Temporal Trends and Spatial Relationships of Radioactive Isotopes (I-131, Cs-134, and Cs-137) in Response to Nuclear Events: A Comprehensive Analysis Using Time Series Graphs, Regression, and Multivariate Techniques

B. Nasution, W. Ritonga, A. Doyan, P. D. Pandara, L. Alfaris, R. C. Siagian

Abstract


This research aims to comprehend the evolution of radioactive isotopes      Iodine-131 (I-131), Cesium-134 (Cs-134), and Cesium-137 (Cs-137) over time in diverse locations and analyze their relationships with the independent variables Longitude and Latitude using Linear Regression, Principal Component Analysis (PCA), and Canonical Correlation Analysis (CCA). The data used in this study were processed from the "DE.xlsx" file, including the imputation of missing values with 0 and column transformation into factors. The results of the Linear Regression analysis indicate a significant association between these isotopes and Longitude and Latitude. Additionally, PCA and CCA analyses reveal complex relationships between the isotopes and independent variables. This research provides valuable insights into the historical trends of radioactive isotopes Iodine-131 (I-131), Cesium-134 (Cs-134), and Cesium-137 (Cs-137) in various locations. The novel aspect and uniqueness of this study lie in the utilization of a comprehensive analytical approach, combining Linear Regression, PCA, and CCA to comprehend the relationships between isotopes and specific environmental factors. Moreover, this study significantly contributes to understanding the phenomena of radioactive isotopes and can serve as a foundation for further research in this field. The findings of this research are expected to support efforts in preventing and managing potential environmental and human health impacts of radioactive isotopes in the future.


Keywords


Radioactive isotopes; Evolution; Longitude and latitude; Linear regression; Principal component analysis (PCA)

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References


I. Zuba, M. Zuba, M. Piotrowski et al., Appl. Radiat. Isot. 162 (2020) 109176.

Q. T. Birch, P. M. Potter, P. X. Pinto et al., Talanta 224 (2021) 121743.

L. Terzi, G. Wotawa, M. Schoeppner et al., Sci. Rep. 10 (2020) 1.

M. Aldener, A. Axelsson, T. Fritioff et al., J. Environ. Radioact. 262 (2023) 107159.

P. A. Assimakopoulos, K. G. Ioannides, Pakou et al., J. Dairy Sci. 70 (1987) 1338.

S. Hashimoto, M. Komatsu and S. Miura, Forest Radioecology in Fukushima, Springer Nature Singapore, Singapore (2022) 1.

O. Masson, J. Tschiersch, L. S. Lebel et al., Radioiodine Releases in Nuclear Emergency Scenarios, in: Nuclear Emergencies, Springer, Japan (2019) 175.

J. Iqbal, F. M. Howari, A. M. O. Mohamed et al., Assessment of Radiation Pollution from Nuclear Power Plants, in: Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering, Elsevier, United States (2021) 1027.

S. Kang, Q. Zhang, Y. Qian et al., Natl. Sci. Rev. 6 (2019) 796.

C. Selvan and S. R. Balasundaram, Data Analysis in Context-Based Statistical Modeling, in: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics, IGI Global, United States (2021) 96.

S. Senthilnathan, Usefulness of Correlation Analysis. https://papers.ssrn.com/sol3/papers. cfm? abstract_id=3416918. Retrieved in February (2023).

