A new high-resolution indoor radon map for Germany using a machine learning based probabilistic exposure model

Radon is a carcinogenic, radioactive gas that can accumulate indoors. Indoor radon exposure 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 such as the availability of geogenic radon or floor level. Furthermore, the sample size usually does not allow exposure 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. We applied a two-stage modelling approach: 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 give an arithmetic mean of 63 Bq/m3, a geometric mean of 41 Bq/m3 and a 95 %ile of 180 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. In large cities, individual indoor radon exposure is generally lower than in rural areas, which is a due to the different distribution of the population on floor levels. The advantages of our approach are 1) an accurate exposure estimation even if the survey was not fully representative with respect to the main controlling factors, and 2) an estimate of the exposure distribution with a much higher spatial resolution than basic descriptive statistics.