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Source web page: https://www.globalgeochemicalbaselines.eu/content/185/ebook-r-scripts-for-generation-of-5,-8-and-16-random-sampling-points-/ SYNOPSIS The Global Geochemical Reference Network consists in total of 19,833 grid cells of 160x160 km that cover the whole globe. The land surface of the Earth is covered by 7356 grid cells (Global Terrestrial Network, GTN), and the marine surface by 12,477 grid cells (Global Marine Network, GMN). Planning the sampling campaigns in both realms requires the generation of random sampling points within each of the 160x160 km grid cell. For this purpose, three R-scripts were written for the generation of 5-, 8- and 16-random sampling points within each 160x160 km grid cell. The three R-scripts can be used to generate random sampling points within any other size rectangular grid cell. This publication provides the instructions for using the three R-scripts, the format of the input and output files, and the availability of the executable files, as well as the output files, from the website of the IUGS Commission on Global Geochemical Baselines. The URL of the pdf file is: https://www.globalgeochemicalbaselines.eu/datafiles/file/He_Geng_2022_IUGS-CGGB_R-scripts_for_generation_of_5-8-16_random-sampling-points.pdf. Supplementary material: The following files can be downloaded from different web pages of the IUGS Commission on Global Geochemical Baselines:- (a) Executable R-scripts: randomSampling5PointsOnGrids.R (R script for the generation of 5 random points). randomSampling8PointsOnGrids.R (R script for the generation of 8 random points). randomSampling16PointsOnGrids.R (R script for the generation of 16 random points). The source code of the 5, 8 and 16 random sampling points are in Appendices 1, 2 and 3, respectively. The three executable R-scripts for the generation of 5-, 8- and 16-random points in predefined rectangles over the terrestrial and marine surfaces are in a zip file: ‘IUGS-CGGB_R-scripts_Generation_5-8-16_random_points.zip’, which can be downloaded from the ‘Publications/Software Programs’ web page. (b) Global Reference Network grid cells of 160x160 km files: IUGS-CGGB_19833_GRN_160x160km_grid_cells.xlsx (Microsoft™ Excel workbook file with the 19,833 grid cells of the Global Reference Network each of 160x160 km). IUGS-CGGB_GRN_19833_160x160km_grid_cells.zip (same as above but in a Google Earth kml file format). When unzipped the file name is: IUGS-CGGB_19833_GRN_160x160km_grid_cells.kml. The above two files can be downloaded from the ‘Sampling/GTN 160x160 km’ web page. (c) Global Terrestrial Network grid cells of 160x160 km files: IUGS-CGGB_7356_GTN_160x160km_grid_cells.xlsx (Microsoft™ Excel workbook file with the 7356 grid cells of the Global Terrestrial Network each of 160x160 km). IUGS-CGGB_7356_GTN_grid_cells_160x160km.zip (same as above but in a Google Earth kml file format). When unzipped the file name is: IUGS-CGGB_7356_GTN_grid_cells_160x160km.kml. The above two files can be downloaded from the ‘Sampling/GTN 160x160 km’ web page. (d) Randomly selected sampling points in each Global Terrestrial Network grid cell of 160x160 km files: IUGS-CGGB_5-random-points_per_GTN_160x160km-grid-cell.xlsx (Microsoft™ Excel workbook file with 5 random points in each of the 7356 grid cells of 160x160 km totalling 36,780 random sampling points). IUGS-CGGB_5-random-points_per_GTN_160x160km-grid-cell.zip (same as above but in a Google Earth kml file format). When unzipped the file name is: IUGS-CGGB_5-random-points_per_GTN_160x160km-grid-cell.kml. IUGS-CGGB_8-random-points_per_GTN_160x160km-grid-cell.xlsx (Microsoft™ Excel workbook file with 8 random points in each of the 7356 grid cells of 160x160 km totalling 58,848 random sampling points). IUGS-CGGB_8-random-points_per_GTN_160x160km-grid-cell.zip (same as above but in a Google Earth kml file format). When unzipped the file name is: IUGS-CGGB_8-random-points_per_GTN_160x160km-grid-cell.kml. IUGS-CGGB_16-random-points_per_GTN_160x160km-grid-cell.xlsx (Microsoft™ Excel workbook file with 16 random points in each of the 7356 grid cells of 160x160 km totalling 117,696 random sampling points). It is noted that this sampling density is not recommended for the Global Geochemical Reference Network. IUGS-CGGB_16-random-points_per_GTN_160x160km-grid-cell.zip (same as above but in a Google Earth kml file format). When unzipped the file name is: IUGS-CGGB_16-random-points_per_GTN_160x160km-grid-cell.kml. The above six files can be downloaded from the ‘Sampling/Sampling Design’ web page. (e) Global Marine Network grid cells of 160x160 km: IUGS-CGGB_12477_GMN_160X160km_grid_cells.xlsx (Microsoft™ Excel workbook file with the 12,477 grid cells of the Global Marine Network each of 160x160 km). IUGS-CGGB_12477_GMN_160x160km_grid_cells.zip (same as above but in a Google Earth kml file format. When unzipped the file name is: IUGS-CGGB_12477_GMN_160x160km_grid_cells.kml). The above two files can be downloaded from the ‘Sampling/GMN 160 x 160 km’ web page. The following files are necessary for the planning of the Global Marine Network random sampling campaign: IUGS-CGGB_19833_GRN_160x160km_grid_cells.xlsx (File is the same as in (b)). IUGS-CGGB_19833_GRN_160x160km_grid_cells.zip (File is the same as in (b)). IUGS-CGGB_7356_GTN_160x160km_grid_cells.xlsx (File is the same as in (c)). IUGS-CGGB_7356_GTN_grid_cells_160x160km.zip (File is the same as in (c)). IUGS-CGGB_5-random-points_per_GTN_160x160km-grid-cell.xlsx (File is the same as in (d)). IUGS-CGGB_5-random-points_per_GTN_160x160km-grid-cell.zip (File is the same as in (d)). IUGS-CGGB_8-random-points_per_GTN_160x160km-grid-cell.xlsx (File is the same as in (d)). IUGS-CGGB_8-random-points_per_GTN_160x160km-grid-cell.zip (File is the same as in (d))., Citation: He, J. & Geng, X., 2022. R-scripts for Generation of 5, 8 and 16 Random Sampling Points Within Predefined Rectangles. IUGS Commission on Global Geochemical Baselines, Athens, Hellenic Republic, Special Publication, 3, 14 pp.; https://doi.org/10.5281/zenodo.7307202.

