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Rezaie F, Panahi M, Bateni SM, Kim S, Lee J, Lee J, Yoo J, Kim H, Won Kim S, Lee S. Spatial modeling of geogenic indoor radon distribution in Chungcheongnam-do, South Korea using enhanced machine learning algorithms. ENVIRONMENT INTERNATIONAL 2023; 171:107724. [PMID: 36608375 DOI: 10.1016/j.envint.2022.107724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/22/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
Prolonged inhalation of indoor radon and its progenies lead to severe health problems for housing occupants; therefore, housing developments in radon-prone areas are of great concern to local municipalities. Areas with high potential for radon exposure must be identified to implement cost-effective radon mitigation plans successfully or to prevent the construction of unsafe buildings. In this study, an indoor radon potential map of Chungcheongnam-do, South Korea, was generated using a group method of data handling (GMDH) algorithm based on local soil properties, geogenic, geochemical, as well as topographic factors. To optimally tune the hyper-parameters of GMDH and enhance the prediction accuracy of modelling radon distribution, the GMDH model was integrated with two metaheuristic optimization algorithms, namely the bat (BA) and cuckoo optimization (COA) algorithms. The goodness-of-fit and predictive performance of the models was quantified using the area under the receiver operating characteristic (ROC) curve (AUC), mean squared error (MSE), root mean square error (RMSE), and standard deviation (StD). The results indicated that the GMDH-COA model outperformed the other models in the training (AUC = 0.852, MSE = 0.058, RMSE = 0.242, StD = 0.242) and testing (AUC = 0.844, MSE = 0.060, RMSE = 0.246, StD = 0.0242) phases. Additionally, using metaheuristic optimization algorithms improved the predictive ability of the GMDH. The GMDH-COA model showed that approximately 7 % of the total area of Chungcheongnam-do consists of very high radon-prone areas. The information gain ratio method was used to assess the predictive ability of considered factors. As expected, soil properties and local geology significantly affected the spatial distribution of radon potential levels. The radon potential map produced in this study represents the first stage of identifying areas where large proportions of residential buildings are expected to experience significant radon levels due to high concentrations of natural radioisotopes in rocks and derived soils beneath building foundations. The generated map assists local authorities to develop urban plans more wisely towards region with less radon concentrations.
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Affiliation(s)
- Fatemeh Rezaie
- Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro, Yuseong-gu, Daejeon 34132, Republic of Korea; Department of Geophysical Exploration, Korea University of Science and Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea; Department of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA
| | - Mahdi Panahi
- Division of Science Education, Kangwon National University, 1, Gangwondaehak-gil, Chuncheon-si, Gangwon-do 24341, Republic of Korea
| | - Sayed M Bateni
- Department of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA
| | - Seonhong Kim
- Indoor Environment and Noise Research Division, Environmental Infrastructure Research Department, National Institute of Environmental Research, Seo-gu, Incheon 22689, Republic of Korea
| | - Jongchun Lee
- Indoor Environment and Noise Research Division, Environmental Infrastructure Research Department, National Institute of Environmental Research, Seo-gu, Incheon 22689, Republic of Korea
| | - Jungsub Lee
- Indoor Environment and Noise Research Division, Environmental Infrastructure Research Department, National Institute of Environmental Research, Seo-gu, Incheon 22689, Republic of Korea
| | - Juhee Yoo
- Indoor Environment and Noise Research Division, Environmental Infrastructure Research Department, National Institute of Environmental Research, Seo-gu, Incheon 22689, Republic of Korea
| | - Hyesu Kim
- Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro, Yuseong-gu, Daejeon 34132, Republic of Korea; Department of Astronomy, Space Science and Geology, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Sung Won Kim
- Geology Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro, Yuseong-gu, Daejeon 34132, Republic of Korea
| | - Saro Lee
- Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro, Yuseong-gu, Daejeon 34132, Republic of Korea; Department of Geophysical Exploration, Korea University of Science and Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea.
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Gini Method Application: Indoor Radon Survey in Kpong, Ghana. ATMOSPHERE 2022. [DOI: 10.3390/atmos13081179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this study, the indoor radon concentrations map, starting from a sparse measurements survey, was realized with the Gini index method. This method was applied on a real dataset coming from indoor radon measurements carried out in Kpong, Ghana. The Gini coefficient variogram is shown to be a good estimator of the inhomogeneity degree of radon concentration because it allows for better constraining of the critical distance below which the radon geological source can be considered as uniform. The indoor radon measurements were performed in 96 dwellings in Kpong, Ghana. The data showed that 84% of the residences monitored had radon levels below 100 Bqm−3, versus 16% having levels above the World Health Organization’s (WHO) suggested reference range (100 Bqm−3). The survey indicated that the average indoor radon concentration (IRC) was 55 ± 36 Bqm−3. The concentrations range from 4–176 Bqm−3. The mean value 55 Bqm−3 is 38% higher than the world’s average IRC of 40 Bqm−3 (UNSCEAR, 1993).
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