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

• Published in: Nuclear engineering and technology Vol. 53; no. 12; pp. 4189 - 4200
• Main Authors: Joo, Han Young, Kim, Jae Wook, Jeong, So Yun, Kim, Young Seo, Moon, Joo Hyun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2021; Elsevier; 한국원자력학회
• Subjects: Big data; Environmental radiation dose rate; Meteorological variables; Multiple regression model; Pearson correlation; 원자력공학; Multiple regression model; Big data; Meteorological variables; Environmental radiation dose rate; Pearson correlation
• ISSN: 1738-5733; 2234-358X
• DOI: 10.1016/j.net.2021.07.001

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.