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Radiation-induced acoustics (RIA) shows promise in advancing radiological imaging and radiotherapy dosimetry methods. However, RIA signals often require extensive averaging to achieve reasonable signal-to-noise ratios, which increases patient radiation exposure and limits real-time applications. Therefore, this paper proposes a discrete wavelet transform (DWT) based filtering approach to denoise the RIA signals and avoid extensive averaging. The algorithm was benchmarked against low-pass filters and tested on various types of RIA sources, including low-energy X-rays, high-energy X-rays, and protons. The proposed method significantly reduced the required averages (1000 times less averaging for low-energy X-ray RIA, 32 times less averaging for high-energy X-ray RIA, and 4 times less averaging for proton RIA) and demonstrated robustness in filtering signals from different sources of radiation. The coif5 wavelet in conjunction with the sqtwolog threshold selection algorithm yielded the best results. The proposed DWT filtering method enables high-quality, automated, and robust filtering of RIA signals, with a performance similar to low-pass filtering, aiding in the clinical translation of radiation-based acoustic imaging for radiology and radiation oncology.

This study presents the implementation of an internet of things (IoT)-based device for the accurate and continuous measurement of gamma and ultraviolet (UV) radiation in a rural area of Sincelejo, Colombia. The device, calibrated with an error margin below 5%, allowed for the reliable collection of data during the year 2022. An average effective dose rate of gamma radiation of (0.998±0.037) mSv/year was recorded, a value that approaches the recommended limit. Additionally, the inverse square law of radiation was confirmed, observing a decrease in radiation with an increase in altitude. Concurrently, a constant risk of high to extremely high UV radiation exposure was detected throughout the year. These findings emphasize the need for constant monitoring and the implementation of UV protection measures in the region. The integration of IoT in environmental dosimetry has proven to be an invaluable tool for detailed tracking of radiation levels, significantly contributing to the understanding of radiation in rural areas. The exploration of more advanced sensors and data analysis tools in future research is recommended to further improve the accuracy and utility of these devices. [ABSTRACT FROM AUTHOR]

The measurement of patients' dosages of radiation caused by medical diagnostics continues to be challenging. A Cantor sequence photonic crystal structure using porous silicon doped with a polymer of polyvinyl alcohol, carbol fuchsin and crystal violet (DPV) is proposed. The influence rules of geometrical and optical parameters such as the radiation doses, number of periods, porosity of porous layers, incident angle and thickness of layers are investigated using MATLAB based on the transfer matrix method. The transmittance of the Cantor sequence of a defective photonic crystal sensor under different conditions is investigated to select the optimum conditions. The proposed system recorded the accepted sensitivity of 0.265 nm/Gy, FoM of 6.5 Gy − 1 , Q of 12,701, RS of 6 × 1 0 − 3 and LoD of 8 × 1 0 − 3 for gamma radiation. The suggested detector has simple design, accurate monitoring efficiency and immense potential for gamma radiation sensing. [ABSTRACT FROM AUTHOR]

Data analysis techniques can be powerful tools for rapidly analyzing data and extracting information that can be used in a latent space for categorizing observations between classes of data. Machine learning models that exploit learned data relationships can address a variety of nuclear nonproliferation challenges like the detection and tracking of shielded radiological material transfers. The high resource cost of manually labeling radiation spectra is a hindrance to the rapid analysis of data collected from persistent monitoring and to the adoption of supervised machine learning methods that require large volumes of curated training data. Instead, contrastive self-supervised learning on unlabeled spectra can enhance models that are built on limited labeled radiation datasets. This work demonstrates that contrastive machine learning is an effective technique for leveraging unlabeled data in detecting and characterizing nuclear material transfers demonstrated on radiation measurements collected at an Oak Ridge National Laboratory testbed, where sodium iodide detectors measure gamma radiation emitted by material transfers between the High Flux Isotope Reactor and the Radiochemical Engineering Development Center. Label-invariant data augmentations tailored for gamma radiation detection physics are used on unlabeled spectra to contrastively train an encoder, learning a complex, embedded state space with self-supervision. A linear classifier is then trained on a limited set of labeled data to distinguish transfer spectra between byproducts and tracked nuclear material using representations from the contrastively trained encoder. The optimized hyperparameter model achieves a balanced accuracy score of 80.30%. Any given model—that is, a trained encoder and classifier—shows preferential treatment for specific subclasses of transfer types. Regardless of the classifier complexity, a supervised classifier using contrastively trained representations achieves higher accuracy than using spectra when trained and tested on limited labeled data. [ABSTRACT FROM AUTHOR]

