谷歌浏览器插件
订阅小程序
在清言上使用

Exploring a new machine learning based probabilistic model for high-resolution indoor radon mapping, using the German indoor radon survey data

Eric Petermann,Peter Bossew, Joachim Kemski,Valeria Gruber, Nils Suhr, Bernd Hoffmann

CoRR(2023)

引用 0|浏览19
暂无评分
摘要
Radon is a carcinogenic, radioactive gas that can accumulate indoors. 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 sample often differ from the characteristics of the population due 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 two-stage modelling approach was applied: 1) a quantile regression forest using environmental and building data as predictors was applied to estimate the probability distribution function of indoor radon for each floor level of each residential building in Germany; (2) a probabilistic Monte Carlo sampling technique enabled 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 with an arithmetic mean of 63 Bq/m3, a geometric mean of 41 Bq/m3 and a 95 180 Bq/m3. The exceedance probability for 100 Bq/m3 and 300 Bq/m3 are 12.5 (10.5 million people) and 2.2
更多
查看译文
关键词
indoor radon map,probabilistic exposure model,machine
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要