Literature DB >> 16904163

Meteorological parameters contributing to variability in 222Rn activity concentrations in soil gas at a site in Sapporo, Japan.

Ryoko Fujiyoshi1, Keita Sakamoto, Tsukushi Imanishi, Takashi Sumiyoshi, Sadashi Sawamura, Janja Vaupotic, Ivan Kobal.   

Abstract

Continuous (222)Rn monitoring in soil gas since November 22, 2004 has revealed variability in activity concentration with time in the semi-natural woods on the campus of Hokkaido University in Sapporo, Japan. Among various factors affecting soil radon levels and variability, temperature was found to be dominant during three seasons when activity concentrations of (222)Rn showed a diurnal high and nocturnal low with a boundary around 10 o'clock in the morning. This pattern was disturbed by low pressure fronts with occasional rain. The activity gradually decreased as soil temperatures decreased from late November to mid-December. After the ground surface was completely covered with snow, soil radon levels became low with a small fluctuation. There were several peaks of (222)Rn on the time-series chart in winter. Those peaks appearing in early winter and early spring may be interpreted by considering meteorological parameters. In a few cases, the radon activity suddenly increased with increasing pressure in the soil at a depth of 10 cm, which may be associated with subsurface events such as seismic activity in the area.

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Year:  2006        PMID: 16904163     DOI: 10.1016/j.scitotenv.2006.07.007

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  4 in total

1.  Tracing the sources of gaseous components (222Rn, CO2 and its carbon isotopes) in soil air under a cool-deciduous stand in Sapporo, Japan.

Authors:  Ryoko Fujiyoshi; Yukihide Haraki; Takashi Sumiyoshi; Hikaru Amano; Ivan Kobal; Janja Vaupotic
Journal:  Environ Geochem Health       Date:  2009-05-31       Impact factor: 4.609

2.  Decadal radon cycles in a hot spring.

Authors:  Rui Yan; Heiko Woith; Rongjiang Wang; Guangcai Wang
Journal:  Sci Rep       Date:  2017-09-21       Impact factor: 4.379

3.  Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data.

Authors:  Adil Aslam Mir; Kimberlee Jane Kearfott; Fatih Vehbi Çelebi; Muhammad Rafique
Journal:  PLoS One       Date:  2022-01-13       Impact factor: 3.240

4.  A radon-thoron isotope pair as a reliable earthquake precursor.

Authors:  Yong Hwa Oh; Guebuem Kim
Journal:  Sci Rep       Date:  2015-08-13       Impact factor: 4.379

  4 in total

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