BACKGROUND Radon is a carcinogenic, radioactive gas that can accumulate indoors and is undetected by human senses. Therefore, accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon-prone areas. OBJECTIVES Indoor radon concentration at the national scale is usually estimated on the basis of extensive measurement campaigns. However, characteristics of the sampled households often differ from the characteristics of the target population owing to the large number of relevant factors that control the indoor radon concentration, such as the availability of geogenic radon or floor level. Furthermore, the sample size usually does not allow estimation with high spatial resolution. We propose a model-based approach that allows a more realistic estimation of indoor radon distribution with a higher spatial resolution than a purely data-based approach. METHODS A multistage modeling approach was used by applying a quantile regression forest that uses environmental and building data as predictors to estimate the probability distribution function of indoor radon for each floor level of each residential building in Germany. Based on the estimated probability distribution function, a probabilistic Monte Carlo sampling technique was applied, enabling the combination and population weighting of floor-level predictions. In this way, the uncertainty of the individual predictions is effectively propagated into the estimate of variability at the aggregated level. RESULTS The results show an approximate lognormal distribution of indoor radon in dwellings in Germany with an arithmetic mean of 63 Bq/m3, a geometric mean of 41 Bq/m3, and a 95th percentile of 180 Bq/m3. The exceedance probabilities for 100 and 300 Bq/m3 are 12.5% (10.5 million people affected) and 2.2% (1.9 million people affected), respectively. In large cities, individual indoor radon concentration is generally estimated to be lower than in rural areas, which is due to the different distribution of the population on floor levels. DISCUSSION The advantages of our approach are that is yields a) an accurate estimation of indoor radon concentration even if the survey is not fully representative with respect to floor level and radon concentration in soil, and b) an estimate of the indoor radon distribution with a much higher spatial resolution than basic descriptive statistics. https://doi.org/10.1289/EHP14171.

中文翻译：

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使用基于机器学习的新概率模型和德国氡气调查数据开发高分辨率室内氡气图。


背景技术氡是一种致癌的放射性气体，可以在室内积聚并且人类感官无法检测到。因此，准确了解室内氡气浓度对于评估氡气相关的健康影响或识别氡气易发区域至关重要。目标 全国范围内的室内氡气浓度通常是根据广泛的测量活动来估计的。然而，由于控制室内氡气浓度的相关因素很多，例如地源氡气的可用性或地板水平，抽样家庭的特征往往与目标人群的特征不同。此外，样本大小通常不允许进行高空间分辨率的估计。我们提出了一种基于模型的方法，与纯粹基于数据的方法相比，它可以以更高的空间分辨率更真实地估计室内氡气分布。方法采用多阶段建模方法，通过应用分位数回归森林，使用环境和建筑数据作为预测因子来估计德国每栋住宅楼各层室内氡气的概率分布函数。基于估计的概率分布函数，应用概率蒙特卡罗采样技术，实现楼层预测的组合和总体加权。通过这种方式，个体预测的不确定性被有效地传播到聚合水平上的变异性估计中。结果 结果显示，德国住宅中的室内氡气近似对数正态分布，算术平均值为 63 Bq/m3，几何平均值为 41 Bq/m3，第 95 个百分位数为 180 Bq/m3。 100 和 300 Bq/m3 的超标概率为 12。分别为 5%（1050 万人受影响）和 2.2%（190 万人受影响）。在大城市，个体室内氡气浓度估计普遍低于农村地区，这是由于楼层人口分布不同所致。讨论 我们的方法的优点是：a）即使调查不能完全代表地面水平和土壤中的氡气浓度，也能准确估计室内氡气浓度；b）通过a）估计室内氡气分布空间分辨率比基本描述统计高得多。 https://doi.org/10.1289/EHP14171。