Model prediction of radioactivity levels in the environment and food around the world’s first AP 1000 nuclear power unit

• DOI: 10.3389/fpubh.2024.1400680
• Publication Date: 2024-05-15T04:30:24Z
• Authors: Peng Wang; Wengzhe Huang; Hua Zou; Xiaoming Lou; Hong Ren; Shunfei Yu; Jiadi Guo; Lei Zhou; Zhongjun Lai; Dongxia Zhang; Zhiqiang Xuan; Yiyao Cao
• Keywords: food; Pilot Projects; Bayes Theorem; ARIMA; Models, Theoretical; 01 natural sciences; Radioactivity; Radiation Monitoring; radioactivity levels; Nuclear Power Plants; AP1000; time-series analysis; Humans; Public Health; Public aspects of medicine; RA1-1270; environment; Food Contamination, Radioactive; Forecasting; 0105 earth and related environmental sciences

ObjectivesModel prediction of radioactivity levels around nuclear facilities is a useful tool for assessing human health risks and environmental impacts. We aim to develop a model for forecasting radioactivity levels in the environment and food around the world’s first AP 1000 nuclear power unit.MethodsIn this work, we report a pilot study using time-series radioactivity monitoring data to establish Autoregressive Integrated Moving Average (ARIMA) models for predicting radioactivity levels. The models were screened by Bayesian Information Criterion (BIC), and the model accuracy was evaluated by mean absolute percentage error (MAPE).ResultsThe optimal models, ARIMA (0, 0, 0) × (0, 1, 1)4, and ARIMA (4, 0, 1) were used to predict activity concentrations of 90Sr in food and cumulative ambient dose (CAD), respectively. From the first quarter (Q1) to the fourth quarter (Q4) of 2023, the predicted values of 90Sr in food and CAD were 0.067–0.77 Bq/kg, and 0.055–0.133 mSv, respectively. The model prediction results were in good agreement with the observation values, with MAPEs of 21.4 and 22.4%, respectively. From Q1 to Q4 of 2024, the predicted values of 90Sr in food and CAD were 0.067–0.77 Bq/kg and 0.067–0.129 mSv, respectively, which were comparable to values reported elsewhere.ConclusionThe ARIMA models developed in this study showed good short-term predictability, and can be used for dynamic analysis and prediction of radioactivity levels in environment and food around Sanmen Nuclear Power Plant.