Fast identification of radionuclides at low activity levels under dynamic conditions is crucial to public safety. The sequential Bayesian approach, leveraging prior information and incorporating both energy and interarrival time distributions, has demonstrated effectiveness in fast identification. However, conventional implementations typically require prior knowledge of radionuclide activity and are rarely applied under dynamic measurement conditions. Even when applied in such scenarios, these methods often rely on prior knowledge of the relative distance between the source and the detector. To overcome these limitations, a theoretically optimized sequential Bayesian approach is proposed, eliminating the need for prior knowledge of source activity and source-detector geometry, and demonstrating strong robustness to count rate fluctuations caused by source movement. Additionally, the method accurately discriminates gamma-rays originating from background radiation, Compton scattering, and radionuclide-specific emissions, significantly improving identification performance. Experiments were conducted using a mobile platform equipped with a LaBr3 (Ce) scintillator detector, simulating realistic scenarios where radionuclide (137Cs, 60Co, and 133Ba) moved laterally across the detector's field of view at varying speeds and distances. The identification time windows varied by condition, with the shortest being 6.7 s. The results show that all radionuclides were correctly identified at speeds up to 150 mm/s, under a maximum source dose rate contribution of 0.10 mu Sv/h. At 150 mm/s, the minimum identification times for 137Cs, 60Co, and 133Ba were 2.04 s, 1.66 s, and 1.78 s, respectively, corresponding to maximum dose rate contributions of 0.10 mu Sv/h, 1.29 mu Sv/h, and 0.89 mu Sv/h. These findings highlight the method's effectiveness for fast, reliable radionuclide identification under dynamic measurement conditions, underscoring its potential application in public-safety radioactive material detection.

Improving the sensitivity of gamma-ray spectrometry is crucial for food safety monitoring. However, naturally abundant 40K in these foods generates significant continuous spectrum background that severely interferes with the detection of other key environmental radionuclides. This study conducted Monte Carlo simulations in Geant4 to systematically quantify the interference mechanism of 40K in gamma-ray energy spectrum measurements. Simulations indicate that the continuous spectrum of bremsstrahlung radiation emitted by 40K beta particles is the primary source of interference, leading to a significant increase in the background level. The optimized sample container structure proposed in this study reduces the 40K background in the target energy region (e.g., the 210Pb region) by approximately 57%, improving the detection sensitivity with a 17% reduction in the relative minimum detectable activity.

Background: Although lead shielding during computed tomography (CT) examinations has served as routine clinical practice, its actual efficacy and execution remain controversial, showing noticeable divergences across national policies and international clinical guidelines. This discrepancy highlights a disconnect between clinical habits and evolving technical evidence. Methods: This narrative review examines the operational, institutional, legal, and cognitive constraints sustaining these divergent practices. Results: Rather than offering empirical data, this work synthesizes existing literature to propose a preliminary conceptual framework integrating these multi-dimensional factors. Conclusions: Unlike prior studies focusing narrowly on dosimetric outcomes, this framework incorporates transitional stages in CT technology and institutional capacities, offering a scalable, context-specific perspective to inform professional discussions and support localized evaluations regarding radiation protection.

Ambient gamma radiation constitutes an important component of environmental background radiation and is crucial for evaluating public exposure. In China, a nationwide survey conducted between 1983 and 1990 established the baseline for ambient gamma air-absorbed dose rates, with a nationwide range of 2.4-340.8 nGy/h. However, long-term spatiotemporal variations remain poorly documented due to limited publicly available monitoring data. Here, we analyzed ten years of national automatic radiation monitoring data to identify spatial and temporal patterns across China, including areas around operational nuclear power plants (NPPs), and to explore their potential links with climatic factors. 
Spatially, ambient dose rates followed a distinct west-to-east declining gradient, with both national and nuclear power plant (NPP)-proximal datasets fluctuating within historical natural background ranges. National dose rates declined gradually from 2016 to 2023 at 0.7 ± 0.1 nGy/h per year, with the most pronounced reductions occurred in the third and fourth quarters, aligned with seasonal precipitation patterns. Conversely, while initial analyses indicated a modest upward trend near NPPs, further spatial decomposition revealed that this pattern was driven entirely by the Taishan station; upon its exclusion, NPP-proximal dose rates remained temporally stable with no significant long-term drift. Overall, monthly mean variations exhibited no uniform seasonal pattern, indicating that individual meteorological factors exerted limited driving influence on the observed multi-year fluctuations.
This study confirms that ambient gamma radiation nationwide remains within established natural background thresholds, providing a robust dataset to support regulatory radiation monitoring, public outreach, and environmental risk communication.

