ArticleFrontiers in public health2024
Model prediction of radioactivity levels in the environment and food around the world's first AP 1000 nuclear power unit.
Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Assessment of radionuclide levels, annual effective dose, and lifetime cancer risk from cultivated crop samples in Hainan Island, China.Frontiers in public health · 2026Article
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12 authors.
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Abstract
Objectives: 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. Methods: 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). Results: The optimal models, ARIMA Conclusion: 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.
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