The development of nuclear power plants is progressing rapidly worldwide. However, there is currently a lack of dynamic monitoring of the thermal discharge temperature rise from these plants, making it unclear to governments where their nuclear power thermal discharges stand globally. We hypothesize that between 2013 and 2022, there are significant temporal and spatial differences in the thermal discharge temperature rise from nuclear power plants globally. Temporal differences are expected to reflect a country\'s nuclear power installed capacity and thermal discharge treatment capabilities, while spatial differences are related to the type of water bodies where nuclear power plants are located. To test these hypotheses, we utilized Landsat data to get the distribution range of thermal discharge and temperature rise levels ranging from 1 °C to 4 °C, and compared the temporal and spatial characteristics of temperature rise in different countries. The results indicate that: (1) Currently, China, the United States, and Canada rank among the top three globally in terms of the area experiencing temperature rise due to thermal discharge, which correlates with the total installed capacity of nuclear power in these countries. (2) Countries such as Russia, Finland, and Mexico exhibit larger areas with a 4 °C temperature rise level per unit installed capacity, with their thermal rise area per unit installed capacity (TRAUIC) exceeding the global average by more than 1.5 times. (3) The spatial dispersion trends of thermal discharges from nuclear power plants vary across different types of water bodies. For nuclear power plants located in bays, thermal discharges primarily disperse along the coast, while in open sea and lakes, thermal discharges tend to spread in a fan-shaped pattern. The findings of this study are crucial for understanding the efficiency of thermal discharge from nuclear power plants across different countries globally, assessing potential environmental risks during the operation of these plants, and promoting the safe and orderly development of nuclear power plants worldwide.

Global nuclear power is surging ahead in its quest for global carbon neutrality, eyeing an anticipated installed capacity of 436 GW for coastal nuclear power plants by 2040. As these plants operate, they emit substantial amounts of warm water into the ocean, known as thermal discharge, to regulate the temperature of their nuclear reactors. This discharge has the potential to elevate the temperature of the surrounding seawater, potentially influencing the marine ecosystem in the discharge vicinity. Therefore, our study area is on the Qinshan and Jinqimen Nuclear Power Plants in China, employing a blend of Landsat 8/9, and unmanned aerial vehicle (UAV) imagery to gather sea surface temperature (SST) data. In situ measurements validate the temperature data procured through remote sensing. Leveraging these SST observations alongside hydrodynamic and meteorological data from field measurements, we input them into the MIKE 3 model to prognosticate the three-dimensional (3D) spatial distribution and temperature elevation resulting from thermal discharge. The findings reveal that (1) satellite remote sensing can instantly acquire the horizontal distribution of thermal discharge, but with a spatial resolution much lower than that of UAV. The spatial resolution of UAV is higher, but the imaging efficiency of UAV is only 1/40,000 of that of satellite remote sensing. (2) Numerical simulation models can predict the 3D spatial distribution of thermal discharge. Although UAV and satellite remote sensing cannot directly obtain the 3D spatial distribution of thermal discharge, using remotely sensed SST as the temperature field input for the MIKE 3 model can reduce the quantity of measured temperature data and lower the cost of numerical simulation. (3) In the process of monitoring and predicting the thermal discharge of nuclear power plants, achieving an effective balance between monitoring accuracy and cost can be realized by comprehensively considering the advantages and costs of satellite, UAV, and numerical simulation technologies.

Reverse osmosis (RO) membrane fouling and biological contamination problems faced by seawater desalination systems are microbiologically related. We used full-length 16S rRNA gene sequencing to assess the bacterial community structure and chlorine-resistant bacteria (CRB) associated with biofilm growth in different treatment processes under the winter mode of a chlorinated seawater desalination system in China. At the outset of the winter mode, certain CRB, such as Acinetobacter, Pseudomonas, and Bacillus held sway over the bacterial community structure, playing a pivotal role in biofouling. At the mode\'s end, Deinococcus and Paracoccus predominated, with Pseudomonas and Roseovarius following suit, while certain CRB genera still maintained their dominance. RO and chlorination are pivotal factors in shaping the bacterial community structure and diversity, and increases in total heterotrophic bacterial counts and community diversity in safety filters may adversely affect the effectiveness of subsequent RO systems. Besides, the bacterial diversity and culturable biomass in the water produced by the RO system remain high, and some conditionally pathogenic CRBs pose a certain microbial risk as a source of drinking water. Targeted removal of these CRBs will be an important area of research for advancing control over membrane clogging and ensuring water quality safety in the future.

