Due to limited natural ventilation we may expect increased concentrations of radioactive gas radon and its progeny in karst caves. Based on the results of measurements of the unattached and attached fractions of radon progeny in the Postojna Cave in different yearly seasons dose conversion factors have been calculated. They exceeded the values recommended by the International Commission of Radiological Protection. The reason for that is low number concentration of non-radioactive aerosols and thus high unattached fraction.
COBISS.SI-ID: 21475111
The study of nano size unattached fraction of radon decay products and comparison of values obtained in different working environments has shown its dependence on meteorological parameters, ventilation and working regimes. These data may help in improvement of radon dosimetry in which we have a disagreement between the effective doses obtained on the basis of epidemiological and dosimetric approaces.
COBISS.SI-ID: 22300455
Radon decay products (RnDP) and general aerosols were monitored in parallel in the air of the Postojna Cave in summer and in winter. Total number concentration of the aerosol particles during visits in summer was lower (700 cm–3) than in winter (2500 cm–3), and was dominated by particles <50 nm in summer and >50 nm in winter. This explains why fun values are by a factor higher in summer than in winter. The difference is caused by an enhanced inflow of fresh air, driven in winter by higher air temperature in the cave than outdoors, introducing outdoor larger aerosols particles into the cave.
COBISS.SI-ID: 24325159
Basic research of parameters, crucial in radon dosimetry, is mostly limited to laboratories and model houses. We see our advantage in conducting experiments in real environment and in combining of our basic knowledge with the applied research. In this paper our approach in managing basic and applied radon research is presented and discussed.
COBISS.SI-ID: 24097575
Time series of radon concentrations have been analysed applying artificial neural networks. The entire database is divided into two subsets: SA subset with the data for seismically active periods and N-SA subset with the data for seismically non-active periods. First, the N-SA subset was used to train the program to predict radon concentration from the environmental data. When then the entire database was analysed, disagreement generally appeared between the predicted and measured radon concentrations. We were able to correctly predict 10 seismic events out of 13 within the 2 years period.
COBISS.SI-ID: 23182375