The detection of ionising radiation in materials from the decommissioning of a dicalcium phosphate plant meant that this whole operation had to be overhauled in terms of radiological protection and safety. Thus, an operations plan was drawn up that included: initial training for all workers involved, measurements of dose rates and a set of analyses by gamma and alpha spectrometry. Samples of "dry sludge" and debris were analysed for the identification and quantification of the radionuclides present, samples of workers' urine were analysed for the detection of any internal contamination, samples of washing water and air filters were analysed for the detection and control of any radioactive atmospheric contamination. In addition, individual dosimeters were distributed to workers. The extent of contamination was identified and the areas where higher values were detected were properly marked. From the results obtained (radiation dose rates and activity concentrations), it was possible to establish categories and separate the contaminated materials according to their physical characteristics. After the plant decommissioning, the resulting waste with dose rates higher than the effective dose limits for the public was packed in big bags. These bags were divided in 3 groups according to the external radiation dose rates at contact. This segregation allowed the competent authority to release a significant number of big bags from regulatory control. The remaining big bags are stored in two metal containers subject to regular radiological monitoring. The objective of this work is the presentation of a factual article where the methodology implemented to deal with this situation, which had never occurred in Portugal, is described, having only the Portuguese legislation as a reference.

In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements.

Doctors have to perform surgery under the guidance of X-rays during DSA intervention diagnosis. Long-term surgery may cause excessive radiation exposure to doctors. It is necessary to develop a small real-time radiation detector to measure the radiation exposure of the different tissues during the surgery, identify weak points in radiation shielding, and develop safer shielding plans. Based on chip technology, we developed a miniaturized, low-power, real-time radiation dose measurement module, which can be easily worn on the hand for real-time measurement of radiation dose. Using the MCNP simulation program, the energy response curve of the detector was calculated under different compensation thicknesses, and the optimal thickness of the compensation was obtained. Various parameters of the measurement module, such as repeatability, dose linearity, energy response, and radiation tolerance dose, were tested and verified. The results showed that the weight of the measurement module was less than 15 g, the volume was less than 15 mm×10 mm×5 mm, the upper limit of dose rate could be extended to 1 Sv/h, and the energy response was better than ±30% in the range of 10~250 keV. The radiation tolerance dose was better than 100 Gy. This work solved the problem of miniaturization of real-time radiation detector, provided radiation real-time measurement methods of exposure dose on the hand for doctors in DSA diagnosis and treatment, measuring X-ray. Through analysis of post data, it can analyze the radiation risk during the surgical process, thus providing technical means and data support for optimization of radiation protection.

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In pediatric radiology, balancing diagnostic accuracy with reduced radiation exposure is paramount due to the heightened vulnerability of younger patients to radiation. Technological advancements in computed tomography (CT) reconstruction techniques, especially model-based iterative reconstruction and deep learning image reconstruction, have enabled significant reductions in radiation doses without compromising image quality. Deep learning image reconstruction, powered by deep learning algorithms, has demonstrated superiority over traditional techniques like filtered back projection, providing enhanced image quality, especially in pediatric head and cardiac CT scans. Photon-counting detector CT has emerged as another groundbreaking technology, allowing for high-resolution images while substantially reducing radiation doses, proving highly beneficial for pediatric patients requiring frequent imaging. Furthermore, cloud-based dose tracking software focuses on monitoring radiation exposure, ensuring adherence to safety standards. However, the deployment of these technologies presents challenges, including the need for large datasets, computational demands, and potential data privacy issues. This article provides a comprehensive exploration of these technological advancements, their clinical implications, and the ongoing efforts to enhance pediatric radiology's safety and effectiveness. [ABSTRACT FROM AUTHOR]

