Radon-222 is a naturally occurring radioactive gas arising from the decay of Uranium-238 present in the earth’s crust. The knowledge of the radon effects on human health is generating a growing attention by national and international authorities aimed at assessing the exposure of people to this radioactive gas and identifying building types and geographic areas where high indoor radon concentrations (IRCs) are likely to be found. However, given its multi-factorial dependence and the substantial regional variation, the analysis of IRC is not a simple task. There have been several efforts to evaluate the impact of the major influencing factors on IRCs. In this paper we illustrate how the complex relationships between the IRCs and a set of associated variables can be analysed using profile regression, a Bayesian non-parametric model for clustering responses and regressors simultaneously. Analyzing a geo-referenced database of annual IRCs for the Abruzzo region (Central Italy), we show that the proposed methodology allows to identify clusters of buildings according to their proneness to IRCs and that, through cluster assignment, it is possible to disentangle the effect of regressors on IRC and predict its levels for specific combinations of the explanatory variables.

中文翻译：

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通过轮廓回归方法研究室内ra浓度与某些潜在影响因素之间的关联

222 222是天然存在的放射性气体，是由于地壳中存在的铀238的衰变而产生的。the对人类健康的影响的认识正在引起国家和国际当局的日益关注，旨在评估人们对这种放射性气体的暴露程度，并确定可能发现高室内high浓度（IRC）的建筑物类型和地理区域。但是，考虑到它的多因素依赖性和较大的区域差异，对IRC的分析并不是一项简单的任务。已经进行了一些努力来评估主要影响因素对IRC的影响。在本文中，我们说明了如何使用配置文件回归分析IRC和一组相关变量之间的复杂关系，贝叶斯非参数模型，用于同时对响应和回归进行聚类。通过分析阿布鲁佐地区（意大利中部）的年度IRC的地理参考数据库，我们发现，所提出的方法可以根据建筑物对IRC的倾向性来识别建筑物的群集，并且通过群集分配，可以消除影响在IRC上进行回归分析，并针对解释变量的特定组合预测其水平。