The reactor coolant pump is a key equipment in a nuclear power plant. If the leakage exceeds a certain threshold, it may cause reactor overheating and shutdown. The reactor coolant pump leakage fault usually has two problems: corrosion and scaling. Accurately and efficiently diagnosing the leakage fault mode as early as possible and predicting its remaining useful life (RUL) are important for taking timely maintenance measures. In this paper, an integrated method is proposed. First, the cross-sectional area of the first seal is extracted as a fault indicator. The motivation is that corrosion may enlarge the cross-sectional area, and scaling may reduce the cross-sectional area. Based on the fluid mechanics theory, an integrated model with several uncertain parameters is established among the cross-sectional area, temperature, and leakage at the inlet and outlet of the first seal. In the diagnosing process, a modified change-detection method is proposed to detect the starting point of degradation. Then, the unknown parameters in the previous relation are estimated, and the degrading data before the starting point of degradation are used to diagnose the leakage fault mode. Second, a time-series model of the autoregressive integrated moving average (ARIMA) is established to predict the remaining useful life based on the degrading data after the starting point of degradation. Finally, the leakage degrading data from six reactor coolant pumps of a nuclear power plant is used to perform the leakage fault mode diagnosis and life prediction with degradation point detection error rates not exceeding 4%, fault mode diagnosis correction rates 100% and practical RUL predicting results, which proves that the proposed integrated method is accurate and efficient. The proposed integrated method combines the advantages of both the physical model diagnosis and the data-driven model diagnosis and innovatively make use of the quantity of flow from the output side of the primary pump as the monitoring indicator and the cross-sectional area as the characteristic index together to diagnose the leakage fault mode happened to the seal and predict its RUL, which can meet the needs of actual operation and maintenance to ensure a healthy and stable operation of the pump and prevent unexpected shutdowns of nuclear power plants and serious accidents.

OBJECTIVE: Numerous epidemiological studies have shown increased health risks among workers and residents living near nuclear power plants exposed to radiation levels meeting regulatory dose limits. This study aimed to evaluate the association between radiation exposure and disease risks among these populations exposed to radiation levels meeting the current regulatory dose limits.
RESULTS: We searched four databases (Cochrane Library, PubMed, ScienceDirect, and Web of Science) for studies published before August 2023, screened eligible studies (inclusion and exclusion criteria based on population, exposure, comparator, and outcome framework), and collected data on exposure indicators and disease risks. We applied random-effects models of meta-analysis to estimate the pooled effects and meta-regression to assess the dose-response relationship (radiation dose rate for workers and distance for residents). We identified 47 studies, 13 with worker and 34 with resident samples, covering 175 nuclear power plants from 17 countries, encompassing samples of 480,623 workers and 7,530,886 residents. Workers had a significantly lower risk for all-cancer and a significantly higher risk for mesothelioma. Residents had significantly higher risks for all-cancer, thyroid cancer, and leukemia. Notably, children under 5 years old showed the highest risk for all-cancer. Our meta-regression showed a significantly positive dose-response relationship between cumulative dose of radiation exposure and risk for circulatory disease among workers. Our findings demonstrated higher risks for mesothelioma for workers and all-cancer, thyroid cancer, and leukemia for residents exposed to low-dose radiation from nuclear power plants. Some included studies did not adjust for cancer risk confounders, which could overestimate the association between radiation exposure and cancer risk and increase the risk of bias.

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.