Abstract—The results of radiation monitoring of gray forest soils of reference agricultural plots of Vladimir oblast, conducted in 2011 and 2016 are presented. The study established background values of specific activities of the studied radionuclides in the soils of reference sites. According to the density of contamination of the arable horizon of soils by 137Cs and 90Sr, the level of the ecological situation was determined, which made it possible to attribute the soils of all reference areas of the region to uncontaminated territories suitable without restrictions for agricultural use. The power of the exposure dose of γ radiation of soils and the contribution of 40K and 137Cs to the formation of the γ background of soils are established. Correlation analysis determined the influence of the individual physical and chemical properties of soils on the behavior and distribution of radionuclides in the soil and the relationship between the radionuclides themselves. Based on the values of the specific activity of 137Cs and 90Sr in plant products grown on the soils of reference sites in 2011 and 2016, rows of cultivated plants were constructed, with a decreasing ability to accumulate these radionuclides from the soil. All plant products grown for all types of crops fully met the sanitary and hygienic standards required to limit the content of 137Cs and 90Sr in plant feeds (green mass, straw, and grain) and food products (apples). According to the transition coefficients, it was found that the process of transition of 90Sr from soil to plants of all kinds proceeded more intensively than the transition of 137Cs. The calculated coefficients of linear correlation between the values of the specific activity of 137Cs and 90Sr in soils and plants revealed the strength and nature of their dependencies. [ABSTRACT FROM AUTHOR]

In urban ecosystems to the west of the Elektrostal Heavy Machinery Plant, six years after the radiation incident, local sites with a total area of 0.2 km2 with an increased equivalent dose rate of γ radiation of 0.15–0.40 μSv/h and a high specific activity of 137Cs reaching 8328–8740 Bq/kg in the upper soil horizons were identified. Statistically significant direct correlations were noted between the specific activity of 137Cs in the layer of 0–10 cm of soil and the equivalent dose rate of γ radiation at the surface and height of one meter. The types of vertical distribution of 137Cs in soils of polluted urban ecosystems, including those on which earth decontamination works were carried out, have been determined. The calculated 137Cs transfer coefficients increase in the series of trees Salix alba < Sórbus aucupária < Ácer platanoídes < Malus domestica < Alnus incana < Ácernegúndo; and grassy vegetation Týpha angustifólia < Artemísia vulgáris < Tussilágo fárfara < Cirsium arvense < Urtíca dióica. [ABSTRACT FROM AUTHOR]

The paper considers structure and composition of the measurement tasks of radiation monitoring by the example of power units of nuclear power plants as objects on which the entire complex of radiation measurements in ensuring nuclear and radiation safety has been most systematically and fully implemented. The achieved level of the state of products of nuclear instrumentation (NI) has been shown. The main directions and problematic issues of the development of nuclear instrumentation, including improvement of measurement methods and metrological support for measurements of ionizing radiation, are presented. The key directions of mathematical modeling in the development of equipment are presented. [ABSTRACT FROM AUTHOR]

The accident at the Fukushima NPP has revealed a certain drawback of traditional methods of registration of ionizing radiation by means of the automated radiation monitoring system, because in the conditions of the accident at the NPP the monitoring posts of the system were damaged as a result of the tsunami (23 of 24), which did not allow to evaluate the degree of radioactive contamination of the area in the early stages. In such conditions the most promising method of radiation control carried out on the potentially hazardous area is a non-contact method using an unmanned dosimetry complex (UDC), the application of which would reduce the risk of exposure to additional dose loads of personnel carrying out search and reconnaissance work and, additionally, provide management not only with information regarding radioactive contamination of the environment, but also directly provide the results of the search and reconnaissance. However, apart from the equipment used to determine the radiation background (detectors, spectrometers, radiometers, etc.) an important part of the UDC is the organization of the dosimetry complex and the way of information transfer. A possible variant of data transmission channel from the dosimetry complex installed on the UDC to the operator's personal computer is presented using NRF radio modules as an example. [ABSTRACT FROM AUTHOR]

The study aimed to measure indoor concentrations of radon, thoron, and their progeny in populated areas across seven locations in Muzaffarnagar using dosimeters, DTPS, and DRPS. The data collected over one year across four quarters revealed significant variability in concentration between locations and seasons. The highest average concentration of radon was found in Charthawal from November to January (92.3 Bq/m³), while the highest average concentration of thoron was observed in Mansoorpur from August to October (84.9 Bq/m³). The average concentration of radon progeny was highest in Rohana from November to January (24.7 Bq/m³), while the average concentration of thoron progeny was highest in Tirupadi from August to October (2.9 Bq/m³). Significant variations were observed in the concentration of radon, thoron, and their progeny between locations, which can be due to geological differences, building materials, and ventilation rates. The data also revealed seasonal variation, with November to January having the highest average concentrations of radon, thoron, and their progeny. The total effective dose of radon + EERC varied considerably across locations, with Tirupadi having the highest total effective dose of 0.89 mSv/year, and Muzaffarnagar having the lowest total effective dose of 0.62 mSv/year and similarly for thoron + EETC having the highest of 0.42 mSv/ year. the lowest of 0.07 mSv/ year in Rohana. [ABSTRACT FROM AUTHOR]

