Browse > Article
http://dx.doi.org/10.1016/j.net.2021.07.001

Use of big data for estimation of impacts of meteorological variables on environmental radiation dose on Ulleung Island, Republic of Korea  

Joo, Han Young (Department of Nuclear Engineering, Dankook University)
Kim, Jae Wook (Department of Nuclear Engineering, Dankook University)
Jeong, So Yun (Department of Nuclear Engineering, Dankook University)
Kim, Young Seo (Department of Nuclear Engineering, Dankook University)
Moon, Joo Hyun (Department of Nuclear Engineering, Dankook University)
Publication Information
Nuclear Engineering and Technology / v.53, no.12, 2021 , pp. 4189-4200 More about this Journal
Abstract
In this study, the relationship between the environmental radiation dose rate and meteorological variables was investigated with multiple regression analysis and big data of those variables. The environmental radiation dose rate and 36 different meteorological variables were measured on Ulleung Island, Republic of Korea, from 2011 to 2015. Not all meteorological variables were used in the regression analysis because the different meteorological variables significantly affect the environmental radiation dose rate during different periods, and the degree of influence changes with time. By applying the Pearson correlation analysis and stepwise selection methods to the big dataset, the major meteorological variables influencing the environmental radiation dose rate were identified, which were then used as the independent variables for the regression model. Subsequently, multiple regression models for the monthly datasets and dataset of the entire period were developed.
Keywords
Environmental radiation dose rate; Meteorological variables; Big data; Pearson correlation; Multiple regression model;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Z. Liu, C.J. Sullivan, Prediction of weather induced background radiation fluctuation with recurrent neural networks, Radiat. Phys. Chem. 155 (2019) 275-280, https://doi.org/10.1016/j.radphyschem.2018.03.005.   DOI
2 A. Melintescu, S.D. Chambers, J. Crawford, A.G. Williams, B. Zorila, D. Galeriu, Radon-222 related influence on ambient gamma dose, J. Environ. Radioact. 189 (2018) 67-78, https://doi.org/10.1016/j.jenvrad.2018.03.012.   DOI
3 N. Fujinami, Observational study of the scavenging of radon daughters by precipitation from the atmosphere, Environ. Int. 22 (1997) 181-185, https://doi.org/10.1016/S0160-4120(96)00106-7.   DOI
4 Korea Institute of Nuclear Safety, Environmental Radioactivity Survey Data in Korea, vol. 43, KINS/ER-028, 2011.
5 K.Y. Kwahk, Ch. 7: Regression analysis, in: Statistical Data Analysis with R, Chungram, Seoul, 2019, pp. 140-220.
6 M.E. Kitto, D.K. Haines, H.D. Arauzo, Emanation of radon from household granite, Health Phys. 96 (2009) 477-482, https://doi.org/10.1097/01.HP.0000339000.33824.f6.   DOI
7 M.R. Morelande, B. Ristic, A. Gunatilaka, Detection and parameter estimation of multiple radioactive sources, in: Proceedings of the 10th International Conference on Information Fusion, IEEE, 2007, pp. 1-7.
8 M.R. Morelande, B. Ristic, Radiological source detection and localization using Bayesian techniques, IEEE Trans. Signal Process. 57 (2009) 4220-4231, https://doi.org/10.1109/TSP.2009.2026618.   DOI
9 A. V Shumakov, V.P. Kasatkin, Comparison of statistical methods of radiation monitoring, Atom. Energy 107 (2009) 165-172.
10 J.F. Mercier, B.L. Tracy, R. d'Amours, F. Chagnon, I. Hoffman, E.P. Korpach, S. Johnson, R.K. Ungar, Increased environmental gamma-ray dose rate during precipitation: a strong correlation with contributing air mass, J. Environ. Radioact. 100 (2009) 527-533, https://doi.org/10.1016/j.jenvrad.2009.03.002.   DOI
11 Korea Institute of Nuclear Safety, Environmental Radioactivity Survey Data in Korea, vol. 44, KINS/ER-028, 2012.
12 Korea Institute of Nuclear Safety, Environmental Radioactivity Survey Data in Korea, vol. 46, KINS/ER-028, 2014.
13 Korea Institute of Nuclear Safety, Environmental Radioactivity Survey Data in Korea, vol. 47, KINS/ER-028, 2015.
14 KMA, data.kma.go.kr.
15 M.K. Seo, Ch. 7: Statistical analysis, in: R for Practical Data Analysis, Gilbut, Seoul, 2014, pp. 342-350.
16 H.G. Kang, Ch. 14: Linear regression analysis, in: Statistical Methods for Health Care Research, sixth ed., Koonja, Paju, 2017, pp. 315-344.
17 R.J. Livesay, C.S. Blessinger, T.F. Guzzardo, P.A. Hausladen, Rain-induced increase in background radiation detected by Radiation Portal Monitors, J. Environ. Radioact. 137 (2014) 137-141, https://doi.org/10.1016/j.jenvrad.2014.07.010.   DOI
18 A. V Shumakov, V.P. Kasatkin, Comparison of statistical methods of radiation monitoring, 107, 2009, pp. 165-172.
19 K.K. Ryu, Ch. 18, Statistical significance, in: Statistics, Bobmunsa, Paju, 2013.
20 J.H. Kim, Ch. 9: Regression analysis, in: Environmental Statistics & Data Analysis, Hannarae, Seoul, 2018, pp. 205-269.
21 S. Menard, in: Applied Logistic Regression Analysis, second ed., SAGE Publication, California, 2001.
22 S.K. Hwang, Rock series and petrochemical classification of the volcanic rocks in Ulleung Island, J. Geol. Soc. Korea (2014) 61-70, https://doi.org/10.14770/jgsk.2014.50.1.61.   DOI
23 G.S. Kim, Ch. 9: Correlation analysis, in: R Big Data Analysis for Value Creation, Hannarae, Seoul, 2017, pp. 217-240.
24 S.H. Kang, Ch. 15: Multi regression analysis, in: Introductory Statistic, Free Academy, Paju, 2012, pp. 456-494.
25 Korea Institute of Nuclear Safety, Environmental Radioactivity Survey Data in Korea, vol. 45, KINS/ER-028, 2013.