Section Radon and NORM, Federal Office for Radiation Protection (BfS), Berlin, Germany.
Sachverständigenbüro Dr. Kemski, Bonn, Germany.
Environ Health Perspect. 2024 Sep;132(9):97009. doi: 10.1289/EHP14171. Epub 2024 Sep 18.
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.
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.
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.
The results show an approximate lognormal distribution of indoor radon in dwellings in Germany with an arithmetic mean of , a geometric mean of , and a 95th percentile of . The exceedance probabilities for 100 and 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.
The advantages of our approach are that is yields ) 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 ) an estimate of the indoor radon distribution with a much higher spatial resolution than basic descriptive statistics. https://doi.org/10.1289/EHP14171.
氡是一种致癌的放射性气体，它可以在室内积聚，而且人类的感官无法察觉。因此，准确了解室内氡浓度对于评估与氡有关的健康影响或确定氡高发地区至关重要。
国家层面的室内氡浓度通常是基于广泛的测量活动来估计的。然而，由于控制室内氡浓度的相关因素很多，例如基岩氡的存在或楼层，采样家庭的特征往往与目标人群的特征不同。此外，样本量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，该方法可以比纯粹基于数据的方法更真实地估计室内氡的分布，并具有更高的空间分辨率。
通过应用使用环境和建筑数据作为预测因子的分位数回归森林，采用多阶段建模方法来估计德国每栋住宅建筑每层的室内氡概率分布函数。基于估计的概率分布函数，应用概率蒙特卡罗抽样技术，对楼层预测进行组合和总体加权。通过这种方式，可以有效地将个别预测的不确定性传播到汇总水平的变异性估计中。
结果表明，德国住宅内的室内氡近似呈对数正态分布，算术平均值为 ，几何平均值为 ，第 95 百分位数为 。氡浓度超过 100 和 的概率分别为 12.5%（受影响的人数为 1050 万人）和 2.2%（受影响的人数为 190 万人）。在大城市，由于人口在楼层上的分布不同，个别室内氡浓度通常估计低于农村地区。
我们方法的优点是，即使调查在土壤中的楼层和氡浓度方面不完全具有代表性，也可以）准确估计室内氡浓度，并且可以）比基本描述性统计数据高得多的空间分辨率估计室内氡分布。https://doi.org/10.1289/EHP14171.