The rapid identification of γ -emitting radionuclides with low activity levels in public areas is crucial for nuclear safety. However, classical methods rely on full-energy peaks in the integral spectrum, requiring sufficient count accumulation for evaluation, thereby limiting response time. The sequential Bayesian approach, which utilizes prior information and considers both photon energies and interarrival times, can significantly enhance the performance of radionuclides identification. This study proposes a theoretical optimization method for the traditional sequential Bayesian approach. Each photon is processed sequentially, and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory. By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval, the type of γ -rays can be identified (background characteristic γ -rays, Compton plateaus γ -rays, or radionuclide-specific characteristic γ -rays). By integrating the information from these multiple characteristic γ -rays, the presence and type of radionuclides were determined based on the final decision function and a set threshold. Based on theoretical research, verification experiments were conducted using a LaBr_3 (Ce) detector in both low-and natural background radiation environments with typical radionuclides (137Cs, 60Co, and 133Ba). The results show that this approach can identify 137Cs in 7.9 s and 8.5 s (source dose rate contribution: approximately 6.5× 10^-3 μGy/h), 60Co in 8.1 s and 9.8 s (approximately 4.8× 10^-2 μGy/h), and 133Ba in 4.05 s and 5.99 s (approximately 3.4× 10^-2 μGy/h) under low and natural background radiation, respectively, with a miss rate below 0.01 % . This demonstrates the effectiveness of the proposed approach for fast radionuclides identification, even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments.

Monitoring volatile iodine-131 (131I) in nuclear medicine is of great importance for occupational protection. Conventional active monitors require continuous power and generate noise, necessitating reliable passive alternatives. Herein, we report the development, geometric optimization, and dynamic calibration of a passive sampler tailored for airborne organic 131I (CH3131I). To maximize High-Purity Germanium (HPGe) detection efficiency while maintaining an optimal Fickian diffusion length, the sampler's geometry was optimized using LabSOCS Monte Carlo simulations. An optimal diameter-to-height ratio of 4 was established for sorbent volumes <150 mL and validated against traceable multi-gamma standards. A 12-16 mesh coconut shell activated carbon impregnated with 5 wt% KI served as the trapping matrix due to its efficient chemisorption via isotopic exchange. Applying these parameters to a 40 g carbon loading yielded a prototype featuring an 8.4 cm internal diameter, a 2.1 cm carbon depth, and a 2.3 cm effective diffusion length. Dynamic calibration in a micro-reactor under clinical conditions (26°C, 60% relative humidity) revealed a stable sampling rate (SR) of 0.030 m3 h-1 within the linear kinetic regime (≤25% EA). The SR demonstrated high thermodynamic stability across typical ward temperatures (10-26°C) with a 6.0% coefficient of variation. With a combined expanded relative uncertainty of 12.16% (k = 2), the sampler achieved a Minimum Detectable Concentration (MDC) of 0.74 Bq m-3. This work opens a new avenue for determining time-weighted average (TWA) airborne radioiodine concentrations, advancing internal dosimetry protocols.

What is already known on this topic?:Healthcare workers in 131I treatment facilities face potential occupational internal exposure through inhalation of volatile radioiodine, in addition to external exposure. What is added by this report?:This study presents the first comprehensive national monitoring data on internal exposure among Chinese nuclear medicine (NM) workers. Approximately one-fifth of personnel working at radioiodine treatment sites showed detectable levels of 131I in their thyroid tissue. What are the implications for public health practice?:These findings provide essential baseline data for enhancing radiation protection protocols in NM facilities and optimizing national internal exposure monitoring.

ABSTRACT:Inhalation of 131 I is the main route for internal doses to nuclear medicine workers. This study aimed to establish a simple analysis method for determining 131 I activity in carbon cartridges, explore the activity concentration of 131 I in nuclear medicine departments, and evaluate the internal dose of workers. A total of 21 nuclear medicine departments in the hospital conducted air sampling using a high-volume air sampler equipped with carbon cartridges and glass fiber filters to collect gaseous 131 I and aerosol 131 I, respectively. Furthermore, a mathematical model was developed to analyze the 131 I activity with inhomogeneous distribution in cartridges. Based on the 131 I activity measured by the HPGe γ spectrometer, the personal annual inhalation effective dose was estimated. The results showed that there is a significant difference in the activity of gaseous 131 I and aerosol 131 I, with the activity ranging from 1.5±0.08 Bq m -1 to 3,944.23±197.21 Bq m -3 and ND (not detectable) to 842.11±42.11 Bq m -3 , respectively. The activity of aerosol 131 I is about 1% to 7% of that of gaseous 131 I. The annual committed effective dose caused by inhalation of 131 I for workers is 3.6 μSv to 8.23 mSv, which is lower than the dose limit of 20 mSv y -1 . In general, the 131 I contamination in the nuclear medicine department cannot be ignored, and the concentration of 131 I should be regularly monitored to prevent and control the internal radiation to which workers may be exposed.

