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ObjectivesThis work aims to establish a dataset of ratios between tissue free water tritium (TFWT) and organically bound tritium (OBT) (containing both exchangeable and non-exchangeable OBT) in different types of seafood from Zhejiang province, China, thus to provide references for rapid screening of potential contamination of tritium in biological samples.MethodsFive types of seafood (fish, prawn, mussel, crab and kelp) were collected from Jiaxing, Ningbo, Zhoushan, Taizhou and Wenzhou city in Zhejiang Province in 2022, and measured for TFWT and OBT using tube furnace combustion system and liquid scintillation counting.ResultsThe observed activity concentrations of TFWT and OBT in this study were within the range of 1.01-10.09 Bq/L and 0.51-7.61 Bq/L, respectively. No significant difference in OBT/TFWT ratio was observed among different types of seafood (p > 0.05), among the geographic distribution of the five cities (p > 0.05), nor among the area of coastline, fishing ground and island (p > 0.05). Additionally, there was no significant difference in the OBT/TFWT ratio between seafood from area with and without nuclear power plant operation (p > 0.05). The mean OBT/TFWT ratio of fish, prawn, mussel, crab and kelp was 0.37 ± 0.19, 0.48 ± 0.27, 0.63 ± 0.33, 0.82 ± 0.50, and 0.64 ± 0.37, respectively. The mean and range of OBT/TFWT ratios were 0.59 ± 0.37 and 0.15-2.09 in all seafood collected from the five cities in Zhejiang Province, respectively.ConclusionsOur results indicate that tritium in seafood from coastal cities in Zhejiang Province is at the background level, and the operation of nuclear power plants in the province has no notable impact on the radioactivity levels of tritium in local marine biota. This study filled the gap in OBT/TFWT ratios in seafood, which provides new dataset for rapid detection of tritium contamination in nuclear emergencies.

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.