P. Schober, T. R. Vetter, Anesth. Analg. 132 (2021) 365.

I. J. B. D. Nascimento, D. P. O. Mathúna, T. C. V. Groote et al., BMC Infect. Dis. 21 (2021) 525.

K. S. J. Glew, B. Espinasse, B. P. V. Hunt et al., Trueman, Global Biogeochem. Cycles 35 (2021) 1.

P. Mishra, C. Pandey, U. Singh et al., Ann. Card. Anaesth. 22 (2019) 297.

G. Francis, V. Jakicic, Behav. Res. Methods 55 (2023) 77.

A. Beyer, Introduction to t-tests, in: Introduction to Statistics for Psychology, Pressbooks, Canada (2021) 1.

P. Maneejuk and W. Yamaka, J. Appl. Stat. 48 (2021) 827.

C. Andrade, Indian J. Psychol. Med. 41 (2019) 203.

L. Hespanhol, C. S. Vallio, L. M. Costa et al., Braz. J. Phys. Ther. 23 (2019) 290.

Z. Hu, Y. Yang, G. Li et al., Scand. J. Stat. 50 (2023) 1884.

J. Salupeto-Dembo, Z. Szabó-Krausz, P. Völgyesi et al., J. Radioanal. Nucl. Chem. 325 (2020) 271.

H. S. Darling, Cancer Res. Stat. Treat. 5 (2022) 139.

R. A. Armstrong, Ophthalmic Physiol. Opt. 39 (2019) 316.

B. Pratap, S. Mondal and B. H. Rao, Asian J. Civ. Eng. (2023) 1.

E. Seeram, Radiol. Technol. 91 (2019) 176.

G. Perinetti, South Eur. J. Orthod. Dentofac. Res. 6 (2019).

M. J. Osei and J. Kim, Econ. Modell. 93 (2020) 154.

E. Temizhan, H. Mirtagioglu and M. Mendes, Am. Acad. Sci. Res. J. Eng. Technol. Sci. 85 (2022) 265.

J. Hou, X. Ye, W. Feng et al., BMC Bioinf. 23 (2022) 81.

A. Shekhar, S. Patel, Int. J. Res. Appl. Sci. Eng. Technol. 10 (2022) 653.

H. Mohajan, J. Econ. Dev. Environ. People 9 (2020) 50.

Q. Yang, A. M. Williamson, A. Hasted et al., Food Qual. Preference 83 (2020) 103928.

R. J. Smith, Am. J. Phys. Anthropol. 172 (2020) 521.

N. S. Turhan, Educ. Res. Rev. 15 (2020) 575.

M. Whatley, Introduction to Quantitative Analysis for International Educators, Springer, USA (2022) 57.

J. Shrestha, P-Value: a True Test of Significance in Agricultural Research https://www. linkedin.com/ pulse/p-value-test-significance-agricultural-rese arch-jiban-shrestha/. Retrieved in February (2023).

A. E. Kossovsky, Stats 4 (2021) 419.

N. Shrestha, Am. J. Appl. Math. Stat. 9 (2021) 4.

N. Shrestha, Am. J. Appl. Math. Stat. 8 (2020) 39.

A. Robitzsch, Front. Educ. 5 (2020) 1.

E. Bergqvist, T. Tossavainen and M. Johansson, Educ. Res. Int. 2020 (2020) 1.

B. G. Hansen and O. Østerås, Prev. Vet. Med. 170 (2019) 104741.

G. D. Garson, Factor Analysis and Dimension Reduction in R, Routledge, London (2022) 1.

J. M. D. Ampong, J. A. Bagares, J. A. Berja et al., Can. J. Fam. Youth 15 (2023) 90.

D. Almaleki, Eng. Technol. Appl. Sci. Res. 11 (2021) 6849.

M. J. Kochenderfer and T. A. Wheeler, Algorithms for Optimization, The MIT Press, London (2019) 1.

G. Pavlov, D. Shi and A. Maydeu-Olivares, Struct. Equation Model. 27 (2020) 908.

C. Lasanthika, K. Wanigasuriya, U. Hettiaratchi et al., J. Patient Exper. 10 (2023) 1.

A. Grant, S. Lagdon, J. Devaney et al., PLoS One 18 (2023) e0285835.

Y. Takane and H. Hwang, Multivar. Behav. Res. 37 (2002) 163.

A. Balasubramaniam, M. J. J. Gul, V. G. Menon et al., IETE Tech. Rev. 38 (2021) 438.

B. M. S. Hasan and A. M. Abdulazeez, J. Soft Comput. Data Min. 2 (2021) 20.

F. Kherif, A. Latypova, Principal Component Analysis, in: Machine Learning, Elsevier, United Kingdom (2020) 209.

J. F. Hair, M. C. Howard and C. Nitzl, J. Bus. Res. 109 (2020) 101.

N. Kettaneh, A. Berglund and S. Wold, Comput. Stat. Data Anal. 48 (2005) 69.

D. N. Lawley and A. E. Maxwell, J. R. Stat. Soc. D 12 (1962) 209.

N. Salem and S. Hussein, Procedia Comput. Sci. 163 (2019) 292.

Y. Li, J. Ren, Y. Yan et al., J. Phys. Conf. Ser. 2278 (2022) 012021.

K. M. M. Uddin, N. Biswas, S. T. Rikta et al., Comput. Methods Programs Biomed. Update 3 (2023) 100098.

J. W. Choi, F. Zotter, B. Jo et al., IEEE/ACM Trans. Audio Speech Lang. Process. 30 (2022) 2310.

K. Martinkus, A. Loukas, N. Perraudin et al., SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators, Proceedings of the 39th International Conference on Machine Learning (2022) 15159.

X. Zhuang, Z. Yang and D. Cordes, Hum. Brain Mapp. 41 (2020) 3807.

W. Wang and Y. H. Zhou, J. Multivar. Anal. 185 (2021) 104781.




DOI: https://doi.org/10.55981/aij.2023.1306

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