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    Wyświetlanie 1-4 z 4
    Tytuł:
    A radon anomaly in soil gas at Cazzaso,NE Italy, as a precursor of an ML = 5.1 earthquake
    Autorzy:
    Vaupotič, J.
    Riggio, A.
    Santulin, M.
    Zmazek, B.
    Kobal, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/148908.pdf
    Data publikacji:
    2010
    Wydawca:
    Instytut Chemii i Techniki Jądrowej
    Tematy:
    radon
    soil gas
    Barasol probes
    anomalies
    earthquakes
    Opis:
    At Cazzaso (Friuli) in northeast Italy, radon (222Rn) activity concentration in soil gas in a borehole at a depth of 80 cm has been monitored continuously (at a frequency of once an hour) since May 2004, using a Barasol probe (Algade, France). In addition, environmental parameters (air and soil temperature, barometric pressure) have been recorded. The results have been evaluated and the relationship between radon levels and seismic activity is discussed. Correlation between radon concentration and barometric pressure has been observed. Preliminary results have shown a distinct radon anomaly prior to some earthquakes.
    Źródło:
    Nukleonika; 2010, 55, 4; 507-511
    0029-5922
    1508-5791
    Pojawia się w:
    Nukleonika
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The characteristics of radon and thoron concentration from soil gas in Shenzhen City of Southern China
    Autorzy:
    Wang, N.
    Zheng, L.
    Chu, X.
    Li, S.
    Yan, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/148789.pdf
    Data publikacji:
    2016
    Wydawca:
    Instytut Chemii i Techniki Jądrowej
    Tematy:
    222Rn/220Rn
    soil gas
    radon mapping
    China
    Opis:
    Radon (222Rn) and thoron (220Rn) from soil gas are very significant factors that can affect the indoor radon level in the first floor or in the basement. China is one of the countries with the highest thorium content in the world. Therefore, it is very significant to study 222Rn/220Rn concentration in the soil in Shenzhen City (SC). A 222Rn/220Rn survey was performed using a portable radon monitor (model RAD7) at 69 sites, covered a total area of 1800 km2 in 2013 to get the original data for radon risk estimation in SC. The average values of 222Rn and 220Rn concentration of soil gas of the total 69 locations are 86 ± 72 kBq•m–3 and 118 ± 85 kBq•m–3, respectively. 222Rn/220Rn concentrations are related to geological lithology. 222Rn concentrations vary from 40 to 370 kBq•m–3 and from 15 to 118 kBq•m–3 in weathered granite products and sediments, respectively, while 220Rn concentrations are from 103 to 435 kBq•m–3 and 2.2 to 96 kBq•m–3. The higher 220Rn values were mainly observed at the sites covered by the weathered granite products. Comparing with the areas of high 222Rn concentration, the areas of high 220Rn values are larger. The distribution of 222Rn concentration in the vertical direction displays an exponential distribution mode, but there is no rule of 220Rn concentration. The investigation suggests that people should pay attention to 220Rn contribution in the radon mapping of SC, as well as in the indoor radon survey.
    Źródło:
    Nukleonika; 2016, 61, 3; 315-319
    0029-5922
    1508-5791
    Pojawia się w:
    Nukleonika
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Radon soil-gas measurement campaign in Hessen: an approach to identifying areas with enhanced geogenic radon
    Autorzy:
    Kuske, Till
    Kerker, Steffen
    Breckow, Joachim
    Lehné, Rouwen
    Laupenmühlen, Tatjana
    Jedmowski, Lena
    Powiązania:
    https://bibliotekanauki.pl/articles/146321.pdf
    Data publikacji:
    2020
    Wydawca:
    Instytut Chemii i Techniki Jądrowej
    Tematy:
    radiation protection law
    radon-priority-areas
    radon
    soil gas
    Opis:
    The new radiation protection law in Germany, which came into effect 2018, puts greater emphasis on the protection against naturally occurring radiation, especially radon as a known health hazard. The law requires the delineation of radon priority areas, where prevention and remediation of high indoor radon concentrations should be taken with priority. In Germany, radiation protection is the administrative responsibility of the federal states. The state of Hesse has early on decided to fully survey the state for radon priority areas. To identify radon priority areas, the geogenic radon potential has to be determined. To achieve that radon, soil-gas measurements combined with soil permeability are a necessity. The University of Applied Sciences (THM) in Giessen is responsible for the radon soil-gas measurement campaign in Hessen. To achieve a statistically sound survey of the state of Hessen with an achievable amount of different measurement locations, and in the given time-frame, a geology-based concept has been designed. Taking into account the known geological information about geological structures in combination with the administrative counties, a survey strategy has been established. Prior known information regarding soil thickness, moisture, digability, and other technical limitations are used to determine the exact measuring locations. At every location, the radon activity in soil gas is measured. The soil permeability is determined for every measurement as well. Three measurements are performed at each location. Having completed the first set of measurements, the design criteria of the campaign and the practical experiences will be presented.
    Źródło:
    Nukleonika; 2020, 65, 2; 139-144
    0029-5922
    1508-5791
    Pojawia się w:
    Nukleonika
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Identification of radon anomalies in soil gas using decision trees and neural networks
    Autorzy:
    Zmazek, B.
    Džeroski, S.
    Torkar, D.
    Vaupotič, J.
    Kobal, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/148699.pdf
    Data publikacji:
    2010
    Wydawca:
    Instytut Chemii i Techniki Jądrowej
    Tematy:
    radon
    soil gas
    anomalies
    decision trees
    artificial neural network
    earthquakes
    Opis:
    The time series of radon (222Rn) concentration in soil gas at a fault, together with the environmental parameters, have been analysed applying two machine learning techniques: (i) decision trees and (ii) neural networks, with the aim at identifying radon anomalies caused by seismic events and not simply ascribed to the effect of the environmental parameters. By applying neural networks, 10 radon anomalies were observed for 12 earthquakes, while with decision trees, the anomaly was found for every earthquake, but, undesirably, some anomalies appeared also during periods without earthquakes.
    Źródło:
    Nukleonika; 2010, 55, 4; 501-505
    0029-5922
    1508-5791
    Pojawia się w:
    Nukleonika
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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