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A mobile simulation and ARIMA modeling for prediction of air radiation dose rates

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Abstract

A spatial simulation method in.mp4 format was proposed to determine Fukushima radioactive fallout transport and the Absorbed Dose Rate, Annual Effective Dose Equivalent, and Excess Lifetime Cancer Risk were determined for 10 months after the accident (March 11 2011). The findings of this study demonstrate that an appropriate ARIMA model can be applied for radiation dose time-series in the case of nuclear reactor accidents like Chernobyl and Fukushima to predict the future air dose rates, which can provide valuable information in determining the evacuation zones, decontamination processes, and radiation protection progresses. The model forecasted results and the actual observation data in the same period shows a gradual decrease in the air dose rates during the prediction period. Moreover, there is a good agreement between them as the prediction and observation scatter plot follows each other with small variations. These results provide important insights into the predictability of ARIMA models; thus, the models were utilized to forecast the air dose rates for the period (January 2020–October 2020).

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References

  1. Xu S, Cook GT, Cresswell AJ, Dunbar E, Freeman SPHT, Hou X, Jacobsson P, Kinch HR, Naysmith P, Sanderson DCW, Tripney BG (2016) Radiocarbon releases from the 2011 Fukushima nuclear accident. Sci Rep 6(1):36947. https://doi.org/10.1038/srep36947

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Hatamura Y, Abe S, Fuchigami M, Kasahara N, Iino K (2014) The 2011 fukushima nuclear power plant accident: how and why it happened, 73. Woodhead publishing, Cambridge

    Google Scholar 

  3. Povinec P, Hirose K, Aoyama M (2013) Fukushima accident: radioactivity impact on the environment. Newnes, London

    Book  Google Scholar 

  4. Srinivasan TN, Gopi Rethinaraj TS (2013) Fukushima and thereafter: reassessment of risks of nuclear power. Energy Policy 52:726–736. https://doi.org/10.1016/j.enpol.2012.10.036

    Article  Google Scholar 

  5. Mimura N, Yasuhara K, Kawagoe S, Yokoki H, Kazama SJM, Change ASfG, (2011) Damage from the Great East Japan Earthquake and Tsunami: a quick report. Mitig Adapt Strateg Global Change 16(7):803–818. https://doi.org/10.1007/s11027-011-9297-7

    Article  Google Scholar 

  6. Bhattacharya S, Hyodo M, Goda K, Tazoh T, Taylor CA (2011) Liquefaction of soil in the Tokyo Bay area from the 2011 Tohoku (Japan) earthquake. Soil Dyn Earthq Eng 31(11):1618–1628. https://doi.org/10.1016/j.soildyn.2011.06.006

    Article  Google Scholar 

  7. Aoyama M (2018) Long-term behavior of 137Cs and 3H activities from TEPCO Fukushima NPP1 accident in the coastal region off Fukushima. Japan J Radioanal Nucl Chem 316(3):1243–1252. https://doi.org/10.1007/s10967-018-5815-3

    Article  CAS  Google Scholar 

  8. Okazumi T, Nakasu T (2015) Lessons learned from two unprecedented disasters in 2011: great East Japan Earthquake and Tsunami in Japan and Chao Phraya River flood in Thailand. Int J Dis Risk Red 13:200–206. https://doi.org/10.1016/j.ijdrr.2015.05.008

    Article  Google Scholar 

  9. Wheatley S, Sovacool BK, Sornette D (2016) Reassessing the safety of nuclear power. Energy Res Soc Sci 15:96–100. https://doi.org/10.1016/j.erss.2015.12.026

    Article  Google Scholar 

  10. Bilici A, Bilici S, Külahcı F (2020) Statistical analysis for 134 Cs and 137 Cs radioactivity risk levels modeling. J Radioanal Nucl Chem. https://doi.org/10.1007/s10967-020-07399-9

