Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms. (1st January 2022)

• Record Type: Journal Article
• Authors: Rezaie, Fatemeh; Panahi, Mahdi; Lee, Jongchun; Lee, Jungsub; Kim, Seonhong; Yoo, Juhee; Lee, Saro
• Is Part Of: Environmental pollution. Volume 292:Part B(2022)
• Publication Date: 2022-01-01
• Subjects: Radon potential map -- Frequency ratio -- Convolutional neural network -- GIS -- Jangsu-gun; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2021.118385
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and machine learning methods, including the frequency ratio (FR) model and a convolutional neural network (CNN). Ten influencing factors, namely elevation, slope, the topographic wetness index (TWI), valley depth, fault density, lithology, and the average soil copper (Cu), calcium oxide (Cao), ferric oxide (Fe2 O3 ), and lead (Pb) concentrations, were analyzed. In total, 27 rock samples with high activity concentration index values were divided randomly into training and validation datasets (70:30 ratio) to train the models. Areas were categorized as very high, high, moderate, low, and very low radon areas. According to the models, approximately 40% of the study area was classified as very high or high risk. Finally, the radon potential maps were validated using the area under the receiver operating characteristic curve (AUC) analysis. This showed that the CNN algorithm was superior to the FR method; for the former, AUC values of 0.844 and 0.840 were obtained using the training and validation datasets, respectively. However, both algorithms had high predictive power. Slope, lithology, and TWI were the best predictors of radon-affected areas. These results provide new information regarding the spatial distribution of radon, and could inform the development of new residential areas. Radon screening is important to reduce public exposure to high levels of naturally occurring radiation. Graphical abstract: Image 1 Highlights: Geogenic radon potential maps are generated using FR and CNN methods. Slope, lithology, and TWI have significant influence on modelling radon level. Approximately 20% of the study area is classified as very high risk for radon gas.