■核设施周围放射性水平的模型预测是评估人类健康风险和环境影响的有用工具。我们的目标是开发一种模型,用于预测世界上第一个AP1000核电机组的环境和食物中的放射性水平。
■在这项工作中,我们报告了一项使用时间序列放射性监测数据建立自回归综合移动平均(ARIMA)模型预测放射性水平的初步研究.模型采用贝叶斯信息准则(BIC)进行筛选,模型精度用平均绝对百分比误差(MAPE)评价。
■最优模型,ARIMA(0,0,0)×(0,1,1)4和ARIMA(4,0,1)用于预测食物中90Sr的活性浓度和累积环境剂量(CAD)。分别。从2023年第一季度(Q1)到第四季度(Q4),90Sr在食品和CAD中的预测值为0.067-0.77Bq/kg,和0.055-0.133mSv,分别。模型预测结果与观测值吻合较好,MAPE分别为21.4%和22.4%,分别。从2024年第一季度到第四季度,90Sr在食品和CAD中的预测值为0.067-0.77Bq/kg和0.067-0.129mSv,分别,与其他地方报告的值相当。
■本研究中开发的ARIMA模型显示出良好的短期可预测性,可用于三门核电站周围环境和食品放射性水平的动态分析和预测。
UNASSIGNED: Model 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.
UNASSIGNED: In 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).
UNASSIGNED: The 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.
UNASSIGNED: The 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.