Literature DB >> 19155048

Temporal variations of radon concentration in the saturated soil of Alpine grassland: the role of groundwater flow.

Frédéric Perrier1, Patrick Richon, Jean-Christophe Sabroux.   

Abstract

Radon concentration has been monitored from 1995 to 1999 in the soil of the Sur-Frêtes ridge (French Alps), covered with snow from November to April. Measurements were performed at 70 cm depth, with a sampling time of 1 h, at two points: the summit of the ridge, at an altitude of 1792 m, and the bottom of the ridge, at an altitude of 1590 m. On the summit, radon concentration shows a moderate seasonal variation, with a high value from October to April (winter), and a low value from May to September (summer). At the bottom of the ridge, a large and opposite seasonal variation is observed, with a low value in winter and a high value in summer. Fluctuations of the radon concentration seem to be associated with temperature variations, an effect which is largely delusory. Indeed, these variations are actually due to water infiltration. A simplified mixing model is used to show that, at the summit of the ridge, two effects compete in the radon response: a slow infiltration response, rich in radon, with a typical time scale of days, and a fast infiltration of radon-poor rainwater. At the bottom of the ridge, similarly, two groundwater contributions compete: one slow infiltration response, similar to the response seen at the summit, and an additional slower response, with a typical time scale of about a month. This second slower response can be interpreted as the aquifer discharge in response to snow melt. This study shows that, while caution is necessary to properly interpret the various effects, the temporal variations of the radon concentration in soil can be understood reasonably well, and appear to be a sensitive tool to study the subtle interplay of near surface transfer processes of groundwater with different transit times.

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Year:  2009        PMID: 19155048     DOI: 10.1016/j.scitotenv.2008.12.018

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


  2 in total

1.  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

2.  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

  2 in total

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