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Predictive radon potential mapping in groundwater: a case study in Yongin, Korea

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Abstract

The purpose of this work was to calculate the relationships between radon levels in groundwater and spatial factors (e.g., geology, topography, soil, and geochemistry), to integrate these relationships, and to map the radon potential levels using a probabilistic method and geographic information systems (GIS) in Yongin, Korea. Radon in groundwater is affected by topographic factors such as elevation and slope, geological factors such as lithology and geochemical factors. A spatial database containing radon, topographic, soil, geological and geochemical data was compiled for the study area using GIS. We then extracted 33 factors from geological maps, topographic maps, and geochemical data. The relationships between radon occurrence and these factors were evaluated using the frequency ratio method, which is one of a probabilistic model. Seven of factors (electrical conductivity (EC), SiO2, Sr, NO3, HCO3, DEM, and geology) had close relationships with 222Rn occurence and were combined to produce a radon potential map using spatial overlay. This radon potential map was validated by comparing with the existing occurrences of radon gas. Of the total number of radon occurrences, 50% were used for mapping, and the remaining 50% were used for model validation. The average radon potential index (RPI) value with radon concentration greater than radon content criterion was 43.69% higher than its average RPI value less than the criterion value in our samples. The average RPI value was 43.96% higher than US EPA’s alternative maximum contaminant level of 148 Bq/L. The radon potential map constructed in this study can serve as an important reference for potential radon exposure.

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References

  • Akkala A, Devabhaktuni V, Kumar A, Bhatt D (2011) Development of an ANN interpolation scheme for estimating missing radon concentrations in Ohio. Open Environ Biol Monit J 4:21–30

    Article  Google Scholar 

  • Appleton JD, Miles JCH (2010) A statistical evaluation of the geogenic controls on indoor radon concentrations and radon risk. J Environ Radioact 101(10):799–803

    Article  Google Scholar 

  • Bossew P, Dubois G, Tollefsen T (2008) Investigations on indoor Radon in Austria, part 2: geological classes as categorical external drift for spatial modelling of the Radon potential. J Environ Radioact 99(1):81–97

    Article  Google Scholar 

  • Bossew P, Žunić ZS, Stojanovska Z, Tollefsen T, Carpentieri C, Veselinović N, Komatina S, Vaupotič J, Simović RD, Antignani S, Bochicchio F (2014) Geographical distribution of the annual mean radon concentrations in primary schools of Southern Serbia—application of geostatistical methods. J Environ Radioact 127:141–148

    Article  Google Scholar 

  • Ciotoli G, Voltaggio M, Tuccimei P, Soligo M, Pasculli A, Beaubien SE, Bigi S (2017) Geographically weighted regression and geostatistical techniques to construct the geogenic radon potential map of the Lazio region: a methodological proposal for the European Atlas of Natural Radiation. J Environ Radioact 166(2):355–375

    Article  Google Scholar 

  • Ding Q, Chen W, Hong H (2017) Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto Int 32(6):619–639

    Google Scholar 

  • Drolet JP, Martel R, Poulin P, Dessau JC, Lavoie D, Parent M, Lévesque B (2013) An approach to define potential radon emission level maps using indoor radon concentration measurements and radiogeochemical data positive proportion relationships. J Environ Radioact 124:57–67

    Article  Google Scholar 

  • Drolet JP, Martel R, Poulin P, Dessau JC (2014) Methodology developed to make the Quebec indoor radon potential map. Sci Total Environ 473–474:372–380

    Article  Google Scholar 

  • Ferrier KL, Kirchner JW (2008) Effects of physical erosion on chemical denudationrates: a numerical modeling study of soil-mantled hillslopes. Earth Planet Sci Lett 272(3–4):591–599

    Article  Google Scholar 

  • Gruber V, Bossew P, De Cort M, Tollefsen T (2013) The European map of the geogenic radon potential. J Radiol Prot 33(1):51–60

    Article  Google Scholar 

  • Lee S, Choi J (2004) Landslide susceptibility mapping using GIS and the weight-of evidence model. Int J Geogr Inf Sci 18(8):789–814

    Article  Google Scholar 

  • Lee SM, Kim HS, Song YS (1989) Explanatory note of the ANSONG sheet. Korean Institute of Geoscience and Mineral Resources, Daejeon, pp 1–18

    Google Scholar 

  • Lee BJ, Kim YB, Lee SL, Kim JC, Kang PJ, Choi HI, Jin MS (1999) Explanatory Note of the Seoul-Namchonjeom Sheet. Korean Institute of Geoscience and Mineral Resources, Daejeon, pp 1–64

