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

• Published in: Environmental earth sciences Vol. 76; no. 15; p. 1
• Main Authors: Hwang, Jaehong, Kim, Taeseung, Kim, Hyungu, Cho, Byongwook, Lee, Saro
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-08-2017; Springer Nature B.V
• Subjects: Banks (topography); Biogeosciences; Case studies; Contaminants; Criteria; Earth and Environmental Science; Earth Sciences; Electrical conductivity; Electrical resistivity; Environmental Science and Engineering; Geochemistry; Geographic information systems; Geographical information systems; Geological mapping; Geological maps; Geology; Groundwater; Hydrology/Water Resources; Information systems; Lithology; Mapping; Original Article; Probabilistic methods; Radon; Radon isotopes; Radon levels; Remote sensing; Satellite navigation systems; Silicon dioxide; Slope; Soil; Soil contamination; Terrestrial Pollution; Topographic mapping; Topographic maps; Topography; Topography (geology); GIS; Potential mapping; Frequency ratio; Radon; Korea
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-017-6838-8

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), SiO
2
, Sr, NO
3
, HCO
3
, DEM, and geology) had close relationships with
222
Rn 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.