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Thermochromic sensors provide an intuitive and real‐time solution for monitoring the local temperature with naked eyes. Conventional thermochromic sensors often utilize either solution‐type or dense film‐type platforms, which are suboptimal morphologies for exposing a large number of dye molecules to the surface, leading to low sensitivity and sluggish responding speeds. Herein, this article introduces rational synthetic routes to fabricate highly sensitive nanofiber (NF) sensor membranes loaded with thermochromic dyes (C3H6N6·CH2O)x‐loaded nanofibers (NFs) sensor membranes by alignment‐controllable electrospinning techniques (x–y perpendicular and rotary). The NF‐based porous sensor membranes exhibit two‐ to fivefold improved thermochromic sensitivity (ΔRGB) compared to those of dense film‐type sensors at 31.6–42.7 °C. This is attributed to the uniform distribution of dyes throughout the porous NF structure (≈95.7%), which exhibits excellent light transmittance that is 10–30‐fold higher than that of film‐type sensors. Based on the available shape‐conforming synthetic strategies, this article further demonstrates wearable thermochromic sensors in the forms of mask‐, patch‐, and bracelet‐type devices, which can accurately monitor body temperature in real time. [ABSTRACT FROM AUTHOR]

Abstract: This paper specifically discusses an atmospheric dispersion modeling and health risk assessment for Cs-137 to assess the potential and actual effects on human health from an inhalation event due to a radiological terrorist attack in the Seoul metropolitan area, Korea. The source term was assumed to be 5TBq of Cs-137, introduced into the central part of Seoul’s metropolitan area by a terrorist attack. Atmospheric dispersion models can be used to support the decision making and risk assessments when terrorist attacks have happened in an urban area. The concepts of Gaussian plume modeling and computational fluid dynamics modeling were used to calculate the Cs-137 concentration in the air. Mortality risk and morbid risk coefficients for the inhalation of contaminated air were used to assess the human health risk. The mortality and morbidity are 1.12E−2 and 1.64E−2, respectively in case of the Gaussian plume, while 6.23E−3 and 9.13E−3 in case of the computational fluid dynamics model. The results of the modeling are dependent on the terror scenarios and dispersion models. Accordingly, the optimization process is needed for final decision making. [Copyright &y& Elsevier]

A study was carried out to determine the source rate from a nuclear power plant using the tracer experimental data conducted at the Yeoung-Kwang nuclear site. The least squares method optimises the agreement between the released source rate and the calculated source rate by minimising the errors between the measured concentrations and the calculated ones using the Gaussian plume model. The least squares estimator generally estimates the source rate to be within a factor of 2. The forecasting ability of the source rate is improved by applying the modified dispersion coefficients that are calculated using the experimental data. Determination of the source rate in an early phase nuclear emergency will be helpful for the decision making when taking appropriate and prompt countermeasures in the case of a radiological emergency. [ABSTRACT FROM AUTHOR]

The influence of predictive contamination to agricultural products due to the wet processes as well as dry processes from radioactive air concentration during a nuclear emergency is comprehensively analyzed. The previous dynamic food chain model DYNACON considering Korean agricultural and environmental conditions, in which the initial input parameter was radionuclide concentrations on the ground, is improved so as to evaluate radioactive contamination to agricultural products from either radioactive air concentrations or radionuclide concentrations on the ground. As for the results, wet deposition is a more dominant mechanism than dry deposition in contamination on the ground. While, the contamination levels of agricultural products are strongly dependent on radionuclide and precipitation when the deposition of radionuclides occurs. It means that the contamination levels of agricultural products are determined from which is the more dominant process between deposition on the ground and interception to agricultural plants. [Copyright &y& Elsevier]

Nanofibers for fine dust filters of four structures (random, aligned, orthogonal, and nanofiber net) were prepared by electrospinning method using polymers such as PAN and PA6. While conventional electret filters experienced deterioration problems in fine dust(PM1.0) capture as its surface charge decayed, the electrospun nanofibers prepared contributed to the removal capacity. The filters from aligned fibers showed high quality factors (qF: filter performance indicator) and filtration efficiency from 22 to 50% depending on particle size than simple electret media at a face velocity of 15.92 cm/s. The fiber structure of nanofiber net (NFN) presented almost absolute collection efficiency, particularly on dust particles smaller than 300 nm. Furthermore, the composite filters which are composed both of a commercial electret mask filters and nanofiber nets effectively enhanced the overall filtration efficiency by 59.46%, resulting in more than 99% for PM1.0. Consequently, electrospun polymer nanofibers offer a promising plausible mask filter material with air permeability. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 48166. [ABSTRACT FROM AUTHOR]

State-of-the-art dose assessment models were applied to estimate doses to the population in urban areas contaminated by the Fukushima Daiichi Nuclear Power Plant accident. Assessment results were compared among five models, and comparisons of model predictions with actual measurements were also made. Assessments were performed using both probabilistic and deterministic approaches. Predicted dose distributions for indoor and outdoor workers from a probabilistic approach were in good agreement with the actual measurements. In addition, when the models were applied to assess the doses to the representative person, based on a concept recommended by the International Commission on Radiological Protection and in the International Atomic Energy Agency Safety Standards, it was evident that doses to the representative person obtained with a deterministic approach were always higher than those obtained with a probabilistic approach using the same model., (© 2022 Society for Radiological Protection. Published on behalf of SRP by IOP Publishing Limited. All rights reserved.)

The IAEA's model testing programmes have included a series of Working Groups concerned with modelling radioactive contamination in urban environments. These have included the Urban Working Group of Validation of Environmental Model Predictions (1988-1994), the Urban Remediation Working Group of Environmental Modelling for Radiation Safety (EMRAS) (2003-2007), the Urban Areas Working Group of EMRAS II (2009-2011), the Urban Environments Working Group of (Modelling and Data for Radiological Impact Assessments) MODARIA I (2013-2015), and most recently, the Urban Exposures Working Group of MODARIA II (2016-2019). The overarching objective of these Working Groups has been to test and improve the capabilities of computer models used to assess radioactive contamination in urban environments, including dispersion and deposition processes, short-term and long-term redistribution of contaminants following deposition events, and the effectiveness of various countermeasures and other protective actions, including remedial actions, in reducing contamination levels, human exposures, and doses to humans. This paper describes the exercises conducted during the MODARIA I and MODARIA II programmes. These exercises have included short-range and mid-range atmospheric dispersion exercises based on data from field tests or tracer studies, hypothetical urban dispersion exercises, and an exercise based on data collected after the Fukushima Daiichi accident. Improvement of model capabilities will lead to improvements in assessing various contamination scenarios (real or hypothetical), and in turn, to improved decision-making and communication with the public following a nuclear or radiological emergency., (© 2022 Society for Radiological Protection. Published on behalf of SRP by IOP Publishing Limited. All rights reserved.)

In this paper, the results obtained by inter-comparing several statistical techniques for estimating gamma dose rates, such as an exponential moving average model, a seasonal exponential smoothing model and an artificial neural networks model, are reported. Seven years of gamma dose rates data measured in Daejeon City, Korea, were divided into two parts to develop the models and validate the effectiveness of the generated predictions by the techniques mentioned above. Artificial neural networks model shows the best forecasting capability among the three statistical models. The reason why the artificial neural networks model provides a superior prediction to the other models would be its ability for a non-linear approximation. To replace the gamma dose rates when missing data for an environmental monitoring system occurs, the moving average model and the seasonal exponential smoothing model can be better because they are faster and easier for applicability than the artificial neural networks model. These kinds of statistical approaches will be helpful for a real-time control of radio emissions or for an environmental quality assessment.