An important tool for solving problems associated with the migration of radionuclides is computer technology that makes it possible to predict the spread of radioactive contamination in soils, which are an important component of the biosphere. At the same time, most modern software for the prediction of the spread of radioactive contamination is based on idealizations that simplify the understanding of this process, while solving the problem of a comprehensive assessment of the biosphere requires the use of a full-fledged spatial model of radionuclide migration in soils. Such a model was developed by P. K. Shalkevich within SPS (Simulation of Processes in Soil) software. SPS uses the initial values of radionuclide concentrations, hydrological and thermal properties of soils and information about meteorological conditions as input data for forecasting. At the same time, the listed initial data must be previously collected, processed and converted into a form that the software able to work with. Such processes require significant intellectual and time costs. These costs negatively affect the possibility of generating operational forecasts of the radioactive situation, but can be significantly reduced by using systems for automated radiation monitoring of soils and automating input and processing of initial data when predicting the spatial migration of radionuclides in the appropriate software, what is considered in the present article.

Data analysis techniques can be powerful tools for rapidly analyzing data and extracting information that can be used in a latent space for categorizing observations between classes of data. Machine learning models that exploit learned data relationships can address a variety of nuclear nonproliferation challenges like the detection and tracking of shielded radiological material transfers. The high resource cost of manually labeling radiation spectra is a hindrance to the rapid analysis of data collected from persistent monitoring and to the adoption of supervised machine learning methods that require large volumes of curated training data. Instead, contrastive self-supervised learning on unlabeled spectra can enhance models that are built on limited labeled radiation datasets. This work demonstrates that contrastive machine learning is an effective technique for leveraging unlabeled data in detecting and characterizing nuclear material transfers demonstrated on radiation measurements collected at an Oak Ridge National Laboratory testbed, where sodium iodide detectors measure gamma radiation emitted by material transfers between the High Flux Isotope Reactor and the Radiochemical Engineering Development Center. Label-invariant data augmentations tailored for gamma radiation detection physics are used on unlabeled spectra to contrastively train an encoder, learning a complex, embedded state space with self-supervision. A linear classifier is then trained on a limited set of labeled data to distinguish transfer spectra between byproducts and tracked nuclear material using representations from the contrastively trained encoder. The optimized hyperparameter model achieves a balanced accuracy score of 80.30%. Any given model—that is, a trained encoder and classifier—shows preferential treatment for specific subclasses of transfer types. Regardless of the classifier complexity, a supervised classifier using contrastively trained representations achieves higher accuracy than using spectra when trained and tested on limited labeled data.

Above-ground nuclear explosions that interact with the surface of the earth entrain materials from the surrounding environment, influencing the resulting physical and chemical evolution of the fireball, which can affect the final chemical phase and mobility of hazardous radionuclides that are dispersed in the environment as fallout particles. The interaction of iron with a nuclear explosion is of specific interest due to the potential for iron to act as a redox buffer and because of the likelihood of significant masses of metals to be present in urban environments. We investigated fallout from a historic surface interacting nuclear explosion conducted on a steel tower and report the discovery of widespread and diverse iron-rich micro-structures preserved within the samples, including crystalline dendrites and micron-scale iron-rich spheres with liquid immiscibility textures. We assert these micro-structures reflect local redox conditions and cooling rates and can inform interpretation of high temperature events, enabling new insights into fireball condensation physics and chemistry when metals from the local environment (i.e. structural steel) are vaporized or entrained. These observations also significantly expand the availability of silicate immiscibility datasets applicable to rapidly quenched systems such as meteorite impact melt glass.

The purpose of this study is to perform radiation monitoring by acquiring gamma images and real-time optical images for 99mTc vial source using charge couple device (CCD) cameras equipped with the proposed compact gamma camera. The compact gamma camera measures 86 × 65 × 78.5 mm3 and weighs 934 g. It is equipped with a metal 3D printed diverging collimator manufactured in a 45° field of view (FOV) to detect the location of the source. The circuit's system uses system-on-chip (SoC) and field-programmable-gate-array (FPGA) to establish a good connection between hardware and software. In detection modules, the photodetector (multi-pixel photon counters) is tiled at 8 × 8 to expand the activation area and improve sensitivity. The gadolinium aluminium gallium garnet (GAGG) measuring 0.5 × 0.5 × 3.5 mm3 was arranged in 38 × 38 arrays. Intrinsic and extrinsic performance tests such as energy spectrum, uniformity, and system sensitivity for other radioisotopes, and sensitivity evaluation at edges within FOV were conducted. The compact gamma camera can be mounted on unmanned equipment such as drones and robots that require miniaturization and light weight, so a wide range of applications in various fields are possible.

