Assessment of radiological consequence of a hypothetical accident at the Ghana Research Reactor-1 facility based on terrorist attack

The International Atomic Energy Agency defines a nuclear and radiation accident as an occurrence that leads to the release of radiation causing significant consequences to people, the environment, or the facility. During such an event involving a nuclear reactor, the reactor core is a critical component which when damaged, will lead to the release of significant amounts of radionuclides. Assessment of the radiation effect that emanates from reactor accidents is very paramount when it comes to the safety of people and the environment; whether or not the released radiation causes an exposure rate above the recommended threshold nuclear reactor safety. During safety analysis in the nuclear industry, radiological accident analyses are usually carried out based on hypothetical scenarios. Such assessments mostly define the effect associated with the accident and when and how to apply the appropriate safety measures. In this study, a typical radiological assessment was carried out on the Ghana Research Reactor-1. The study considered the available reactor core inventory, released radionuclides, radiation doses and detailed process of achieving all the aforementioned parameters. Oak Ridge isotope generation-2 was used for core inventory calculations and Hotspot 3.01 was also used to model radionuclides dispersion trajectory and calculate the released doses. Some of the radionuclides that were considered include I-131, Sr-90, Cs-137, and Xe-137. Total effective doses equivalent to released radionuclides, the ground deposition activity and the respiratory time-integrated air concentration were estimated. The maximum total effective doses equivalent value of 5.6 × 10−9 Sv was estimated to occur at 0.1 km from the point of release. The maximum ground deposition activity was estimated to be 2.5 × 10−3 kBq/m3 at a distance of 0.1 km from the release point. All the estimated values were found to be far below the annual regulatory limits of 1 mSv for the general public as stated in IAEA BSS GSR part 3.

Machine Learning-Based Classification and Regression Approach for Sustainable Disaster Management: The Case Study of APR1400 in Korea

During nuclear accidents, decision-makers need to handle considerable data to take appropriate protective actions to protect people and the environment from radioactive material release. In such scenarios, machine learning can be an essential tool in facilitating the protection action decisions that will be made by decision-makers. By feeding machines software with big data to analyze and identify nuclear accident behavior, types, and the concentrations of released radioactive materials can be predicted, thus helping in early warning and protecting people and the environment. In this study, based on the ground deposition concentration of radioactive materials at different distances offsite in an emergency planning zone (EPZ), we proposed classification and regression models for three severe accidents. The objective of the classification model is to recognize the transient situation type for taking appropriate actions, while the objective of the regression model is to estimate the concentrations of the released radioactive materials. We used the Personal Computer Transient Analyser (PCTRAN) Advanced Power Reactor (APR) 1400 to simulate three severe accident scenarios and to generate a source term released to the environment. Additionally, the Radiological Consequence Analysis Program (RCAP) was used to assess the off-site consequences of nuclear power plant accidents and to estimate the ground deposition concentrations of radionuclides. Moreover, ground deposition concentrations at different distances were used as input data for the classification and regression tree (CART) models to obtain an accident pattern and to establish a prediction model. Results showed that the ground deposition concentration at a near distance from a nuclear power plant is a more informative parameter in predicting the concentration of radioactive material release, while the ground deposition concentration at a far distance is a very informative parameter in identifying accident types. In the regression model, the R-square of the training and test data was 0.995 and 0.994, respectively, showing a mean strong linear relationship between the predicted and actual concentration of radioactive material release. The mean absolute percentage error was found to be 26.9% and 28.1% for the training and test data, respectively. In the classification model, the model predicted a scenario (1) of 99.8% and 98.9%, scenario (2) of 98.4% and 91.6%, and scenario (3) of 98.6% and 94.7% for the training and test data, respectively.

Comparison of Orchard Target-Oriented Spraying Systems Using Photoelectric or Ultrasonic Sensors

Orchard pesticide off-target deposition and drift cause substantial soil and water pollution, and other environmental pollution. Orchard target-oriented spraying technologies have been used to reduce the deposition and drift caused by off-target spraying and control environmental pollution to within an acceptable range. Two target-oriented spraying systems based on photoelectric sensors or ultrasonic sensors were developed. Three spraying treatments of young cherry trees and adult apple trees were conducted using a commercial sprayer with a photoelectric-based target-oriented spraying system, an ultrasonic-based target-oriented spraying system or no target-oriented spraying system. A rhodamine tracer was used instead of pesticide. Filter papers were fixed in the trees and on the ground. The tracer on the filter papers was washed off to calculate the deposition distribution in the trees and on the ground. The deposition data were used to evaluate the systems and pesticide off-target deposition achieved with orchard target-oriented sprayers. The results showed that the two target-oriented spraying systems greatly reduced the ground deposition compared to that caused by off-target spraying. Compared with that from off-target spraying, the ground deposition from photoelectric-based (trunk-based) and ultrasonic-based (canopy-based) target-oriented spraying decreased by 50.63% and 38.74%, respectively, for the young fruit trees and by 21.66% and 29.87%, respectively, for the adult fruit trees. The trunk-based target-oriented detection method can be considered more suitable for young trees, whereas the canopy-based target-oriented detection method can be considered more suitable for adult trees. The maximum ground deposition occurred 1.5 m from the tree trunk at the back of the tree canopy and was caused by the high airflow at the air outlet of the sprayer. A suitable air speed and air volume at the air outlet of the sprayer can reduce pesticide deposition on the ground.

