Within this paper, radioecological data concerning the "peaceful" underground nuclear explosion Kraton-3, conducted at a remote Arctic location (65.9 degrees N, 112.3 degrees E) within the former USSR in 1978, are reviewed. The data and estimates published in the available literature sources before September 2008 could be grouped as following: (a) characterisation of the current radioactive contamination (gamma-, beta- and alpha-emitters) of environmental compartments in terms of radionuclides composition, activity concentration, area contamination density; (b) determination of current gamma dose rates in air, including mapping using GPS; (c) evaluation of cumulative gamma doses in air (with calculations and thermoluminiscence measurements in ceramic objects); (d) description of the visually distinguishable changes in the terrestrial ecosystem; (e) description and quantitative evaluation of morphological abnormalities in the organs of adult plants as well as in seeds and seedlings of some herbs and shrubs, and in small mammals; (f) application of countermeasures. Knowledge gaps and possible further studies are indicated.
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http://dx.doi.org/10.1016/j.jenvrad.2009.04.002 | DOI Listing |
ACS Earth Space Chem
January 2025
Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
Naturally occurring bedded salt deposits are considered robust for the permanent disposal of heat-generating nuclear waste due to their unique physical and geological properties. The Brine Availability Test in Salt (BATS) is a US-DOE Office of Nuclear Energy funded project that uses heated borehole experiments underground (∼655 meters depth) at the Waste Isolation Pilot Plant (WIPP) in the bedded salt deposits of the Salado Formation to investigate the capacity for safe disposal of high-level, heat generating nuclear waste in salt. Uncertainties associated with brine mobility near heat-generating waste motivates the need to characterize the processes and sources of brine in salt deposits.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
Department of Nuclear Science and Technology, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
The mechanism of radon exhalation from surrounding rock fracture has important guiding significance for radon prevention and control in underground research laboratories. The optimal ratio scheme of similar materials in the granite surrounding rock of Beishan underground laboratory was obtained by orthogonal test. The radon exhalation characteristics of fractured rock samples under dry and saturated conditions were obtained by using 10 standard joint roughness coefficient (JRC) curves.
View Article and Find Full Text PDFPlanta
January 2025
Institute of Botany and Botanical Garden, Faculty of Biology, University of Belgrade, Belgrade, Serbia.
Two different strategies for the distribution of macro- and trace elements can be observed in the terrestrial orchid Gymnadenia conopsea. Most trace elements are not translocated to the above-ground parts, whereas for macro-elements the trend was reversed, with the highest accumulation in the distal parts of the plants. Edaphic stress is one of the main factors affecting plant fitness, but it is still poorly understood, even in rare plants such as orchids.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Materials Science, Case Western Reserve University, Cleveland, 44106, USA.
Understanding subsurface temperature variations is crucial for assessing material degradation in underground structures. This study maps subsurface temperatures across the contiguous United States for depths from 50 to 3500 m, comparing linear interpolation, gradient boosting (LightGBM), neural networks, and a novel hybrid approach combining linear interpolation with LightGBM. Results reveal heterogeneous temperature patterns both horizontally and vertically.
View Article and Find Full Text PDFSci Rep
January 2025
Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran.
Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil - nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs.
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