Radionuclide identification using NaI(Tl) gamma-ray spectroscopy is critical in nuclear security, environmental monitoring, and medicine. While cost-effective and efficient, NaI(Tl) detectors are limited by low energy resolution, spectral noise, and environmental variability. This systematic review evaluates how machine learning (ML) advancements address these limitations. Our analysis reveals that deep learning models—particularly convolutional neural networks (CNNs), hybrid architectures, and other advanced Deep Networks—excel in analyzing low-resolution spectra, achieving over 95% accuracy even under complex conditions (e.g., shielding effects, low-count spectra). Hybrid models which integrate CNNs with traditional algorithms demonstrate superior robustness and explainability. Nevertheless, traditional ML methods (e.g., SVMs) remain valuable for limited datasets or real-time applications. Despite these methodological advances, the field continues to face overarching challenges including data scarcity, model generalization, and explainability, necessitating standardized datasets and physics-informed ML frameworks. ML bridges the performance gap between NaI(Tl) and high-resolution detectors, enabling portable, automated solutions. Future research should prioritize hybrid models, dataset standardization, and optimization for field deployment, enhancing nuclear safety and environmental monitoring capabilities.

Mining-induced radionuclide contamination has become an issue of increasing concern, as it disrupts natural background concentrations and poses potential risks to ecosystems and human health. This study integrates soil data from nine types of mining activities across fifteen countries to systematically assess contamination levels and associated radiological hazards. Results indicate that rare earth elements, phosphate, tin, and uranium mining sites exhibit significantly elevated concentrations of 238U, 232Th, 226Ra, and 40K, with radiation risks well above the global baseline.To further evaluate anthropogenic influences, activity ratios of 238U/226Ra and 232Th/226Ra were analyzed.

In this study, to achieve accurate measurement of radioactive noble gas and enhance the precision of efficiency calibration, a relatively low-cost and low-density simulated-gas calibration source (SGCS) was produced from polyurethane foam with a density of ρ = 0.098 g cm-3. Using SGCS with a Marinelli beaker geometry, the efficiency calibration was applied to a BE5030, 50.5% relative efficiency HPGe detector in an energy range of 59.54 keV∼1836.06 keV. Then, taking the 81 keV gamma-ray emitted by 133Xe as an example, due to the density difference between the SGCS and the 133Xe gas sample, it is necessary to correct for self-attenuation effects. Therefore, a semi-empirical function for self-attenuation correction was established by using LabSOCS software and XCOM. Upon validation, the relative deviation of efficiency calibration values between the SGCS and the LabSOCS of 133Xe under the density of 0.001 g cm-3 to 0.01 g cm-3 was about 3%. After using the self-attenuation correction method established in this study, the results verified a good consistency of the efficiency calculated by SGCS and LabSOCS software.

The biological concentration effect of radionuclides in marine fish has exacerbated public anxiety about seafood security in the context of Fukushima nuclear-contaminated water discharged into the ocean. However, the most polluted port near the Fukushima Daiichi Nuclear Power Plant (FDNPP) has seldom been investigated, especially for radioactivity in marine fish. In this study, decadal observations of radiocesium in marine fish and seawater from the most polluted port were simultaneously established after the Fukushima Nuclear Accident. We found a generally decreasing trend of historical 137Cs activity in seawater, with seasonal variations modulated by precipitation. Seasonal variations were elucidated with finer detail and divided into exponential decline in the dry season and steady variation in the wet season. A novel method was proposed to estimate the continuing source term of 137Cs derived from the FDNPP, which was 3.9 PBq in 2011 and 19.3 TBq between 2012 and 2022 on the basis of historical 137Cs. The biological concentration effect of marine fish is quantitatively emphasized according to the higher ratio of over-standards for radiocesium in marine fish relative to that in seawater. Long-term observation and analysis of radiocesium in marine fish and seawater from the most polluted port would provide insights into the scientific evaluation of the effectiveness of the decommissioning of the FDNPP in the past and share lessons on the fate of Fukushima-derived radionuclides in the future.

Abstract In the absolute measurement method of nuclide radioactivity by the internal gas proportional counter, the reasonable correction of the small pulse counting loss is the key to obtaining the measurement results accurately. Considering the decay type and energy of radioactive gas nuclides, the influence of the low-energy beta particles and the wall effect counting loss on the activity measurement results is different also. To this end, two typical radioactive gas nuclides (37Ar and 3H) are used to study the cause of counting loss based on the Monte Carlo simulation. The results show that the counting loss of small pulse in the activity measurement of 37Ar comes mainly from the wall effect generated by x rays. Within the given gas pressure of 60-300 kPa, the simulated wall effect correction factors are 1.063-1.021. The decay energy of β particles generated by 3H is very low, and there is no obvious wall effect. The small pulse counting loss mainly comes from the low-energy beta particles’ contribution with the energy below the counting threshold, which can be corrected by extrapolating the beta energy spectrum at a lower counting threshold (below 1 keV).

