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Radon is a carcinogenic, radioactive gas that can accumulate indoors.Therefore, accurate knowledge of indoor radon concentration is crucial forassessing radon-related health effects or identifying radon-prone areas. Indoorradon concentration at the national scale is usually estimated on the basis ofextensive measurement campaigns. However, characteristics of the sample oftendiffer from the characteristics of the population due to the large number ofrelevant factors that control the indoor radon concentration such as theavailability of geogenic radon or floor level. Furthermore, the sample sizeusually does not allow estimation with high spatial resolution. We propose amodel-based approach that allows a more realistic estimation of indoor radondistribution with a higher spatial resolution than a purely data-basedapproach. A two-stage modelling approach was applied: 1) a quantile regressionforest using environmental and building data as predictors was applied toestimate the probability distribution function of indoor radon for each floorlevel of each residential building in Germany; (2) a probabilistic Monte Carlosampling technique enabled the combination and population weighting offloor-level predictions. In this way, the uncertainty of the individualpredictions is effectively propagated into the estimate of variability at theaggregated level. The results show an approximate lognormal distribution withan arithmetic mean of 63 Bq/m3, a geometric mean of 41 Bq/m3 and a 95 %ile of180 Bq/m3. The exceedance probability for 100 Bq/m3 and 300 Bq/m3 are 12.5 %(10.5 million people) and 2.2 % (1.9 million people), respectively.

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