    Article  Google Scholar 

  11. Hisadome K, Onda Y, Loffredo N, Kawamori A, Kato H (2020) Spatial variation and radiocesium flux of litterfall in hardwood-pine mixed forest and cedar plantations based on long-term monitoring data. J Radioanal Nucl Chem. https://doi.org/10.1007/s10967-020-07433-w

    Article  Google Scholar 

  12. Hegedűs M, Shiroma Y, Iwaoka K, Hosoda M, Suzuki T, Tamakuma Y, Yamada R, Tsujiguchi T, Yamaguchi M, Ogura K, Tazoe H, Akata N, Kashiwakura I, Tokonami S (2020) Cesium concentrations in various environmental media at Namie, Fukushima. J Radioanal Nucl Chem 323(1):197–204. https://doi.org/10.1007/s10967-019-06942-7

    Article  CAS  Google Scholar 

  13. Taira Y, Hayashida N, Yamashita S, Kudo T, Matsuda N, Takahashi J, Gutevitc A, Kazlovsky A, Takamura N (2012) Environmental contamination and external radiation dose rates from radionuclides released from the Fukushima nuclear power plant. Radiat Protect Dosim 151(3):537–545. https://doi.org/10.1093/rpd/ncs040

    Article  CAS  Google Scholar 

  14. Hasegawa A, Ohira T, Maeda M, Yasumura S, Tanigawa K (2016) Emergency responses and health consequences after the fukushima accident; evacuation and relocation. Clin Oncol 28(4):237–244. https://doi.org/10.1016/j.clon.2016.01.002

    Article  CAS  Google Scholar 

  15. Endo S, Kimura S, Takatsuji T, Nanasawa K, Imanaka T, Shizuma K (2012) Measurement of soil contamination by radionuclides due to the Fukushima Dai-ichi nuclear power plant accident and associated estimated cumulative external dose estimation. J Environ Radioact 111:18–27. https://doi.org/10.1016/j.jenvrad.2011.11.006

    Article  CAS  PubMed  Google Scholar 

  16. Nihei N, Tanoi K, Nakanishi TM (2015) Inspections of radiocesium concentration levels in rice from Fukushima Prefecture after the Fukushima Dai-ichi nuclear power plant accident. Sci Rep. https://doi.org/10.1038/srep08653

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kato H, Onda Y, Yamaguchi T (2018) Temporal changes of the ambient dose rate in the forest environments of Fukushima Prefecture following the Fukushima reactor accident. J Environ Radioact 193–194:20–26. https://doi.org/10.1016/j.jenvrad.2018.08.009

    Article  CAS  PubMed  Google Scholar 

  18. Sun D, Wainwright HM, Oroza CA, Seki A, Mikami S, Takemiya H, Saito K (2020) Optimizing long-term monitoring of radiation air-dose rates after the Fukushima Daiichi nuclear power plant. J Environ Radioact. https://doi.org/10.1016/j.jenvrad.2020.106281

    Article  PubMed  Google Scholar 

  19. Sanada Y, Yoshimura K, Urabe Y, Iwai T, Katengeza EW (2020) Distribution map of natural gamma-ray dose rates for studies of the additional exposure dose after the Fukushima Dai-ichi Nuclear Power Station accident. J Environ Radioact 223–224:106397. https://doi.org/10.1016/j.jenvrad.2020.106397

    Article  CAS  PubMed  Google Scholar 

  20. Velasco H (2021) Temporal attenuation of gamma dose rate in air due to radiocesium downward mobility in soil. Health Phys 120(2):163–170

    Article  CAS  Google Scholar 

  21. Hirouchi J, Takahara S, Iijima M, Watanabe M, Munakata M (2017) Identification of penetration path and deposition distribution of radionuclides in houses by experiments and numerical model. Rad Phys Chem 140:127–131. https://doi.org/10.1016/j.radphyschem.2017.02.005

    Article  CAS  Google Scholar 

  22. Gonze M-A, Mourlon C, Calmon P, Manach E, Debayle C, Baccou J (2016) Modelling the dynamics of ambient dose rates induced by radiocaesium in the Fukushima terrestrial environment. J Environ Radioact 161:22–34. https://doi.org/10.1016/j.jenvrad.2015.06.003

