Abstract
This study depicts the radioactivity time series levels of 226Ra, 232Th, and 40K prospectively by Monte Carlo simulations (MCSs). Three ARIMA stationary stochastic processes are used with measurement records statistical parameter conservation. The MCSs by means of the ARIMA stochastic processes, the statistical characteristics of the radionuclide data are determined and the future simulation forecasts are made for different periods (time between two measurements, i.e. 1 week). Future concentrations of radionuclides with MCS are estimated for the first time. The results obtained on the transport, control and management of radionuclides can also reach similar gains for other different materials.
Graphical abstract
Similar content being viewed by others
References
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
Korkulu Z, Özkan N (2013) Determination of natural radioactivity levels of beach sand samples in the black sea coast of Kocaeli (Turkey). Radiat Phys Chem 88:27–31
Külahcı F (2011) A risk analysis model for radioactive wastes. J Hazard Mater 191(1–3):349–355
Celik N, Cevik U, Celik A, Koz B (2009) Natural and artificial radioactivity measurements in Eastern Black Sea region of Turkey. J Hazard Mater 162(1):146–153. https://doi.org/10.1016/j.jhazmat.2008.05.017
Sabiha J, Tufail M, Asghar M (2010) Hazard of NORM from phosphorite of Pakistan. J Hazard Mater 176(1–3):426–433. https://doi.org/10.1016/j.jhazmat.2009.11.047
Gorur FK, Camgoz H (2014) Natural radioactivity in various water samples and radiation dose estimations in Bolu province, Turkey. Chemosphere 112:134–140. https://doi.org/10.1016/j.chemosphere.2014.02.074
Uǧur A, Özden B, Filizok I (2011) Spatial and temporal variability of 210Po and 210Pb in mussels (Mytilus galloprovincialis) at the Turkish coast of the Aegean Sea. Chemosphere 83(8):1102–1107. https://doi.org/10.1016/j.chemosphere.2011.01.032
Hassan AK, Fares S, Abd El-Rahma M (2013) Natural radioactivity levels and radiation hazards for gypsum materials used in Egypt. J Environ Sci Technol 7(1):56–66. https://doi.org/10.3923/jest.2014.56.66
Menezes M, Maia ECP, Filho SS, Albinati C (2002) Assessment of occupational exposure and contamination by means of airborne particulate matter and biomonitors using k(0) instrumental neutron activation analysis. J Radioanal Nucl Chem 254(3):499–507. https://doi.org/10.1023/a:1021638021157
Borylo A (2013) Determination of uranium isotopes in environmental samples. J Radioanal Nucl Chem 295(1):621–631. https://doi.org/10.1007/s10967-012-1900-1
Yucel H, Karadeniz H, Cetiner MA, Demirel H, Turhan S (2003) Measurement of absolute intensity of 1001 keV gamma-ray of (234) mPa. J Radioanal Nucl Chem 258(2):445–447. https://doi.org/10.1023/a:1026226930151
Abbasi A, Hassanzadeh M (2017) Measurement and Monte Carlo simulation of γ-ray dose rate in high-exposure building materials. Nucl Sci Technol. https://doi.org/10.1007/s41365-016-0171-x
Abdollahnejad H, Vosoughi N, Zare MR (2016) Design and fabrication of an in situ gamma radioactivity measurement system for marine environment and its calibration with Monte Carlo method. Appl Radiat Isot 114:87–91. https://doi.org/10.1016/j.apradiso.2016.05.013
Ba VN, Loan TTH, Huy NQ (2018) Evaluation of characteristics of the peak-to-valley ratio versus material thickness in transmission gamma spectra by Monte Carlo simulation. J Radioanal Nucl Chem 317(3):1455–1461. https://doi.org/10.1007/s10967-018-6035-6
Çelik N (2012) Monte Carlo modelling of human body for dose conversion coefficients of 137Cs in soil of the Eastern Black Sea region of Turkey. Isot Environ Health Stud 48(2):280–285. https://doi.org/10.1080/10256016.2012.647815
