Izvori nesigurnosti pri klasifikaciji radonskih zona

Abstract

Evropski savet je doneo direktivu 2013/59/EURATOM (EU-BSS) po kojoj se nalaže državama članicama EU da imaju ustanovljen radonski akcioni plan, što između ostalog podrazumeva i identifikaciju radonskih prioritetnih zona (Radon priority areas), odnosno zona sa različitim nivoom "prioriteta". S obzirom da je neizvodljivo vršiti merenja radona u svakoj kući, potrebno je dizajnirati prospekciju radona kako bi se dobila reprezentativna procena srednje godišnje koncentracije radona u zatvorenim prostorijama na određenoj teritoriji. Nije bitan samo reprezentativan izbor kuća, nego je i nesigurnost merenja i procene srednje godišnje koncentracije potrebno držati što je niže moguće. Nesigurnostn klasifikacije zone određenog prioriteta u nekoj oblasti je stoga kombinacija nesigurnosti pojedinačnog merenja i ekstrapolacije tog merenja na celu oblast. U ovom radu ćemo pokušati da nabrojimo i procenimo izvore nesigurnosti pri klasifikaciji i damo preporuke u cilju smanjenja stepena nesigurnosti. Ovaj rad je urađen u sklopu MetroRadon projekta.

The EC has laid down directive 2013/59/EURATOM which represents basic safety standards regarding the radon protection of the European citizens. Within the BSS they oblige countries to establish radon action plans which include identification of Radon Priority Areas (RPA). Since it is not feasible to perform indoor radon measurements in each dwelling, it is necessary to carefully design indoor radon survey in order to get representative estimation of annual average indoor radon concentration of the certain territory. It is not sufficient only to have representative selection of dwellings, but it is important to keep uncertainty of measurement and estimation of annual radon concentration as low as possible. Uncertainty of classification of radon zones is therefore combination of uncertainties coming from a single measurement uncertainty and uncertainty of extrapolation of single or group of measurements to the whole region. In this contribution, we will try to estimate sources of classification uncertainties and to give recommendation in order to reduce level of uncertainty. The research presented in this paper was realized within 16ENV04 MetroRADON project. This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union‘s Horizon 2020 research and innovation programme.