Industrial process monitoring is a critical application of multivariate time-series (MTS) anomaly detection, especially crucial for safety-critical systems such as nuclear power plants (NPPs). However, some current data-driven process monitoring approaches may not fully capitalize on the temporal-spatial correlations inherent in operational MTS data. Particularly, asynchronous time-lagged correlations may exist among variables in actual NPPs, which further complicates this challenge. In this work, a reconstruction-based MTS anomaly detection approach based on a temporal-spatial transformer is proposed. It employs a two-stage temporal-spatial attention mechanism combined with a multi-scale strategy to learn the dependencies within normal operational data at various scales, thereby facilitating the extraction of temporal-spatial correlations from asynchronous MTS. Experiments on simulated datasets and real NPP datasets demonstrate that the proposed model possesses stronger feature learning capabilities, as evidenced by its improved performance in signal reconstruction and anomaly detection for asynchronous MTS data. Moreover, the proposed TS-Trans model enables earlier detection of anomalous events, which holds significant importance for enhancing operational safety and reducing potential losses in NPPs.

Based on the monitoring data of 137Cs and 90Sr in Tian Bay in 2005-2023, the impacts of the operation of Tianwan Nuclear Power Plant on the marine ecosystem were assessed. The 137Cs and 90Sr activity concentrations in the seawater and sediment varied within the background ranges. The radiation dose rates derived from 137Cs and 90Sr for the marine organisms ranged from 2.4 × 10-5 to 2.2 × 10-4 nGy/h, it was far below the most conservative screening dose rate (10 μGy/h). The committed effective dose for humans was 0.070-0.094 μSv, 1/1500th of the world\'s mean annual effective dose (0.12 mSv) from ingesting food containing uranium and thorium series nuclides. Radiation risk assessment showed no radiation risk for the long-term discharge of nuclear wastes in the future. Overall, the long-term normal operation of TNPPs has almost no radiation impact on the adjacent marine ecosystem.

The Chinese government promotes nuclear energy development in the context of mitigating climate change. However, the large-scale development is still facing challenges related to the knowledge gap among the general public and the potential \"not-in-my-back-yard\" objection. Based on a representative national survey, we analyze Chinese people\'s knowledge and perceptions of nuclear energy and estimate their willingness-to-accept the potential risks of new nuclear programs in neighborhoods via the Contingent Valuation Method. Generally, more than half of people do not know anything about nuclear energy. The main factors influencing public knowledge are identified, such as the residential distance to existing nuclear power stations and the frequency of internet use. Moreover, approximately 12% of individuals with some knowledge seem to be willing to accept new nuclear power plants in their neighborhoods with no compensation needed. Specifically, the perceptions of nuclear risks and pollution from fossil fuels are significant factors influencing people\'s acceptance of nuclear energy. Although public knowledge does not directly influence acceptance, more knowledge seems to reduce risk perception and increase benefit perception. The residential distance to exiting nuclear stations has limited effects on people\'s acceptance of newly planned nuclear programs for those living in the same county with some knowledge. In general, a typical Chinese household is willing to accept USD $5.66 every month or USD $67.97 every year to bear the potential risks of the new nuclear program in neighborhoods. Significant practical implications that can be transferable to other new energy technologies and countries or regions are provided.

This study established specialized radiation dose models to evaluate the internal radiation doses derived from 137Cs and 134Cs in fishes in the port of the Fukushima Daiichi Nuclear Power Plant from 2012 to 2023. By August 2018, the activities of 134Cs and 137Cs in fishes decreased at the T1/2 of 176 d and 191 d, respectively. The corresponding mass concentrations were far lower than 1 mg/kg and the chemical toxicity can be negligible. Regarding radiotoxicity, 18,000 Bq/kgfresh weight of 134Cs and 137Cs in grouper Sebastes schlegelii produced 276 μGy/h of radiation dose, which was below the no-effect-dose-rate benchmarks (400 μGy/h). 740,000 Bq/kgfresh weight of 134Cs and 137Cs in greenling Hexagrammos otakii produced 12,600 μGy/h of radiation dose, which was much higher than 400 μGy/h, indicating the possibility of radiation effects. If a person eats these two reported fishes, the resulting committed effective doses for humans are 7.7 μSv and 6.31 mSv, respectively.