    Article  CAS  PubMed  Google Scholar 

  23. Bilici S, Külahcı F, Bilici A (2019) Spatial modelling of Cs-137 and Sr-90 fallout after the Fukushima nuclear power plant accident. J Radioanal Nucl Chem 322(2):431–454. https://doi.org/10.1007/s10967-019-06713-4

    Article  CAS  Google Scholar 

  24. Külahcı F, Bilici A (2019) Advances on identification and animated simulations of radioactivity risk levels after Fukushima Nuclear Power Plant accident (with a data bank): a critical review. J Radioanal Nucl Chem 321(1):1–30. https://doi.org/10.1007/s10967-019-06559-w

    Article  CAS  Google Scholar 

  25. Külahcı F, Aközcan S, Günay O (2020) Monte Carlo simulations and forecasting of Radium-226, Thorium-232, and Potassium-40 radioactivity concentrations. J Radioanal Nucl Chem 324(1):55–70. https://doi.org/10.1007/s10967-020-07059-y

    Article  CAS  Google Scholar 

  26. Kim S, Ko W, Nam H, Kim C, Chung Y, Bang S (2017) Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost. Nucl Eng Technol 49(5):1063–1070. https://doi.org/10.1016/j.net.2017.05.007

    Article  Google Scholar 

  27. Ambrosino F, Thinová L, Briestenský M, Šebela S, Sabbarese C (2020) Detecting time-series anomalies using hybrid methods applied to Radon signals recorded in caves for possible correlation with earthquakes. Acta Geodaetica et Geophys 55:405–420

    Article  Google Scholar 

  28. Stránský V, Thinová L (2017) Radon concentration time-series modeling and application discussion. Rad Protect Dosimetry 177(1–2):155–159. https://doi.org/10.1093/rpd/ncx207%

    Article  Google Scholar 

  29. Cui L, Taira Y, Matsuo M, Orita M, Yamada Y, Takamura N (2020) Environmental remediation of the difficult-to-return zone in Tomioka Town, Fukushima Prefecture. Sci Rep 10(1):10165. https://doi.org/10.1038/s41598-020-66726-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ogura K, Hosoda M, Tamakuma Y, Suzuki T, Yamada R, Negami R, Tsujiguchi T, Yamaguchi M, Shiroma Y, Iwaoka K, Akata N, Shimizu M, Kashiwakura I, Tokonami S (2021) Discriminative measurement of absorbed dose rates in air from natural and artificial radionuclides in Namie Town, Fukushima Prefecture. Int J Environ Res Health policy 18(3):978. https://doi.org/10.3390/ijerph18030978

    Article  CAS  Google Scholar 

  31. Aközcan S, Külahcı F, Mercan Y (2018) A suggestion to radiological hazards characterization of 226Ra, 232Th, 40K and 137Cs: spatial distribution modelling. J Hazard Mater 353:476–489. https://doi.org/10.1016/j.jhazmat.2018.04.042

    Article  CAS  PubMed  Google Scholar 

  32. Wang KY, Nedelec P, Clark H, Harris N (2019) Measurements of atmospheric radioactivity dose rates over the North Pacific after the Fukushima Daiichi nuclear power plant accident during the period March 2011–2015. Earth Syst Sci Data Discuss. https://doi.org/10.5194/essd-2019-156

  33. Matsuura T (2021) Assessment of potentially reusable edible wild plant and mushroom gathering sites in eastern Fukushima based on external radiation dose. J Environ Radioact 227:106465. https://doi.org/10.1016/j.jenvrad.2020.106465

    Article  CAS  PubMed  Google Scholar 

  34. Bilici S, Bilici A, Külahcı F (2019) Transport modeling of 137Cs in soil after Fukushima Dai-Ichi Nuclear Power Plant accident by point cumulative semi-variogram method. Environ Earth Sci 78(6):212. https://doi.org/10.1007/s12665-019-8232-1