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Soil is an important source and medium of radionuclides, and the content of radioactivity in soil is crucial for radiological impact evaluation. In this study, twenty soil samples in the high background natural radiation area of Yangjiang, China were collected and analyzed for 226Ra, 232Th, 40K and 137Cs concentrations in order to evaluate the radiological health risk in the area. Results showed that the average activity concentrations of 226Ra, 232Th and 40K are 66 Bq/kg, 109 Bq/kg and 211 Bq/kg, respectively. The calculated radiological parameters of radium equivalent activity (Raeq), absorbed dose rate (D), annual effective dose equivalent (AEDE), internal and external hazard indices (Hin and Hex) show a large variation at different sampling sites. Additionally, the elemental oxidation composition and 40K/K mass ratio in the soil were analyzed to further augment the background information of the high background radiation area in Yangjiang.

A large amount of artificial radionuclides have been released into the ocean, contributing to serious nuclear pollution in marine environment, and arising public concerns and worry around the world. The Fukushima-derived artificial radionuclides can also be used as tracers to reveal the migration, transformation processes, and fate of artificial radionuclides in the ocean. The most polluted port within less than 1 km from the Fukushima Daiichi Nuclear Power Plant (FDNPP) was focused on in this study. The most polluted port near the FDNPP serves as windows to reflect progresses and effectiveness of decommissioning of the FDNPP, which is inaccessible for public and many other counties around the world. Historical activities of 134,137Cs in seawater, marine sediment, and marine fish were reconstructed from April 2011 to October 2023 on the basis of over 1 000 reports from Ministry of Economy, Trade and Industry of Japan, Nuclear Regulation Authority of Japan, and Tokyo Electric Power Company. The patterns of the three-stage evolution of 134,137Cs in seawater, the four-stage evolution of 134,137Cs in sediments, and the three-stage evolution of 134,137Cs in marine fish were proposed to quantify the activity levels and effective half-lives (EHL) of 134,137Cs at different stages. The evolutions of historical 134,137Cs in seawater, sediment, and marine fish were closely related to multiple countermeasures of decommissioning at the FDNPP, including the relocation of the drainage channels during June 2014 to April 2015, seabed covering of port in April 2015, removal of highly contaminated retained water in December 2015, filling of tunnels and towers in December 2015, and completed construction of sea-side impermeable walls in February 2016. The longest EHL of 134,137Cs in marine sediment indicates the memory effect of marine sediment and its persistent and dominated contribution to 134,137Cs in marine fish. Additionally, a highly consistent activity ratio of 134Cs to 137Cs (about 1.0) was simultaneously calculated in seawater, sediment, and marine fish, indicating the transferring of the Fukushima-derived 134,137Cs in multiple matrices in the marine environment. The temporal variation of concentration factor of 137Cs in marine fish was also constructed to reveal the dynamic processes of the enrichment and uptake of 137Cs in marine fish from seawater. The relatively high value of concentration factor of 137Cs in marine fish was observed during the initial period of nuclear accident followed by a decline in concentration factor of 137Cs to about 100 L/kg. This study would provide scientific evaluations for the effectiveness of the decommissioning of the FDNPP and the consequences of Fukushima contaminated water discharged into the ocean.

131I I has been extensively utilized in nuclear medicine, resulting in its widespread detection in coastal algal samples due to its discharge. Therefore, it is essential to monitor 131I I in the coastal algal samples. gamma-spectrometry is an expeditious method for measuring 131 I, but this method requires the pretreatment of the algal sample. The effect on 131I I in the algal sample during the oven-drying treatment is unclear. In this study, the Laminaria japonica Areschoug and Sargassum vachellianum Greville were collected at two locations and analyzed for 131I I using gamma-spectrometry. Additionally, the content of iodine was measured using an Inductively Coupled Plasma-Mass Spectrometer (ICP-MS) to clarify the effect of 131I I loss during drying treatment at different temperatures. The results demonstrated that the dried Laminaria and Sargassum samples had calculated 131I I activity concentration relative standard deviations (RSDs) of 6.34 % and 16.31 %, respectively, while the fresh samples exhibited RSDs of 11.70% and 15.57%. Additionally, the iodine content RSDs in the dried samples were 9.19% for Laminaria and 10.34 % for Sargassum. Significantly, discrepancies in 131I I activity concentration between the fresh and dried Laminaria and Sargassum were 5.4 % and 10.3 %. These findings indicate that the temperature factor in drying has no effect on 131I I loss in Laminaria and Sargassum in the range of 70 degrees C-110 degrees C.