RECONSTRUCTION OF THE RADIOACTIVE CONTAMINATION OF THE TERRITORY OF UKRAINE BY IODINE-131 DURING INITIAL PERIOD OF THE CHORNOBYL ACCIDENT USING THE RESULTS FROM NUMERICAL MODEL WRF

Objective. To reconstruct the 131I activity concentrations in air and 131I ground deposition densities from 26 April to 7 May 1986 from the radioactivity release after the Chornobyl accident in the settlements of Ukraine using the mesoscale radionuclides atmospheric transport model LEDI and meteorological information from the numerical weather forecast model WRF and to compare the obtained results with those calculated previously as well as with available measurements of 131I activity in soil. Object of research: the near-ground layer of the atmosphere and the surface of the territory of Ukraine radioactively contaminated as a result of the Chornobyl accident. Materials and methods of research. The dispersion of 131I in the atmosphere and deposition on the ground surface in Ukraine were calculated using the Lagrangian-Eulerian diffusion model LEDI. The detailed fields of meteorological parameters calculated using the mesoscale weather forecast model WRF, which was adapted for the territory of Ukraine, were used as input data for the LEDI model. Results. The 131I daily-average activity concentrations in the surface air and 131I daily ground deposition densities from 26 April to 7 May 1986 were calculated using the up-to-date mesoscale model of numerical weather forecast WRF for 30,352 settlements in entire Ukraine, including 1,263 settlements in Kyiv, 1,717 – in Zhytomyr and 1,570 – in Chernihiv Oblasts. Conclusions. The method of mathematical modeling of the atmospheric transport of the radionuclides is combination with the up-to-date mesoscale model of numerical weather forecast WRF is a useful tool for reconstruction of radioactive contamination of the air and the ground surface after the Chornobyl accident. Calculated in this study 131I activity concentrations in air and 131I ground deposition densities were used to reconstruct the thyroid doses due to 131I intake to the population of Ukraine. Key words: Chornobyl accident, Iodine-131, atmospheric transport model.

Radiological consequences assessment of a hypothetical accident during transportation of spent nuclear fuel

During the transportation of spent nuclear fuels, the potential release of the radioactive materials into the atmosphere in the case of an accident becomes a serious threat to public health and the environment. In China, a commercial reprocessing plant is planned to be commissioned around 2025 based on the China nuclear roadmap. After being cooled on site the spent nuclear fuels are transported to the reprocessing plant by train or truck. This requires the assessment of radiological consequences of such accidents during transportation, therefore dose calculations under hypothetical accident conditions have been presented in this paper. The total effective dose equivalent and ground deposition are calculated using the HotSpot health physics computer code with site-specific meteorological conditions. The results indicate that the total effective dose equivalent and ground deposition are both decreased with the increase of the downwind distance. The maximum of the total effective dose equivalent is about 1.4?101 Sv, which is larger than the regulation limit for the public. The TEDE counter plot shows that the inner regions marked with dose contours of 1.0?10?3 Sv are higher than the regulation limits for the public, however this needs no intervention but any unnecessary trip to this area should be avoided.

Measurement of Pesticide Drift from Unmanned Aerial Vehicle Application to a Vineyard

Unmanned aerial vehicles (UAVs) are now being used to perform commercial pesticide applications in California, but little information is available regarding the amount of pesticide drift resulting from these applications. The physical dimensions and operating speed of UAVs differ substantially from those of manned aircraft and fall outside the validated range of spray dispersion models. This study measured spray drift from a 0.84 ha aerial pesticide application of imidacloprid performed with a Yamaha R-Max II UAV over a Napa Valley vineyard. Downwind deposition samples, in-swath deposition samples, and downwind air samples were collected up to 48 m downwind of the application field. In-swath deposition samples measured approximately 57% of the target rate, while downwind drift deposition decreased from approximately 0.4% at 7.5 m downwind to 0.03% at 48 m downwind. All air samples were below the method detection limit. A drift deposition curve fitted to measured ground deposition using a log-log second-degree polynomial function yielded an R2 value of 0.985. An estimated 0.28% to 0.54% of applied material was lost as drift out to 50 m downwind of the field edge based on ground deposition measurements, 82% of which deposited within the first 7.5 m downwind. Uncertainty in mass accountancy and deposition measurements is discussed, with sources of error including obstructions in the downwind measurement area, low collection efficiency of the sampling media, a high coefficient of variation of spray deposition in the treatment field, and possible photodegradation of the tracer material. Keywords: Aerial application, AGDISP, Pesticide deposition, Pesticide drift, Remotely piloted aircraft, UAV, Unmanned aerial vehicle, Vineyard.