• Economic and Environmental Geology
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• v.54 no.6
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• pp.689-698
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• 2021

Since the mine reclamation scheme was implemented from 2007 in Korea, various remediation programs have been decontaminated the pollution associated with mining and 254 mines were managed to reclamation from 2011 to 2015. However, as the total amount of contaminated mine drainage has been increased due to the discovery of potential hazards and contaminated zone, more efficient and economical treatment technology is required. Therefore, in this study, the adsorption properties of arsenic was evaluated according to the adsorbents which were derived from water treatment sludge(Alum based adsorbent, ABA-500) and granular ferric hydroxide(GFH), already commercialized. The alum sludge and GFH adsorbents consisted of aluminum, silica materials and amorphous iron hydroxide, respectively. The point of zero charge of ABA-500 and GFH were 5.27 and 6.72, respectively. The result of the analysis of BET revealed that the specific surface area of GFH(257 m²·g^-1) was larger than ABA-500(126~136 m²·g^-1) and all the adsorbents were mesoporous materials inferred from N₂ adsorption-desorption isotherm. The adsorption capacity of adsorbents was compared with the batch experiments that were performed at different reaction times, pH, temperature and initial concentrations of arsenic. As a result of kinetic study, it was confirmed that arsenic was adsorbed rapidly in the order of GFH, ABA-500(granule) and ABA-500(3mm). The adsorption kinetics were fitted to the pseudo-second-order kinetic model for all three adsorbents. The amount of adsorbed arsenic was increased with low pH and high temperature regardless of adsorbents. When the adsorbents reacted at different initial concentrations of arsenic in an hour, ABA-500(granule) and GFH could remove the arsenic below the standard of drinking water if the concentration was below 0.2 mg·g^-1 and 1 mg·g^-1, respectively. The results suggested that the ABA-500(granule), a low-cost adsorbent, had the potential to field application at low contaminated mine drainage.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.3
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• pp.313-331
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• 2022

Natural or native abiotic molecular hydrogen (H₂) is a major component in natural gas, however yet its importance in the global energy sector's usage as clean and renewable energy is underestimated. Here we review the occurrence and geological settings of native hydrogen to demonstrate the much widesprease H₂ occurrence in nature by comparison with previous estimations. Three main types of source rocks have been identified: (1) ultramafic rocks; (2) cratons comprising iron (Fe²⁺)-rich rocks; and (3) uranium-rich rocks. The rocks are closely associated with Precambrian crystalline basement and serpentinized ultramafic rocks from ophiolite and peridotite either at mid-ocean ridges or within continental margin(Zgonnik, 2020). Inorganic geological processes producing H₂ in the source rocks include (a) the reduction of water during the oxidation of Fe²⁺ in minerals (e.g., olivine), (b) water splitting due to radioactive decay, (c) degassing of magma at low pressure, and (d) the reaction of water with surface radicals during mechanical breaking (e.g., fault) of silicate rocks. Native hydrogen are found as a free gas (51%), fluid inclusions in various rock types (29%), and dissolved gas in underground water (20%) (Zgonnik, 2020). Although research on H₂ has not yet been carried out in Korea, the potential H₂ reservoirs in the Gyeongsang Basin are highly probable based on geological and geochemical characteristics including occurrence of ultramafic rocks, inter-bedded basaltic layers and iron-copper deposits within thick sedimentary basin and igneous activities at an active continental margin during the Permian-Paleogene. The native hydrogen is expected to be clean and renewable energy source in the near future. Therefore it is clear that the origin and exploration of the native hydrogen, not yet been revealed by an integrated studies of rock-fluid interaction studies, are a field of special interest, regardless of the presence of economic native hydrogen reservoirs in Korea.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.157-165
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• 2023