Külahcı F (2020) Environmental distribution and modelling of radioactive lead (210): a Monte Carlo simulation application. In: Gupta DK, Chatterjee S, Walther C (eds) Lead in plants and the environment. Springer, Berlin, pp 15–32
Sang TT, Chuong HD, Tam HD (2019) Simple procedure for optimizing model of NaI(Tl) detector using Monte Carlo simulation. J Radioanal Nucl Chem 322(2):1039–1048. https://doi.org/10.1007/s10967-019-06787-0
Yoo DH, Shin WG, Lee J, Yeom YS, Kim CH, Chang BU, Min CH (2017) Development of an effective dose coefficient database using a computational human phantom and Monte Carlo simulations to evaluate exposure dose for the usage of NORM-added consumer products. Appl Radiat Isot 129:42–48. https://doi.org/10.1016/j.apradiso.2017.07.064
Rashed-Nizam QM, Tafader MK, Zafar M, Rahman MM, Bhuian AKMSI, Khan RA, Kamal M, Chowdhury MI, Alam MN (2016) Radiological risk analysis of sediment from Kutubdia island, Bangladesh due to natural and anthropogenic radionuclides. Intl J Radiat Res 14(4):373–377. https://doi.org/10.18869/acadpub.ijrr.14.4.373
Kawakami H, Honda MC, Watanabe S, Sino T (2014) Time-series observations of 210Po and 210Pb radioactivity in the western North Pacific. J Radioanal Nucl Chem 301(2):461–468. https://doi.org/10.1007/s10967-014-3141-y
Loos M, Krauss M, Fenner K (2012) Pesticide nonextractable residue formation in soil: insights from inverse modeling of degradation time series. Environ Sci Technol 46(18):9830–9837. https://doi.org/10.1021/es300505r
Yamanishi H, Miyake H (2003) Separation of natural background by using correlation of time-series data on radiation monitoring. J Nucl Sci Technol 40(1):44–48. https://doi.org/10.1080/18811248.2003.9715331
Zhang Y-J, Hu L-S, Bai T (2017) Online estimation of radionuclide transportation in water environment. J Radioanal Nucl Chem 314(2):1237–1244. https://doi.org/10.1007/s10967-017-5484-7
Hu Y, Wang Z, Wen J, Li Y (2016) Stochastic fuzzy environmental risk characterization of uncertainty and variability in risk assessments: a case study of polycyclic aromatic hydrocarbons in soil at a petroleum-contaminated site in China. J Hazard Mater 316:143–150. https://doi.org/10.1016/j.jhazmat.2016.05.033
Li J, He L, Lu H, Fan X (2014) Stochastic goal programming based groundwater remediation management under human-health-risk uncertainty. J Hazard Mater 279:257–267. https://doi.org/10.1016/j.jhazmat.2014.06.082
Li X, Li H, Liu Y, Xiong W, Fang S (2018) Joint release rate estimation and measurement-by-measurement model correction for atmospheric radionuclide emission in nuclear accidents: an application to wind tunnel experiments. J Hazard Mater 345:48–62. https://doi.org/10.1016/j.jhazmat.2017.09.051
Külahci F, Şen Z (2009) Potential utilization of the absolute point cumulative semivariogram technique for the evaluation of distribution coefficient. J Hazard Mater 168(2–3):1387–1396. https://doi.org/10.1016/j.jhazmat.2009.03.027
Külahcı F, Şen Z (2009) Spatio-temporal modeling of 210Pb transportation in lake environments. J Hazard Mater 165(1–3):525–532. https://doi.org/10.1016/j.jhazmat.2008.10.026
Hyndman RJ, Khandakar Y (2008) Automatic time series forecasting: the forecast package for R. J Stat Softw 27(3):1–22. https://doi.org/10.18637/jss.v027.i03
Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175
Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley, New York
Suganthi L, Samuel AA (2012) Energy models for demand forecasting—a review. Renew Sustain Energy Rev 16(2):1223–1240. https://doi.org/10.1016/j.rser.2011.08.014
Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407(1–4):28–40. https://doi.org/10.1016/j.jhydrol.2011.06.013
Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J Hydrol 476:433–441. https://doi.org/10.1016/j.jhydrol.2012.11.017
Quintela-del-Rio A, Francisco-Fernandez M (2011) Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis. Chemosphere 82(6):800–808. https://doi.org/10.1016/j.chemosphere.2010.11.025
Duenas C, Fernandez MC, Canete S, Carretero J, Liger E (2005) Stochastic model to forecast ground-level ozone concentration at urban and rural areas. Chemosphere 61(10):1379–1389. https://doi.org/10.1016/j.chemosphere.2005.04.079
Rubinstein RY, Kroese DP (2016) Simulation and the Monte Carlo method, vol 10. Wiley, New York
Aalizadeh R, Nika MC, Thomaidis NS (2019) Development and application of retention time prediction models in the suspect and non-target screening of emerging contaminants. J Hazard Mater 363:277–285. https://doi.org/10.1016/j.jhazmat.2018.09.047
Sechopoulos I, Rogers DWO, Bazalova-Carter M, Bolch WE, Heath EC, McNitt-Gray MF, Sempau J, Williamson JF (2018) RECORDS: improved reporting of Monte Carlo radiation transport studies: report of the AAPM Research Committee Task Group 268. Med Phys 45(1):e1–e5. https://doi.org/10.1002/mp.12702
Ahmadzadeh F (2018) Change point detection with multivariate control charts by artificial neural network. Int J Adv Manuf Technol 97(9–12):3179–3190. https://doi.org/10.1007/s00170-009-2193-6
Schuhmacher M, Meneses M, Xifro A, Domingo JL (2001) The use of Monte-Carlo simulation techniques for risk assessment: study of a municipal waste incinerator. Chemosphere 43(4–7):787–799. https://doi.org/10.1016/s0045-6535(00)00435-5
Toros H, Erdun H, Çapraz Ö, Özer B, Daylan EB, Öztürk Aİ (2013) Air Pollution and quality levels in metropolitans of turkey for sustainable life. EJOSAT Eur J Sci Technol 1(1):12–18
MTA (2018) General directorate of mineral research and explorations. Available via MTA. http://www.mta.gov.tr/eng/. Accessed 27 Dec 2018
Aközcan S (2014) Annual effective dose of naturally occurring radionuclides in soil and sediment. Toxicol Environ Chem 96(3):379–386
Ediger VŞ, Akar S (2007) ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy 35(3):1701–1708
Kumar U, Jain V (2010) ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO). Stoch Environ Res Risk Assess 24(5):751–760
Cholette PA (1982) Prior information and ARIMA forecasting. J Forecast 1(4):375–383
Xu X, Qi Y, Hua Z (2010) Forecasting demand of commodities after natural disasters. Expert Syst Appl 37(6):4313–4317
Li C, Chiang T-W (2013) Complex neurofuzzy ARIMA forecasting—a new approach using complex fuzzy sets. IEEE Trans Fuzzy Syst 21(3):567–584
MathWorks I (1996) MATLAB: application program interface guide, vol 5. MathWorks, Natick
Adam AM, Junior PO (2017) Financial econometrics: an example-based handbook. An example-based handbook. Financial Econometrics. Nova Science Publishers Inc, New York
Chatfield C (2016) The analysis of time series: An introduction. The Analysis of Time Series: An Introduction, 6th edn. CRC Press, Boca Raton
Ozcan T, Küçükdeniz T, Sezgin FH (2016) Comparative analysis of statistical, machine learning, and grey methods for short-term electricity load forecasting. In: Nature-inspired computing: concepts, methodologies, tools, and applications, vol 2–3. IGI Global, Hershey, pp 1161–1183. https://doi.org/10.4018/978-1-5225-0788-8.ch044
Ramarao NV, Babu PYY, Ganesh S, Rajendran C (2017) Multiobjective forecasting: time series models using a deterministic pseudo-evolutionary algorithm. In: Big data analytics using multiple criteria decision-making models. CRC Press, Boca Raton, pp 135–153. https://doi.org/10.1201/9781315152653
