ground, radon could be present in domestic water [104] and thus enter into apartments on higher floors [105].…”

Quantile Regression Models and Their Applications: A Review

“…In the subarea of economics and policy, existing corruption levels have been explored [37] and the relationship between FDI and corruption level [38] has been examined using QR models. In the finance field, QR has been adopted to study housing price [ [50], and ecology [51,52].…”