Source inversion of both long- and short-lived radionuclide releases from the Fukushima Daiichi nuclear accident using on-site gamma dose rates

Li, Xinpeng; Sun, Shijie; Hu, Xiaofeng; Huang, Hong; Li, Hong; Morino, Yu; Wang, Shuntan; Yang, Xingtuan; Shi, Jiasong; Fang, Sheng

doi:10.1016/j.jhazmat.2019.120770

Cited by 39 publications

(17 citation statements)

References 26 publications

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“…(Please see the details in Supplemental Materials 2). Other more sophisticated methods such as Bayesian inference 52,53 may also work with the SEIR-CV model and may provide additional statistical information in mathematics, but the increased computational cost might not be acceptable for operational usage. The detailed parameters for control measures in different intervention strategies were traced using the surveillance data as shown in Table S5.…”

Section: Parameterization and Solutionmentioning

confidence: 99%

Transmission Dynamics of Coronavirus Disease 2019 (COVID-19) in the World: The Roles of Intervention and Seasonality

Huang¹,

Xu³

et al. 2020

Preprint

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The coronavirus disease 2019 (COVID-19) is spreading rapidly all over the world. The transmission dynamics of the COVID-19 pandemic is still unclear, but developing strategies for mitigating the severity of the pandemic is yet a top priority for global public health. In this study, we developed a novel compartmental model, SEIR-CV(susceptible-exposed-infectious-removed with control variables), which not only considers the key characteristics of asymptomatic infection and the effects of seasonal variations, but also incorporates different control measures for multiple transmission routes, so as to accurately predict and effectively control the spread of COVID-19. Based on SEIR-CV, we predicted the COVID-19 epidemic situation in China out of Hubei province and proposed corresponding control strategies. The results showed that the prediction results are highly consistent with the outbreak surveillance data, which proved that the proposed control strategies have achieved sound consequent in the actual epidemic control. Subsequently, we have conducted a rolling prediction for the United States, Brazil, India, five European countries (the United Kingdom, Italy, Spain, Germany, and France), southern hemisphere, northern hemisphere, and the world out of China. The results indicate that control measures and seasonal variations have a great impact on the progress of the COVID-19 pandemic. Our prediction results show that the COVID-19 pandemic is developing more rapidly due to the impact of the cold season in the southern hemisphere countries such as Brazil. While the development of the pandemic should have gradually weakened in the northern hemisphere countries with the arrival of the warm season, instead of still developing rapidly due to the relative loose control measures such as the United States and India. Furthermore, the prediction results illustrate that if keeping the current control measures in the main COVID-19 epidemic countries, the pandemic will not be contained and the situation may eventually turn to group immunization, which would lead to the extremely severe disaster of about 5 billion infections and 300 million deaths globally. However, if China's super stringent control measures were implemented from 15 July, 15 August or 15 September 2020, the total infections would be contained about 15 million, 32 million or 370 million respectively, which indicates that the stringent and timely control measures is critical, and the best window period is before September for eventually overcoming COVID-19.

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Section: Parameterization and Solutionmentioning

confidence: 99%

Transmission Dynamics of Coronavirus Disease 2019 (COVID-19) in the World: The Roles of Intervention and Seasonality

Huang¹,

Xu³

et al. 2020

Preprint

Self Cite

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“…An effective way is to reconstruct the source by using environmental monitoring data and a dispersion model. Such methods have been applied to wind tunnel experiments [1,2], tracer experiments [3,4], and real accidents such as nuclear accidents [5,6] and radionuclide emissions under normal conditions [7].…”

Section: Introductionmentioning

confidence: 99%

Source Reconstruction of Atmospheric Releases by Bayesian Inference and the Backward Atmospheric Dispersion Model: An Application to ETEX-I Data

Zhao

Liu

Wang

et al. 2021

Science and Technology of Nuclear Installations

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Source term reconstruction methods attempt to calculate the most likely source parameters of an atmospheric release given measurements, including both location and release amount. However, source term reconstruction is vulnerable to uncertainties. In this paper, a method combining Bayesian inference with the backward atmospheric dispersion model is developed for robust source term reconstruction. The backward model is used to quantify the relationship between the source and measurements and to reduce the search range of the Bayesian inference. A Markov chain Monte Carlo method is used to sample from the multidimensional parameter space of the source term. The source location and release rate are estimated simultaneously, and the posterior probability distribution is produced by applying Bayes’ theorem. The proposed method is applied to a set of real concentration data from the ETEX-I experiment. The results demonstrate that the source location is estimated to be −2.86° ± 1.01°E, 48.25° ± 0.33°N, and the release rate is estimated to be 20.16 ± 3.56 kg/h. The true source location is correctly estimated to be within a one standard deviation interval, and the release rate is correctly determined to be within a three standard deviation interval.

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“…Also, Chen et al (2019) demonstrated an air quality forecast system over Canada that incorporates near-real-time measurement of biomass burning emissions to forecast smoke plumes from fire events. There are numerous other global smoke forecast systems (Chen et al, 2011;Larkin et al, 2009;Lee et al, 2019;Li et al, 2019b;Pavlovic et al, 2016;Sofiev et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary satellite observations

et al. 2020

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Abstract. Smoke forecasts have been challenged by high uncertainty in fire emission estimates. We develop an inverse modeling system, the HYSPLIT-based Emissions Inverse Modeling System for wildfires (or HEIMS-fire), that estimates wildfire emissions from the transport and dispersion of smoke plumes as measured by satellite observations. A cost function quantifies the differences between model predictions and satellite measurements, weighted by their uncertainties. The system then minimizes this cost function by adjusting smoke sources until wildfire smoke emission estimates agree well with satellite observations. Based on HYSPLIT and Geostationary Operational Environmental Satellite (GOES) Aerosol/Smoke Product (GASP), the system resolves smoke source strength as a function of time and vertical level. Using a wildfire event that took place in the southeastern United States during November 2016, we tested the system's performance and its sensitivity to varying configurations of modeling options, including vertical allocation of emissions and spatial and temporal coverage of constraining satellite observations. Compared with currently operational BlueSky emission predictions, emission estimates from this inverse modeling system outperform in both reanalysis (21 out of 21 d; −27 % average root-mean-square-error change) and hindcast modes (29 out of 38 d; −6 % average root-mean-square-error change) compared with satellite observed smoke mass loadings.

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Source inversion of both long- and short-lived radionuclide releases from the Fukushima Daiichi nuclear accident using on-site gamma dose rates

Cited by 39 publications

References 26 publications

Transmission Dynamics of Coronavirus Disease 2019 (COVID-19) in the World: The Roles of Intervention and Seasonality

Transmission Dynamics of Coronavirus Disease 2019 (COVID-19) in the World: The Roles of Intervention and Seasonality

Source Reconstruction of Atmospheric Releases by Bayesian Inference and the Backward Atmospheric Dispersion Model: An Application to ETEX-I Data

Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary satellite observations

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