Fattah J, Ezzine L, Aman Z, El Moussami H, Lachhab A (2018) Forecasting of demand using ARIMA model. Int J Eng Bus Manag. https://doi.org/10.1177/1847979018808673
Ongbali SO, Igboanugo AC, Afolalu SA, Udo MO, Okokpujie IP (2018) Model selection process in time series analysis of production system with random output. Institute of Physics Publishing, Bristol. https://doi.org/10.1088/1757-899x/413/1/012057
Akaike H (1976) Canonical correlation analysis of time series and the use of an information criterion. Math Sci Eng. https://doi.org/10.1016/S0076-5392(08)60869-3
Box GEP, Jenkins GM, Reinsel GC (2013) Time series analysis: forecasting and control. Time Series Analysis: Forecasting and Control, 4th edn. Wiley, New York. https://doi.org/10.1002/9781118619193
Chatfield C (2000) Time-series forecasting. CRC Press, Boca Raton
Lütkepohl H (2005) New introduction to multiple time series analysis. New introduction to Multiple Time Series Analysis. Springer, Berlin. https://doi.org/10.1007/978-3-540-27752-1
Mills TC, Markellos RN (2008) The econometric modelling of financial time series. The Econometric Modelling of Financial Time Series. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511817380
Ljung L (1987) System identification: theory for the user. Prentice-Hall, Upper Saddle River
Chadwick MB, Herman M, Oblozinsky P et al (2011) ENDF/B-VII.1 nuclear data for science and technology: cross sections, covariances, fission product yields and decay data. NDS 112(12):2887–2996. https://doi.org/10.1016/j.nds.2011.11.002
Beringer J, Arguin JF, Barnett RM et al (2012) Review of particle physics. Particle Data Group. PhRvD 86 (1). https://doi.org/10.1103/physrevd.86.010001
Abdolhamidzadeh B, Abbasi T, Rashtchian D, Abbasi SA (2010) A new method for assessing domino effect in chemical process industry. J Hazard Mater 182(1–3):416–426. https://doi.org/10.1016/j.jhazmat.2010.06.049
Zhao Y, Nielsen CP, Lei Y, McElroy MB, Hao J (2011) Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China. Atmos Chem Phys 11(5):2295–2308. https://doi.org/10.5194/acp-11-2295-2011
Robert C, Casella G (2013) Monte Carlo statistical methods. Springer, Berlin
Akdi Y (2003) Zaman serileri analizi: Birim kökler ve kointegrasyon. Bıçaklar Kitabevi
Enders W (2008) Applied econometric time series. Wiley, New York
Burney SA, Raza SA (2007) Monte carlo simulation and prediction of Internet load using conditional mean and conditional variance model. In: Proceedings of the 9th Islamic countries conference on statistical sciences
Hamilton J (1994) Time series analysis. Princeton University Press Princeton, Cambridge
Faruk Y, Tüfekçí S (2017) Handbook of research on applied optimization methodologies in manufacturing systems. IGI Global, New York
Acknowledgements
This work was carried out using the Gamma Spectrometer Laboratory at Advanced Technologies Application and Research Center, Kirklareli University, Kirklareli, Turkey. We would like to thank Prof. Zsolt Révay for his positive scientific approach and management from the first presentation of this research to the final stage. On the other hand, both anonymous referees contributed a lot to the development of this article. We would like also to thank them for their scientific comments.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Külahcı, F., Aközcan, S. & Günay, O. Monte Carlo simulations and forecasting of Radium-226, Thorium-232, and Potassium-40 radioactivity concentrations. J Radioanal Nucl Chem 324, 55–70 (2020). https://doi.org/10.1007/s10967-020-07059-y
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10967-020-07059-y