    Article  CAS  Google Scholar 

  35. IAEA (2012) Fukushima monitoring database. https://iec.iaea.org/fmd/monitoring_points.aspx. Accessed 11 Oct 19.

  36. Brockwell PJ, Brockwell PJ, Davis RA, Davis RA (2016) Introduction to time-series and forecasting. Springer, New York

    Book  Google Scholar 

  37. Külahcı F, Şen Z (2009) Spatio-temporal modeling of 210Pb transportation in lake environments. J Hazard Mater 165(1):525–532. https://doi.org/10.1016/j.jhazmat.2008.10.026

    Article  CAS  PubMed  Google Scholar 

  38. Ong C-S, Huang J-J, Tzeng G-H (2005) Model identification of ARIMA family using genetic algorithms. Appl Math Comput 164(3):885–912. https://doi.org/10.1016/j.amc.2004.06.044

    Article  Google Scholar 

  39. Zhang GP (2003) Time-series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175. https://doi.org/10.1016/S0925-2312(01)00702-0

    Article  Google Scholar 

  40. Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time-series analysis: forecasting and control. Wiley, Hoboken

    Google Scholar 

  41. Montanari A, Rosso R, Taqqu MS (1997) Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation, and simulation. Water Resour Res 33(5):1035–1044. https://doi.org/10.1029/97wr00043

    Article  Google Scholar 

  42. Murat M, Malinowska I, Gos M, Krzyszczak J (2018) Forecasting daily meteorological time-series using ARIMA and regression models. Int Agrophys 32(2):253–264. https://doi.org/10.1515/intag-2017-0007

    Article  Google Scholar 

  43. Wang HR, Wang C, Lin X, Kang J (2014) An improved ARIMA model for precipitation simulations. Nonlin Process Geophys 21(6):1159–1168. https://doi.org/10.5194/npg-21-1159-2014

    Article  Google Scholar 

  44. Feng S, Cai G (2016) Passenger flow forecast of metro station based on the ARIMA model. In: Qin Y, Jia L, Feng J, An M, Diao L (eds) Proceedings of the 2015 international conference on electrical and information technologies for rail transportation, pp 463–470. Springer, Berlin Heidelberg

  45. Wang D, Borthwick AG, He H, Wang Y, Zhu J, Lu Y, Xu P, Zeng X, Wu J, Wang L, Zou X, Liu J, Zou Y, He R (2018) A hybrid wavelet de-noising and rank-set pair analysis approach for forecasting hydro-meteorological time series. Environ Res 160:269–281. https://doi.org/10.1016/j.envres.2017.09.033

    Article  CAS  PubMed  Google Scholar 

  46. UNSCEAR (2015) Sources, effects and risks of ionizing radiation, united nations scientific committee on the effects of atomic radiation (UNSCEAR) 2012 report: report to the general assembly, with scientific annexes A and B. United Nations Fund for Population Activities,

  47. Ezekiel AO (2017) Assessment of excess lifetime cancer risk from gamma radiation levels in Effurun and Warri city of Delta state. Nigeria J Taibah Univ Sci 11(3):367–380. https://doi.org/10.1016/j.jtusci.2016.03.007

    Article  Google Scholar 

  48. Jackson PC (1996) Age-dependent doses to members of the public from intake of radionuclides: part 5 compilation of ingestion and inhalation dose coefficients (ICRP Publication 72). Phys Med Biol 41(12):2807–2807. https://doi.org/10.1088/0031-9155/41/12/017

    Article  Google Scholar 

  49. Aközcan S (2014) Annual effective dose of naturally occurring radionuclides in soil and sediment. Toxicol Environ Chem 96(3):379–386. https://doi.org/10.1080/02772248.2014.939177