The oil terminal is a facility for oil transportation and storage. Flammable gas detectors (FGDs) are able to reliably detect the release of hazardous liquid chemical materials in the tank area, but it cannot accurately obtain the key detecting parameters at the alarm time. This work analyzes the three stages of storage tank leakages under a static storage state. Firstly, the key elements affecting the leakage and detection are analyzed after considering the relative–position relationship of the horizontal distance from the jet point on ground level to the leak hole (Xjet), which denoted by the FGD’s coordinate and the wind direction. Secondly, the denser–than–air dispersion is calculated by the SLAB model based on the liquid–pool radius determined by the bisection method (r), the liquid pool area corresponding to the alarm–response time can be ascertained, and then the detecting rapid response model (DRRM) for the leakage of the flammable liquid storage tank is established. Finally, the model was verified by a small–scale tank leakage and some crucial parameters such as Xjet, r and area of liquid pool, mass accumulating during the continuous leakage period (M) and total leaking time that corresponds to the time to alarm (Ttotal) are obtained. These findings provide scientific data for providing early warning and emergency response.

A detailed theoretical investigation of the atomic structure and electronic properties of ZrC(111)/Cu(111) interfaces was carried out by means of first-principles calculations based on density functional theory. The ZrC (111) surfaces were constructed by two types of surface models, denoted as Zr-termination and C-termination, respectively. The non-stoichiometric surface energy was applied to determine the stability of ZrC (111), which proves the C-termination ZrC (111) surface is more reactive than that of Zr-termination in the entire range of the ( μ C − μ C bulk ). The research shows that the C-termination interface has the smaller interfacial separation and the larger ideal work of adhesion. The interface energies of both termination interfaces were evaluated and it is confirmed that C-termination ZrC (111) surface is more preferable to join with Cu (111) surface. Analysis of electronic structure reveals that the dominant interfacial bonding is the Cu Zr covalent bond and Cu C covalent bond for the Zr-termination and C-termination ZrC(111)/Cu(111) interface, respectively.

Molybdenum atom adsorption and migration behavior on clean graphite (0001) surface have been investigated systematically by using first-principles calculations based on the density functional theory. The Mo atom was not adsorpted at the bridge site of graphite (0001) surface because Mo atom moves towards to the hollow site after geometry optimization. Mo atom has been predicted to adsorb preferentially at the hollow site of graphite (0001) surface because of the larger adsorption energy and the smaller work function. It is found from electronic properties that Mo adsorption on graphite (0001) surface belongs to chemisorptions, which is attributed to the electrons of Mo 4d states transfer to C atoms of graphite (0001) surface. The calculated adsorptive rate suggests that the Mo atom adsorption at the hollow site of graphite (0001) surface is more stable with the increasing of temperature. The optimum energy migration paths for Mo atom diffusion from the hollow site to the nearby hollow site is hollow 1 site → bridge site → hollow 2 site by using LST/QST transition state seeking method.

Heavy gas dispersion is significantly impacted by meteorological conditions. In this study, the widely-used heavy gas dispersion model SLAB is revised by using alternative parametrizations for the meteorological parameters, mixing layer height, and wind speed profile. The Nozaki method and the stability modified logarithmic wind profile evolved from the Monin-Obukhov similarity theory are adopted to calculate the mixing layer height and wind speed profile in the improved SLAB-M model, respectively. Dense gas dispersion model evaluation protocols are used to evaluate SLAB-M, and the datasets from the Jack Rabbit II (JR II) 2015 Trials are used to compare the accuracy of the simulations to observed data. The statistical performance measures (SPMs) of SLAB-M are overall a little better than that of SLAB, and SLAB-M effectively simulates heavy gas dispersion under the different meteorological conditions of the JR II 2015 Trials. Thus, it can better satisfy the requirements of consequence assessment and emergency response for the leakage and dispersion of hazardous gasses.