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

With the development of China’s economy, the application of industrial radiation flaw detection technology is becoming more and more widespread, and the radiation monitoring and protection of flaw detection work has become an important topic in the field of radiation safety and protection. This paper uses pyroelectric technology to track X-ray radiation levels in the environment. It also chooses the scale curve method for calibrating the pyroelectric dosimeter and figures out the scale factor at the same time. The neutron and photon dimensions are used to evaluate the personal dose equivalent. The applicable range of pyroelectric dosimeters is calculated and the method is applied for radiation dose monitoring in industrial X-ray flaw detection rooms. Concrete wall shielding is used to determine the design parameters of the flaw detection room and the location of the distribution points to improve the shielding capacity of the flaw detection room and control the radiation intensity. The monitoring value of the operation position of the flaw detection room of Leshan Leboiler Enterprises in 2018 was 258.2, which was 73 higher than that of 2019. The results of the on-site monitoring of the absorbed dose rate of the ambient air in the flaw detection room showed that the average absorbed rate ranged from 0.015 to 0.546 μSv/h, which was lower than the standard of 2.5 μSv/h, and the shielding capacity of the flaw detection room was better, and the intensity of the radiation could be effectively controlled.

• The paper presents the electron fluxes measured by SATRAM on the Proba-V satellite in LEO. • A neural network has been apllied for particle identification and flux estimation at low fluxes. • A new simple, statistical method is provided to estimate fluxes at high fluxes. • The fluxes are compared with the fluxes measured by EPT on the Proba-V satellite. • Good agreement has been achieved.. We present the determination of electron fluxes measured by the Space Application of Timepix Radiation Monitor (SATRAM), a pixelated single-layer particle detector and the comparison with the Energetic Particle Telescope (EPT), a science-class radiation spectrometer. Both are attached to the Proba-V satellite of the European Space Agency. SATRAM hosts a Timepix chip with a 300 μ m thick silicon sensor divided into a 256 × 256 pixel matrix with 55 μ m pixel pitch. Simulations were conducted to determine the geometric factor of the sensor and the effective area that includes the shielding effects from all directions. The simulation was further used to study the influence of secondary particle production, track interruption, and backscattering on the number of detected particles. Particle identification is performed using two different methods. The first method requires that particle tracks are individually identifiable. A neural network was developed for this purpose, achieving an accuracy of 90.2% for particle identification. If individual particle tracks could not be identified, a statistical approach was used utilizing the energy deposition, average cluster energy, and the last known fraction of electrons. A comparison of the two instruments shows good agreement within one order of magnitude for the majority of the data. [ABSTRACT FROM AUTHOR]

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations. [ABSTRACT FROM AUTHOR]

An organic glass scintillator developed by Sandia National Laboratories was characterized in terms of its light output and pulse shape discrimination (PSD) properties and compared to commercial liquid (EJ-309) and plastic (EJ-276) organic scintillators. The electron light output was determined through relative comparison of the 137Cs Compton edge location. The proton light yield was measured using a double time-of-flight technique at the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory. Using a tunable broad-spectrum neutron source and an array of pulse-shape-discriminating observation scintillators, a continuous measurement of the proton light yield was performed for EJ-309 (200 keV–3.2 MeV), EJ-276 (170 keV–4.9 MeV), and the organic glass (50 keV–20 MeV). Finally, the PSD properties of the organic glass, EJ-309, and EJ-276 were evaluated using an AmBe source and compared via a figure-of-merit metric. The organic glass exhibited a higher electron light output than both EJ-309 and EJ-276. Its proton light yield and PSD performance were comparable to EJ-309 and superior to that of EJ-276. With these performance characteristics, the organic glass scintillator is well poised to replace current state-of-the-art PSD-capable scintillators in a range of fast neutron detection applications.

