Site specific radionuclide dispersion databases were archived for the emergency response to the hypothetical releases of Cs from the Uljin nuclear power plant in Korea. These databases were obtained with the horizontal resolution of 1.5 km in the local domain centered the power plant site by simulations of the Lagrangian Particle Dispersion Model (LPDM) with the Unified Model (UM)-Local Data Assimilation Prediction System (LDAPS). The Eulerian Dispersion Model-East Asia (EDM-EA) with the UM-Global Data Assimilation Prediction System (UM-GDAPS) meteorological models was used to get dispersion databases in the regional domain. The LPDM model was performed for a year with a 5-day interval yielding 72 synoptic time-scale cases in a year. For each case hourly mean near surface concentrations, hourly mean column integrated concentrations, hourly total depositions for 5 consecutive days were archived by the LPDM model in the local domain and by the EDM-EA model in the regional domain of Asia. Among 72 synoptic cases in a year the worst synoptic case that showed the highest mean surface concentration averaged for 5 days in the LPDM model domain was chosen to illustrate the emergency preparedness to the hypothetical accident at the site. The simulated results by the LPDM model with the Cs emission rate of the Fukushima nuclear power plant accident for the first 5-day period were found to be able to provide prerequisite information for the emergency response to the early phase of the accident whereas those of the EDM-EA model could provide information required for the environmental impact assessment of the accident in the regional domain. The archived site-specific database of 72 synoptic cases in a year could have a great potential to be used as a prognostic information on the emergency preparedness for the early phase of accident.
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http://dx.doi.org/10.1016/j.jenvrad.2017.09.012 | DOI Listing |
Integr Environ Assess Manag
January 2025
Department of Environmental Health Engineering, Faculty of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
This study aimed to evaluate the concentrations of sulfur dioxide (SO2) and nitrogen oxides (NOX) around the Qom (a province in Iran) combined cycle power plant in relation to seasonal variations and fuel type from December 2014 to May 2015. Passive sampling was used in three monitoring sites around the power plant to assess noncarcinogenic health risks associated with exposure to SO2 and NOX. Results showed the higher concentrations of NOX and SO2 in winter than in spring.
View Article and Find Full Text PDFFront Public Health
January 2025
Department of Occupational Health and Radiological Protection, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
Objective: Assess the level of radiation-related knowledge (RRK) and nuclear energy-related knowledge (NERK) among residents near the Sanmen Nuclear Power Plant, the first project adopted the Advanced Passive Pressurized Water Reactor (AP1000) technology.
Methods: In this study, respondents were selected using stratified multi-stage random sampling for residents aged 18 years and above living within 30 kilometers of the Sanmen Nuclear Power Station. Respondents were surveyed face-to-face by investigators who received standardized training.
BMC Health Serv Res
January 2025
Department of Engineering, University Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, Rome, 00128, Italy.
Background: Oxygen therapy is critical and vital treatment for hypoxemia and respiratory distress, however, access to reliable oxygen systems remains limited in SSA. Despite WHO initiatives that distributed over 30,000 OC oxygen concentrators worldwide, SSA faces significant challenges related to their maintenance and use, due to harsh environmental conditions, technical skill shortages and inadequate infrastructure. This review aims to systematically identify and assess the literature on OC design adaptations, maintenance challenges, and knowledge gaps in SSA, providing actionable recommendations to inform innovative and context-sensitive solutions to improve healthcare delivery in the region.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Physical Chemistry, University of Cádiz, 11510, Puerto Real, Spain.
To reduce greenhouse emissions and producing electricity with the smallest environmental impact, developing solar power technology is one of the most important milestones to achieve. Thus, to improve the efficiency of the concentrated solar power (CSP) plants, with lower environmental impact, is of great interest. This work reports the development of nanofluids, a colloidal suspension of nanomaterials in a fluid, based on an environment-friendly base fluid for improving the performance of the heat transfer process in CSP plants.
View Article and Find Full Text PDFSci Rep
January 2025
Amal Jyothi College of Engineering (Autonomous), Kanjirappally, Kerala, India.
In agriculture, promptly and accurately identifying leaf diseases is crucial for sustainable crop production. To address this requirement, this research introduces a hybrid deep learning model that combines the visual geometric group version 19 (VGG19) architecture features with the transformer encoder blocks. This fusion enables the accurate and précised real-time classification of leaf diseases affecting grape, bell pepper, and tomato plants.
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