    Google Scholar 

  • Lee S, Choi JK, Park I, Koo BJ, Ryu JH, Lee YK (2014) Application of geospatial models to map potential Ruditapes philippinarum habitat using remote sensing and GIS. Int J Remote Sens 35(10):3875–3891

    Article  Google Scholar 

  • Manthena DV, Kadiyala A, Kumar A (2009) Interpolation of radon concentrations using GIS-based kriging and cokriging techniques. Environ Prog Sustain Energy 28(4):487–492

    Article  Google Scholar 

  • Mose DG, Mushrush GW (1997) Variable spatial and seasonal hazards of airborne radon. Atmos Environ 31(21):3523–3530

    Article  Google Scholar 

  • Mose DG, Mushrush G, Siaway G (2006) Radioactivity in small homes using well water. Geol Soc Am Abstr Programs 38(1):3

    Google Scholar 

  • NIER (2012) Study on naturally occurring radioactive materials in groundwater in South Korea. National Institute of Environmental Research, NIER-RP 2012:196

  • NIER (2013) Studies on the naturally occurring radionuclides in groundwater of the Yongin high potential area, in the multi-geologic areas, National Institute of Environmental Research, NIER-SP2013-416, p 220

  • Norton K, von Blanckenburg F (2010) Silicate weathering of soil-mantled slopes in an active Alpine landscape. Geochim Cosmochim Acta 74(18):5243–5258

    Article  Google Scholar 

  • Oh HJ, Lee S (2008) Regional probabilistic and statistical mineral potential mapping of gold-silver deposits using GIS in the Gangreung Area, Korea. Res Geol 58(2):171–187

    Article  Google Scholar 

  • Pereira A, Lamas R, Miranda M, Domingos F, Neves L, Ferreira N, Costa L (2017) Estimation of the radon production rate in granite rocks and evaluation of the implications for geogenic radon potential maps: a case study in Central Portugal. J Environ Radioact 166(2):270–277

    Article  Google Scholar 

  • Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto Int 31(1):42–70

    Article  Google Scholar 

  • RDA (1990) Detailed soil map of YONG-IN-GUN at the scale of 1:25,000, Institute of Agricultural Science, Rural Development Administration

  • Scheib J, Höke A (2013) Advances in peripheral nerve regeneration. Nat Rev Neurol 9(12):668–676

    Article  Google Scholar 

  • Shi X, Hong T, Walter KL, Ewalt M, Michishita E, Hung T, Carney D, Peña P, Lan F, Kaadige MR, Lacoste N, Cayrou C, Davrazou F, Saha A, Cairns BR, Ayer DE, Kutateladze TG, Shi Y, Côté J, Chua KF, Gozani O (2006) ING2 PHD domain links histone H3 lysine 4 methylation to active gene repression. Nature 442(7098):96–99

    Article  Google Scholar 

  • Smethurst MA, Watson RJ, Baranwal VC, Rudjord AL, Finne I (2017) The predictive power of airborne gamma ray survey data on the locations of domestic radon hazards in Norway: a strong case for utilizing airborne data in large-scale radon potential mapping. J Environ Radioact 166(2):321–340

    Article  Google Scholar 

  • US National Institutes of Health (2016) National cancer institute fact sheet- radon and cancer: questions and answer. http://www.cancer.gov/cancertopics/factsheet/Risk/radon. Accessed on Jan 8

  • USEPA (1999) National primary drinking water regulations; Radon-222; Prosed rule, Federal Register. 64(211): 76708

  • Yeo SC, Lim JH (1974) Explanatory note of the Icheon sheet. Korean Institute of Geoscience and Mineral Resources, Daejeon, pp 1–15

    Google Scholar 

  • Yerrabolu P, Mareddy L, Bhatt D, Aggarwal P, Kumar A, Devabhaktuni V (2013) Correction model-based ANN modeling approach for the estimation of radon concentrations in Ohio. Environ Prog Sustain Energy 32(4):1223–1233

    Article  Google Scholar 

  • Yoo K, Mudd SM, Sanderman J, Amundson R, Blum A (2009) spatial patterns and controls of soil chemical weathering rates along a transient hillslope. Earth Planet Sci Lett 288:184–193

    Article  Google Scholar 

  • Zhu HC, Charlet JM, Poffijn A (2001) Radon risk mapping in southern Belgium: an application of geostatistical and GIS techniques. Sci Total Environ 272(1):203–210

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Minister of Science, ICT and Future Planning of Korea.

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Correspondence to Saro Lee.

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Hwang, J., Kim, T., Kim, H. et al. Predictive radon potential mapping in groundwater: a case study in Yongin, Korea. Environ Earth Sci 76, 515 (2017). https://doi.org/10.1007/s12665-017-6838-8

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