Radiation air dose rates near the Fukushima Daiichi Nuclear Power Plant (FDNPP) have been steadily decreasing over the past eight years since the release of radioactive elements in March 2011. Currently, the radiation monitoring program is expected to transition to long-term monitoring after most of the remediation activities are completed. The main long-term monitoring objectives are to (1) confirm the continuing reduction of contaminant and hazard levels, (2) provide assurance for the public, (3) accumulate the basic datasets for scientific knowledge and future preparation, and (4) detect changes or anomalies in contaminant mobility (if they occur), or any unexpected processes or events. In this work, we have developed a methodology for optimizing the monitoring locations of radiation air dose-rate monitoring. Our approach consists of three steps in order to determine monitoring locations in a systematic manner: (1) prioritizing the critical locations, such as schools or regulatory requirement locations, (2) diversifying locations that cover the key environmental controls that are known to influence contaminant mobility and distributions, and (3) capturing the heterogeneity of radiation air-dose rates across the domain. For the second step, we use a Gaussian mixture model to identify the representative locations among multiple environmental variables, such as elevation and land-cover types. For the third step, we use a Gaussian process model to capture and estimate the heterogeneity of air-dose rates across the domain. Employing an integrated dose-rate map derived from Bayesian geostatistical methods as a reference map, we distribute the monitoring locations in such a way as to capture the heterogeneity of the reference map. Our results have shown that this approach allows us to select monitoring locations in a systematic manner such that the heterogeneity of air dose rates is captured by the minimal number of monitoring locations.

Insights at the microscopic level of the process of radiocesium adsorption and interaction with clay mineral particles have improved substantially over the past several years, triggered by pressing social issues such as management of huge amounts of waste soil accumulated after the Fukushima Dai-ichi nuclear power plant accident. In particular, computer-based molecular modeling supported by advanced hardware and algorithms has proven to be a powerful approach. Its application can now generally encompass the full complexity of clay particle adsorption sites from basal surfaces to interlayers with inserted water molecules, to edges including fresh and weathered frayed ones. On the other hand, its methodological schemes are now varied from traditional force-field molecular dynamics on large-scale realizations composed of many thousands of atoms including water molecules to first-principles methods on smaller models in rather exacting fashion. In this article, we overview new understanding enabled by simulations across methodological variations, focusing on recent insights that connect with experimental observations, namely: 1) the energy scale for cesium adsorption on the basal surface, 2) progress in understanding the structure of clay edges, which is difficult to probe experimentally, 3) cesium adsorption properties at hydrated interlayer sites, 4) the importance of the size relationship between the ionic radius of cesium and the interlayer distance at frayed edge sites, 5) the migration of cesium into deep interlayer sites, and 6) the effects of nuclear decay of radiocesium. Key experimental observations that motivate these simulation advances are also summarized. Furthermore, some directions toward future solutions of waste soil management are discussed based on the obtained microscopic insights.

In this study, we quantify the temporal changes of air dose rates in the regional scale around the Fukushima Dai-ichi Nuclear Power Plant in Japan, and predict the spatial distribution of air dose rates in the future. We first apply the Bayesian geostatistical method developed by Wainwright et al. (2017) to integrate multiscale datasets including ground-based walk and car surveys, and airborne surveys, all of which have different scales, resolutions, spatial coverage, and accuracy. This method is based on geostatistics to represent spatial heterogeneous structures, and also on Bayesian hierarchical models to integrate multiscale, multi-type datasets in a consistent manner. We apply this method to the datasets from three years: 2014 to 2016. The temporal changes among the three integrated maps enables us to characterize the spatiotemporal dynamics of radiation air dose rates. The data-driven ecological decay model is then coupled with the integrated map to predict future dose rates. Results show that the air dose rates are decreasing consistently across the region. While slower in the forested region, the decrease is particularly significant in the town area. The decontamination has contributed to significant reduction of air dose rates. By 2026, the air dose rates will continue to decrease, and the area above 3.8 μSv/h will be almost fully contained within the non-residential forested zone.

Environmental radiation monitoring is required to protect from the effects of radiation in industrial fields such as nuclear power plant (NPP) monitoring, and various gamma camera systems are being developed. The purpose of this study is to optimize parameters of a diverging collimator composed of pure tungsten for compactness and lightness through Monte Carlo simulation. We conducted the performance evaluation based on spatial resolution and signal-to-noise ratio for point source and obtained gamma images and profiles. As a result, optimization was determined at a collimator height of 60.0 mm, a hole size of 1.5 mm, and a septal thickness of 1.0 mm. Also, the full-width-at-half-maximum was 3.5 mm and the signal-to-noise ratio was 53.5. This study proposes a compact 45° diverging collimator structure that can quickly and accurately identify the location of the source for radiation monitoring.

