The delineation of radon prone areas is one of the central requirements of the European Council Directive 2013/59/EURATOM. It is quite a complex task which usually requires the collection of radon data through an appropriate survey as a first step. This paper presents the design and methodology of the recent Austrian radon survey (ÖNRAP 2, 2013-2019) and its implementation. It details the results of the nationwide survey as well as correlations and dependencies with geology and building characteristics. The paper also discusses the representativeness of the survey as well as advantages and disadvantages of the selected approach. For the purpose of establishing a new delineation of radon prone areas in Austria we distributed approximately 75,000 passive long-term radon detectors. They were offered to selected members of the voluntary fire brigades and this resulted in about 50,000 radon measurements. Thus, a return rate of about 67% was achieved. The distribution of the radon results closely follows a log-normal distribution with a median of 99 Bq/m³, a geometric mean of 109 Bq/m³, and a geometric standard deviation factor of 2.29. 11% of the households show a mean radon concentration above the national reference level of 300 Bq/m³. Important data on building characteristics and the location of the measured rooms were collected by means of a specific questionnaire and a measurement protocol that were handed out together with the radon detectors. We were able to identify significant correlations between the indoor radon concentration and geology, the year of construction, and the coupling of the room to the ground (basement yes/no, floor level). Being a geographically-based and not a population-weighted survey, the comparison of building characteristics with the Austrian census data confirms that rural areas are over-represented in this survey. As a summary, the selected approach of conducting passive long-term radon measurements in selected dwellings of members of the voluntary fire brigades proved to be an efficient method to collect reliable data as a basis for the delineation of radon prone areas. The next step was to eliminate factors that influence the measured radon concentration through appropriate modelling. Based on the results predicted by the model radon areas are then be classified. This will be presented in a subsequent publication.
Keywords: Building characteristics; Geology; National radon survey; Radon areas; Survey design.
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