Identifying radon priority areas by mapping geogenic radon potential of soils in Baden-Wuerttemberg (Germany) using machine learning algorithms

Radon (²²²Rn) is a radioactive gas of the uranium-radium decay chain and occurs naturally in soil air. Primarily by diffusion, radon migrates to the surface and can accumulate in buildings, where it is harmful to human health because of its radioactivity. To tackle this particular health hazard, radon mitigation was recently introduced into national law. Among other things, the law demanded the designation of radon priority areas by the end of 2020. These areas are defined as administrative areas with a large proportion of land affected by high radon concentrations inside buildings. In the state of Baden-Wuerttemberg these areas were selected at municipality level based on a 10x10 km national map of geogenic radon potential (GRP) provided by the German Federal Office for Radiation Protection (BfS) accompanied by a state-specific map of uranium concentrations. With the prospect of future designations, this work aims at replacing the national GRP map used in 2020 by a more sophisticated state-specific version. In doing so, we improve spatial resolution, focus on covariates that only represent the state-relevant features of geology and soil and move away from a kriging-based mapping approach towards machine learning.

This ongoing study is currently based on 580 radon measurements in soil gas at 1 m depth from different surveys spread irregularly over Baden-Wuerttemberg in southeast Germany. This point dataset is combined with a set of covariates from factors that influence radon concentrations such as soil parameters, geology, relief, climate and a map of uranium concentrations created from over 4000 heavy metal measurements. Modelling is done using a random forest (RF) approach as implemented in the R packages ranger and mlr3. Preliminary results indicate that the new GRP map with a spatial resolution of 250 m is highly useful in classifying communities as vulnerable areas, which were previously not called due to uncertain underlying data. In addition, model output confirms up to 80 % of already identified radon priority areas.

Machine learning algorithms such as RF with its precise learning progressions can be used to create GRP maps at regional scale at high resolution. Besides further improving the RF model, next steps will focus on explainable machine learning, i.e. to produce features that support policy makers in finding acceptance by the public in sight of a sensitive topic. This includes variable importance plots, uncertainty measures and maps representing areas of applicability. The latter will also be used to help guiding the ongoing radon measurement programme of Baden-Wuerttemberg, which currently comprises approximately 100 new locations per year.