A comprehensive approach for modeling the pulse height spectra of gamma-ray detectors from passing radioactive cloud in a case of accident at NPP has been developed. It involves modeling the transport of radionuclides in the atmosphere using Lagrangian stochastic model, WRF meteorological processor with an ARW core and GFS data to obtain spatial distribution of radionuclides in the air at a given moment of time. Applying representation of the cloud as superposition of elementary sources of gamma radiation the pulse height spectra are calculated based on data on flux density from point isotropic sources and detector response function. The proposed approach allows us to obtain time-dependent spectra for any complex radionuclide composition of the release. The results of modeling the pulse height spectra of the scintillator detector NaI(Tl) Ø63x63 mm for a hypothetical severe accident at a NPP are presented.

The goal of this study was to give comparative evaluation of applicability of different species of mosses and lichens for radiation monitoring of 137Cs contamination of pine forest biogeocenoses in Ukrainian Polissia in different forest-ecological conditions (FEC). Objects of research were: species composition of mosses and lichens on stationary experimental plots (SEP), their occurrence in FEC–В3 and FEC–А1, sufficiency for multiyear monitoring, values of 137Сs specific activity in investigated indicator species, and concentration ratio (CR) in the system «indicator biomass – substratum». Research was carried out in 2020 in Ukrainian Polissia, Zhytomyr Region, on 4 stationary experimental plots: SEP-1 – in forest-ecological conditions FEC–В3, SEP-2, 3, 4 – in FEC–А1. For these forest-ecological conditions occurrence of indicator species of mosses and lichens as well as their sufficiency for sampling at multiyear monitoring was studied by route method on 20 forest sites. On each SEP, samples of moss and lichen species were collected in sets of 3-5 repetitions. The substratum was sampled directly below them: for epigeous species – soil at the depth of 20 cm, for epiphytic ones – pine bark. 137Сs specific activity was measured on spectrum analyser SЕG-001 «АKP-S»-150 with scintillation detector BDЕG-20-R1. Relative error of measurement – < 15 %. A concentration ratio (CR) was chosen as an index of intensity of 137Cs accumulation by biota. The occurrence of the following species-indicators of radioactive contamination of a pine forest were shown to prevail in FEC–В3: Dicranum polysetum, Pleurozium schreberi, Hypogymnia physodes; in FEC–А1: Cladonia mitis, Cladonia rangiferina, Hypogymnia physodes, Dicranum polysetum. It was found that in FEC–В3, the average values of CR in Dicranum polysetum (2.31±0.127) significantly exceeded the corresponding values in Pleurozium schreberi (1.64±0.06). The difference of the average CR values was essential at a confidence level of 95 %. In FEC–В3, epiphytic lichen Hypogymnia physodes was characterized by the highest average CR value (8.45±0.33), i.e. the difference with mosses was statistically reliable. In FEC–A1, epigeous mosses accumulated 137Cs more intensively than epigeous lichens of genus Cladonia, the average CR values were 4.78±0.28 and 2.51±0.15, respectively, the difference was essential. In FEC–A1, the group of epiphytic lichens was characterized by the maximum CR, with average value 6.84±0.36, which was essentially and reliably different from the corresponding values of the group of epigeous lichens and the group of epigeous mosses. It was found that among the indicator species of mosses and lichens, Hypogymnia physodes was characterized by the highest values of radiocontamination in investigated forest ecological conditions.

We fabricated a 15 m long position-sensitive plastic scintillation optical fiber (PSOF) detector consisting of a PSOF, two photomultiplier tubes, four fast amplifiers, and a digitizer. A single PSOF was used as a sensing part to estimate the gamma-ray source position, and 137Cs, an uncollimated solid-disk-type radioactive isotope, was used as a gamma-ray emitter. To improve the sensitivity, accuracy, and measurement time of a PSOF detector compared to those of previous studies, the performance of the amplifier was optimized, and the digital signal processing (DSP) was newly designed in this study. Moreover, we could measure very low dose rates of gamma-rays with high sensitivity and accuracy in a very short time using our proposed PSOF detector. The results of this study indicate that it is possible to accurately and quickly locate the position of a very low dose rate gamma-ray source in a wide range of contaminated areas using the proposed position-sensitive PSOF detector.