    Article  CAS  Google Scholar 

  50. Taneja K, Ahmad S, Ahmad K, Attri SD (2016) Time-series analysis of aerosol optical depth over New Delhi using Box-Jenkins ARIMA modeling approach. Atmos Pollut Res 7(4):585–596. https://doi.org/10.1016/j.apr.2016.02.004

    Article  Google Scholar 

  51. Imai C, Armstrong B, Chalabi Z, Mangtani P, Hashizume M (2015) Time-series regression model for infectious disease and weather. Environ Res 142:319–327. https://doi.org/10.1016/j.envres.2015.06.040

    Article  CAS  PubMed  Google Scholar 

  52. Lin Y, Chen M, Chen G, Wu X, Lin T (2015) Application of an autoregressive integrated moving average model for predicting injury mortality in Xiamen. China BMJ Open 5(12):e008491. https://doi.org/10.1136/bmjopen-2015-008491

    Article  PubMed  Google Scholar 

  53. Alsharif MH, Younes MK, Kim J (2019) Time-series ARIMA model for prediction of daily and monthly average global solar radiation: the case study of Seoul. South Korea Symmetry 11(2):240. https://doi.org/10.3390/sym11020240

    Article  Google Scholar 

  54. Lugendo P, Tsegaye T, Wortman CJ, Neale CM (2017) climate variability implications for maize yield food security and rural poverty in tanzania. SSRN 3233487. https://doi.org/10.2139/ssrn.3233487

    Article  Google Scholar 

  55. Gairaa K, Khellaf A, Messlem Y, Chellali F (2016) Estimation of the daily global solar radiation based on Box-Jenkins and ANN models: a combined approach. Renew Sustain Energy Rev 57:238–249. https://doi.org/10.1016/j.rser.2015.12.111

    Article  Google Scholar 

  56. Katimon A, Shahid S, Mohsenipour MJSWRM (2018) Modeling water quality and hydrological variables using ARIMA: a case study of Johor River, Malaysia. Sustain Water Resour Manag 4(4):991–998. https://doi.org/10.1007/s40899-017-0202-8

    Article  Google Scholar 

  57. Hassan J (2014) ARIMA and regression models for prediction of daily and monthly clearness index. Renew Energy 68:421–427. https://doi.org/10.1016/j.renene.2014.02.016

    Article  Google Scholar 

  58. NIST/SEMATECH (2013) e-Handbook of statistical methods. https://www.itl.nist.gov/div898/handbook/. Accessed 25 Oct 2019

  59. Toki H, Wada T, Manabe Y, Hirota S, Higuchi T, Tanihata I, Satoh K, Bando M (2020) Relationship between environmental radiation and radioactivity and childhood thyroid cancer found in Fukushima health management survey. Sci Rep 10(1):4074. https://doi.org/10.1038/s41598-020-60999-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Yamamoto H, Hayashi K, Scherb H (2019) Association between the detection rate of thyroid cancer and the external radiation dose-rate after the nuclear power plant accidents in Fukushima. Japan. Med (Baltimore) 98(37):e17165–e17165. https://doi.org/10.1097/md.0000000000017165

    Article  CAS  Google Scholar 

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Acknowledgements

This research is part of Hemn Salh’s doctoral thesis. The authors would like to thank Firat University for providing a research environment.

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Authors and Affiliations

  1. Science Faculty, Physics Department, Nuclear Physics Division, Firat University, Elazig, Turkey

    Hemn Salh & Fatih Külahcı

  2. Department of Physics, Faculty of Science and Health, Koya University, Koya KOY45, Iraq

    Hemn Salh

  3. Art and Science Faculty, Physics Department, Kirklareli University, Kirklareli, Turkey

    Serpil Aközcan

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  1. Hemn Salh
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  2. Fatih Külahcı
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Correspondence to Fatih Külahcı.

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Salh, H., Külahcı, F. & Aközcan, S. A mobile simulation and ARIMA modeling for prediction of air radiation dose rates. J Radioanal Nucl Chem 328, 889–901 (2021). https://doi.org/10.1007/s10967-021-07726-8

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