Ambient dose rate as an additional predictor for the identification of radon-prone areas as used in the German State of Saxony-Anhalt

As a consequence of Council Directive 2013/59/EURATOM and its transposition into national law, the German States (Länder) had to define radon-prone areas where special rules apply in order to assure protection against excessive exposure to Radon-222 in living spaces and at work. The state of Saxony-Anhalt has chosen to define these areas based on datasets of long-term average indoor Rn concentration and Rn concentration in soil-air, complemented by ambient dose rate (ADR), whose values were acquired by a measurement campaign throughout the state territory.

Saxony-Anhalt is characterized by sabulous lowlands in its northern part with little Rn exhalation, but also by granite in the secondary mountains of the Harz massif and copper-shale dominated areas in the south. Radon prone areas can therefore be expected in the latter regions.

ADR was measured from vehicles carrying both a bGeigie nano pancake detector (the standard device of the Safecast mapping project, https://safecast.org/) with automatic GPS geo-referencing and a Thermo Eberline ESM FHZ 672-2 plastic scintillator. The latter device features a natural-background rejection system which could be used to determine high ADR levels as a consequence of K-40 anomalies that needed to be rejected. An ADR map could be created which was statistically linked to geogenic Rn data and soil geochemistry.

The purpose of the resulting model is to develop an algorithm that makes ADR a predictor for identifying radon-prone areas, which is sufficiently accurate for the objective of Rn legislation. It can be used in regions lacking of indoor Rn concentration or soil-sample data, on which the identification of such areas is usually based, but whose acquisition is too time and labour consuming to be achievable in the time frame allowed for implementation of Rn legislation.

It could be demonstrated that the gamma-dose rate can be used as an additional predictor for the identification of radon-prone areas with an algorithm that applies selective averaging on the data obtained on 2 500 km of measuring trips throughout the entire state of Saxony-Anhalt.

In this contribution, the algorithm is presented together with the areas the State of Saxony-Anhalt has finally determined as radon prone, with the legal consequence for employers to do quality assured Radon measurements and take action with respect to radon mitigation, if necessary. Special emphasis is put on establishing a statistically significant relationship between the measured dose rate on the one hand and geological information and radon exhalation, on the other.