Nuclear power is a sustainable energy source, but radiation management is required for its safe use. Radiation-detection technology has been developed for the safe management of radioactive materials in nuclear facilities but its performance may vary depending on the size and complexity of the structure of nuclear facilities. In this study, a nuclear monitoring system using a multi-sensor network was designed to monitor radioactive materials in a large nuclear facility. Additionally, an artificial-intelligence-based localization algorithm was developed to accurately locate radioactive materials. The system parameters were optimized using the Geant4 Application for Tomographic emission (GATE) toolkit, and the localization algorithm was developed based on the performance evaluation of the Artificial Neural Network (ANN) and Decision Tree (D-Tree) models. In this article, we present the feasibility of the proposed monitoring system by converging the radiation detection system and artificial intelligence technology. [ABSTRACT FROM AUTHOR]

Objective To evaluate and analyze the occupational hazards and radiation protection of the construction project of biological irradiator for research of this university′s scientific research sharing platform. Methods From 2019 to 2021,the X-ray biological irradiator was taken as the research object. According to the requirements of national laws and regulations, such as The Technical Code for Radiation Dose Rate Measurement（HJ1157-1993）,The Code for Personal Monitoring of Occupational External Exposure（GBZ128-2019） and The Safety Standard for Protection against Ionizing Radiation and Radiation Sources（GB18871-2002）,the occupational hazard factors and preventive measures were comprehensively evaluated and analyzed by means of on-site investigation, data collection and on-site detection. Results The maximum dose equivalent rate of the detection points around 5 cm of the external surface of the instrument was 0.13-0.44 μSv/h, and the maximum personal dose was 0.21 mSv/a. No abnormality was found in the occupational physical examination of the radiation staff, and the above results did not reach the specified requirements safety limit. Conclusion The self-shielding radiation protection facilities and management measures of the instrument meet the protection requirements. The user should continue to strengthen the radiation protection work in accordance with the environmental protection regulations, and effectively protect the radiation safety and health of the radiation staff and the public. [ABSTRACT FROM AUTHOR]

Joung, S.; Ji, Y.-y., and Choi, Y., 2023. Development of a floating radiation measurement system for underwater readings in the East Sea of Korea. Journal of Coastal Research, 39(2), 384–393. Charlotte (North Carolina), ISSN 0749-0208. Following the Fukushima nuclear power plant accident and subsequent discharge of contaminated water, the need for marine radioactivity monitoring has emerged. Currently, various underwater radiation monitoring systems are being developed and utilized for monitoring radioactive contamination of seawater. In this study, a floating on-site radiation monitoring system that can measure the radiation energy spectrum of a water environment in the field in real time was designed and fabricated with the aim to reduce the effect of background radiation on the system, which is a problem for land-based measurement technology. This study performed computational simulations to derive the ambient dose rate conversion factor and efficiency of the developed floating on-site radioactivity analysis system, named MARK-U3. The system was tested in the laboratory and in the field in both freshwater and saltwater environments. From the experimental results, the MARK-U3 can perform precise radioactivity analysis without the need for a thick shield, and by strengthening mobility, it can operate continuously. [ABSTRACT FROM AUTHOR]

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Radiation monitoring is crucial to ensure safe, stable, and reliable operation of nuclear technology utilization equipment. The radiation levels at workplaces, in water storage wells, and due to individuals' occupational exposure during the operation of 60Co irradiation equipment over the past five years at the Hunan irradiation center have been monitored and analyzed. The results reveal that the radiation levels received by personnel and at various sites are remarkably lower than the national limits. All the source water quality indexes meet the requirements, and the radioactivity does not exceed the standard limits. The grey GM(1,1) prediction model was introduced to perform the prediction analysis based on 5 years of historical radiation monitoring data. The results show negligible change in the radiation level over the next 3 years, and it is considerably below the prescribed limit; hence, no radiation harm to the surrounding environment and human health would be observed.

The main purpose is determine and evaluate uranium concentration in urine of the inhabitants of nearby settlement of uranium mining sites and workers of uranium processing sites. As uranium enterprises working in Stepnogorsk of Akmola Region and its exploitation could be a risk of radiation exposure for the workers as well as for the local population, monitoring of biosamples could allow information about the exposure to uranium for the workers and inhabitants. Workers and inhabitants of male genders were asked to collect 24 h-urine samples. The concentration of uranium in urine was measured in mass spectrometry (ICP-MS). Uranium concentration in urine samples from workers values ranged from 0.05 to 12.5 μgL−1 , for Akmola region inhabitants values ranged from 0.10 to 0.60 μgL−1 . The results of concentrations of uranium in the urine of inhabitants living near SMCC were comparable to values reported in other studies. The concentration of uranium in the urine of the workers had slightly elevated values, comparable to the conditional norm of 0.9 μg/L.