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.
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.
Objectives
This study hypothesized that a higher diet quality score is associated with a lower observance of symptoms of depression and anxiety and a higher QoL.
Methods
This study Evaluated 1295 adults (521 men; 774 women) aged 19–64 years, who participated in the 2014–2015 National Fitness Award Project. Diet quality was measured by the recommended food score (RFS), and mental health and QoL were assessed by the Beck depression inventory (BDI), Beck anxiety inventory (BAI), and the World Health Organization QoL–Brief (WHOQoL–BREF).
Results
After adjusting for covariates, the individuals with depression had a significantly lower RFS value compared to those without depression, and the good QoL group had a higher RFS value than the poor QoL group. These trends occurred in both men and women. Subjects in the highest tertile of RFS showed a lower odds of depression (OR = 0.44, 95% CI = 0.29–0.68, P-trend = 0.0002) and poor QoL (OR = 0.35, 95% CI = 0.26–0.47, P-trend <0.0001) than those in the lowest tertile. There was no association of the RFS with anxiety.
Conclusions
Our data suggest that improved diet quality is associated with lower ing depressive symptoms and a better QoL in Korean adults.
Funding Sources
This study was supported by the Bio & Medical Technology Development Program funded by the Ministry of Science & ICT (2012M3A9C4048761) and Future Planning through the National Research Foundation (NRF) and Basic Science Research Program (NRF-2018R1C1B4A01023629) funded by the Ministry of Science, ICT, and Future Planning, Republic of Korea.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.