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Respiratory masks are the primary and most effective means of protecting individuals from airborne hazards such as droplets and particulate matter during public engagements. However, conventional electrostatically charged melt-blown microfiber masks typically require thick and dense membranes to achieve high filtration efficiency, which in turn cause a significant pressure drop and reduce breathability. In this study, we have developed a multielectrospinning system to address this issue by manipulating the pore structure of nanofiber networks, including the use of uniaxially aligned nanofibers created via an electric-field-guided electrospinning apparatus. In contrast to the common randomly collected microfiber membranes, partially aligned dual-nanofiber membranes, which are fabricated via electrospinning of a random 150 nm nanofiber base layer and a uniaxially aligned 450 nm nanofiber spacer layer on a roll-to-roll collector, offer an efficient way to modulate nanofiber membrane pore structures. Notably, the dual-nanofiber configuration with submicron pore structure exhibits increased fiber density and decreased volume density, resulting in an enhanced filtration efficiency of over 97% and a 50% reduction in pressure drop. This leads to the highest quality factor of 0.0781. Moreover, the submicron pore structure within the nanofiber networks introduces an additional sieving filtration mechanism, ensuring superior filtration efficiency under highly humid conditions and even after washing with a 70% ethanol solution. The nanofiber mask provides a sustainable solution for safeguarding the human respiratory system, as it effectively filters and inactivates human coronaviruses while utilizing 130 times fewer polymeric materials than melt-blown filters. This reusability of our filters and their minimum usage of polymeric materials would significantly reduce plastic waste for a sustainable global society.

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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.

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Most respiratory masks are made of fabrics, which only capture the infectious virus carriers into the matrix. However, these contagious viruses stay active for a long duration (∼7 days) within the fabric matrix possibly inducing post-contact transmissions. Moreover, conventional masks are vulnerable to bacterial growth with prolonged exposure to exhaled breaths. Herein, we combined violacein, a naturally-occurring antimicrobial agent, with porous nanofiber membranes to develop a series of functional filters that autonomously sterilizes viruses and bacteria. The violacein-embedded membrane inactivates viruses within 4 h (99.532 % reduction for influenza and 99.999 % for human coronavirus) and bacteria within 2 h (75.5 % reduction). Besides, its nanofiber structure physically filters out the nanoscale (<0.8 µm) and micron-scale (0.8 µm - 3 µm) particulates, providing high filtration efficiencies (99.7 % and 100 % for PM 1.0 and PM 10, respectively) with long-term stability (for 25 days). In addition, violacein provides additional UV-resistant property, which protects the skin from sunlight. The violacein-embedded membrane not only proved the sterile efficacy of microbe extracted pigments for biomedical products but also provided insights to advance the personal protective equipment (PPE) to fight against contagious pathogens.

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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.

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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.

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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.

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