• Korean Journal of Mineralogy and Petrology
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• v.34 no.3
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• pp.169-176
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• 2021

This study aimed to fundamentally understand structural changes of zeolite under pressure and in the presence of different pressure-transmitting media (PTM) for application studies such as immobilization of heavy metal cation or CO₂ storage using pressure. High-pressure X-ray powder diffraction study was conducted on the zeolite-W (K_6.4Al_6.5Si_25.8O₆₄× 15.3H₂O, K-MER) to understand linear compressibility and the bulk moduli in different PTM conditions. Zeolite-w is a synthetic material having the same framework as natural zeolite merlinoite ((K, Ca_0.5, Ba_0.5, Na)₁₀ Al₁₀Si₂₂O₆₄× 22H₂O). The space group of the sample was identified as I4/mmm belonging to the tetragonal crystal system. Water, carbon dioxide, and silicone-oil were used as pressure-transmitting media. The mixture of sample and each PTM was mounted in a diamond anvil cell (DAC) and then pressurized up to 3 GPa with an increment of ca. 0.5 GPa. Pressure-induced changes of powder diffraction patterns were measured using a synchrotron X-ray light source. Lattice constants, and bulk moduli were calculated using the Le-Bail method and the Birch-Murnaghan equation. In all PTM conditions, linear compressibility of c-axis (𝛽^c) was 0.006(1) GPa^-1 or 0.007(1) GPa^-1. On the other hand, the linear compressibility of a(b)-axis (𝛽^a) was 0.013(1) GPa^-1 in silicone-oil run, which is twice more compressible than the a(b)-axis in water and carbon dioxide runs, 𝛽^a = 0.006(1) GPa^-1. The bulk moduli were measured as 50(3) GPa, 52(3) GPa, and 29(2) GPa in water, carbon dioxide, and silicone-oil run, respectively. The orthorhombicities of ac-plane in the water, and carbon dioxide runs were comparatively constant, near 0.350~0.353, whereas the value decreased abruptly in the silicone-oil run following formula, y = -0.005(1)x + 0.351(1) by non-penetrating pressure fluid condition.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.