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Je, Hyun-Kuk; Kang, Chigu; Chon, Hyo‐Taek

doi:10.1023/a:1006613016217

Cited by 16 publications

(3 citation statements)

References 6 publications

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“…Notably, pores saturated with water trap radon in the soil and slow its transport through soil into the atmosphere (Kellenbenz and Shakya, 2021;Shahrokhi and Kovacs, 2021). However, soil moisture content can influence radon escape from mineral matter if fewer than 30% of the soil pores are filled with water; higher soil moisture content leads to a considerable reduction in radon emanation because of decreased gas permeability (Je et al, 1999). Furthermore, large valley depth values indicate areas with low elevation and gentle slopes (Figure 3).…”

Section: Geospatial Database Of Radon Factorsmentioning

confidence: 99%

Application of Machine Learning Algorithms for Geogenic Radon Potential Mapping in Danyang-Gun, South Korea

Rezaie

Kim

Alizadeh

et al. 2021

Front. Environ. Sci.

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Continuous generation of radon gas by soil and rocks rich in components of the uranium chain, along with prolonged inhalation of radon progeny in enclosed spaces, can lead to severe respiratory diseases. Detection of radon-prone areas and acquisition of detailed knowledge regarding relationships between indoor radon variations and geogenic factors can facilitate the implementation of more appropriate radon mitigation strategies in high-risk residential zones. In the present study, 10 factors (i.e., lithology; fault density; mean soil calcium oxide [CaO], copper [Cu], lead [Pb], and ferric oxide [Fe2O3] concentrations; elevation; slope; valley depth; and the topographic wetness index [TWI]) were selected to map radon potential areas based on measurements of indoor radon levels in 1,452 dwellings. Mapping was performed using three machine learning methods: long short-term memory (LSTM), extreme learning machine (ELM), and random vector functional link (RVFL). The results were validated in terms of the area under the receiver operating characteristic curve (AUROC), root mean square error (RMSE), and standard deviation (StD). The prediction abilities of all models were satisfactory; however, the ELM model had the best performance, with AUROC, RMSE, and StD values of 0.824, 0.209, and 0.207, respectively. Moreover, approximately 40% of the study area was covered by very high and high-risk radon potential zones that mainly included populated areas in Danyang-gun, South Korea. Therefore, the map can be used to establish more appropriate construction regulations in radon-priority areas, and identify more cost-effective remedial actions for existing buildings, thus reducing indoor radon levels and, by extension, radon exposure-associated effects on human health.

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Section: Geospatial Database Of Radon Factorsmentioning

confidence: 99%

Application of Machine Learning Algorithms for Geogenic Radon Potential Mapping in Danyang-Gun, South Korea

Rezaie

Kim

Alizadeh

et al. 2021

Front. Environ. Sci.

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“…At the same time, the radon concentration in the water (6.04 Bq l −1 in the rainy season and 4.65 Bq l −1 in the dry season) was low. In this thermal region, the sedimentary rocks are the main geological unit (Sönmez 2003), and sedimentary rocks such as limestone, sandstone have lower radiation levels (Je et al 1999, Unscear 2000, Ramola et al 2006, Marocchi et al 2011.…”

Section: Geothermal Regionmentioning

confidence: 99%

Variation in the radon concentrations and outdoor gamma radiation levels in relation to different geological formations in the thermal regions of Bursa, Turkey

Akkaya¹,

Kahraman²,

Koray³

et al. 2016

J. Radiol. Prot.

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Spring waters used as spas and their region may contain significant amounts of natural radionuclides. The main sources of exposure are the inhalation of radon and its decay products released from the water and soil and terrestrial gamma-radiation. In order to evaluate the potential risk of thermal regions in Bursa, located in the impact area of the NAF (North Anatolian Fault), radon and thoron concentrations in soil gas, radon concentrations in thermal waters and outdoor gamma radiation levels were measured in thermal regions that have different geological formations. The radon and thoron concentrations in soil-gas were found to vary from 2272 ± 121 to 245196 ± 3455 Bq m and from 999 ± 218 to 178 848 ± 17 742 Bq m, respectively. The radon concentrations in thermal waters ranged from 0.99 ± 0.21 to 226.74 ± 2.51 Bq l in the rainy season and from 0.26 ± 0.10 to 178.03 ± 12.86 Bq l in the dry season. The measured outdoor gamma radiation levels varied from 38 to 180 nGy h. The gamma dose rates were found to be strong positively correlating with the radon and thoron concentrations in soil-gas. The radon and outdoor gamma radiation levels were observed to be a function of the geological formations of the area.

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“…222 Rn, 220 Rn and their daughter products emit alpha particles, damaging lung tissue if inhaled (10,(25)(26)(27). 222 Rn and 220 Rn concentrations naturally vary geo-spatially, influenced by soil geochemistry, soil permeability and underlying geogenic factors (28)(29)(30)(31)(32). Furthermore, indoor radon (hereafter referred to as InRn) accumulation is also influenced by weather/climate, housing and occupancy characteristics (33)(34)(35)(36)(37).…”

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confidence: 99%

A user’s guide to radon priority areas, examples from Ireland

Hughes

Elío

Crowley

2022

radon

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Exposure to radon over time has significant detrimental effects on human health. Approximately 226,000 annual radon-related deaths have been reported from 66 countries (1). Many countries have a radon action plan, in order to reduce the harmful effects of radon exposure on the general public. Maps are routinely used to assist with mitigation strategies and delineate areas of priority regulation. Standard regulations in the European Union include the requirement for workplaces to test and the requirement to have reduction methods in newly built homes. Such laws are assigned systematically to areas that are understood to have high values of indoor radon. This article demonstrates that the boundaries of radon priority areas may vary, depending on the data set and methods used. We propose a table and a decision matrix to assist in choosing the most appropriate visual aid according to the purpose for which the map is to be used. We conclude that no single radon map is suitable to fit all objectives, and some maps are more suitable than others depending on the purpose.

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Untitled

Cited by 16 publications

References 6 publications

Application of Machine Learning Algorithms for Geogenic Radon Potential Mapping in Danyang-Gun, South Korea

Application of Machine Learning Algorithms for Geogenic Radon Potential Mapping in Danyang-Gun, South Korea

Variation in the radon concentrations and outdoor gamma radiation levels in relation to different geological formations in the thermal regions of Bursa, Turkey

A user’s guide to radon priority areas, examples from Ireland

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