Ultrasound imaging uses sound waves of frequencies to cause physical actions such as reflection, absorption, refraction, and transmission at the edge between different tissues. Improvement is needed because there is a lot of noise due to the characteristics of the data generated from the ultrasound equipment, and it is difficult to grasp the shape of the tissue to be actually observed because the edge is vague. The edge enhancement method is used as a method to solve the case where the edge surface looks clumped due to a decrease in image quality. In this paper, as a method to strengthen the interface, the quality improvement was confirmed by strengthening the interface, which is the high-frequency part, in each image using an unsharpening mask and high boost. The mask filtering used for each image was evaluated by measuring PSNR and SNR. Abdominal, head, heart, liver, kidney, breast, and fetal images were obtained from Philips epiq5g and affiniti70g and Alpinion E-cube 15 ultrasound equipment. The program used to implement the algorithm was implemented with MATLAB R2022a of MathWorks. The unsharpening and high-boost mask array size was set to 3*3, and the laplacian filter, a spatial filter used to create outline-enhanced images, was applied equally to both masks. ImageJ program was used for quantitative evaluation of image quality. As a result of applying the mask filter to various ultrasound images, the subjective image quality showed that the overall contour lines of the image were clearly visible when unsharpening and high-boost mask were applied to the original image. When comparing the quantitative image quality, the image quality of the image to which the unsharpening mask and the high boost mask were applied was evaluated higher than that of the original image. In the portal vein, head, gallbladder, and kidney images, the SNR, PSNR, RMSE and MAE of the image to which the high-boost mask was applied were measured to be high. Conversely, for images of the heart, breast, and fetus, SNR, PSNR, RMSE and MAE values were measured as images with the unsharpening mask applied. It is thought that using the optimal mask according to the image will help to improve the image quality, and the contour information was provided to improve the image quality.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.407-419
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• 2022

Soil(vadose zone) gas compositions were measured for about 3 days to suggest a method for monitoring and interpreting soil gas data collected around wells from which methane(CH₄) is outflowing. The vadose zone gas samples were collected within 1 m around two test wells(TB2 and TB3) at Pohang and analyzed for CO₂, CH₄, N₂ and O₂ concentrations in situ. CO₂ flux was measured beside TB2. In addition, gas samples from well head in TB2 and atmospheric air samples were collected for comparison. Carbon isotopes of CO₂(δ¹³C_CO2) of samples collected on the last day of the study period were analyzed in the laboratory. The two test wells (TB2 and 3) were 12.7 m apart and only TB3 was cemented to the surface. According to the bio-geochemical process-based interpretation, the relationships between CO₂ and O₂, N₂, and N₂/O₂ of vadose zone gas were plotted between the lines of CH₄ oxidation and CO₂ dissolution. In addition, the CH₄ concentrations of gas samples from the wellhead of the uncemented well (TB2) were 5.2 times higher than the atmospheric CH₄ concentration. High CO₂ concentrations (average 1.148%) of vadose zone gas around TB2 seemed to be attributed to the oxidation of CH₄. On the other hand, the vadose zone CO₂ around the cemented well(TB3) showed a relatively low concentration(0.136%). This difference indicates that the vadose zone gas(including CO₂) around the CH₄ outflowing well were strongly affected by well completion(cementing). This study result can be used to establish strategies for environmental monitoring of soil around natural gas sites, and can be used to monitor leakage around injection and observation wells for CO₂ geological storage. In addition, the method of this study is useful for soil monitoring in natural gas storage and oil-contaminated sites.

• Ecology and Resilient Infrastructure
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• v.9 no.3
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• pp.183-193
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• 2022

The growth inhibitory effect of Microcystis aeruginosa according to the ultrasonic irradiation time was evaluated using a large algae sample volume (10 L) for various ultrasonic irradiation times (0.5, 1, 1.5, 2, 2.5 and 3 hr) at a laboratory scale. Based on the analysis of Chl-a and cell number of M. aerginosa, algae growth inhibition was observed with the decrease in Chl-a and cell number in all experimental groups after the ultrasonic irradiation. For the experimental group (T_B, T_C, T_D) with an ultrasonic irradiation time of less than 2 hours, rapid regrowth of algae was observed after growth inhibition, but the experimental group (T_E, T_F, T_G) with an irradiation time of more than 2 hours successfully inhibited algal growth lasting one or two more days. Based on the comparison of the recovery time to initial cell number the experimental group (T_B, T_C, T_D) took less than 20 days whereas the experimental group (T_E, T_F, T_G) took about 30 days. Correspondingly, the experimental group showed a high first order decay rate (𝜅) in proportion to the ultrasonic irradiation time during the growth inhibition period. Additionally, the specific growth rates (𝜇) during regrowth in the experimental group with irradiation time of more than 2 hours were relatively low compared to those in the experimental group with less than 2 hours. Therefore, ultrasonic irradiation for more than 2 hours is required for long-term (30 days) inhibition of algal growth in stagnant waters. However, the appropriate ultrasonic irradiation time for algae growth inhibition should be determined according to various field conditions such as the volume of stagnant water, water depth, flow rate, algae concentration, etc. Finally, damages to the algal cell surface and cell membrane were clearly observed, and both destruction and disturbance of gas vesicles of M. aeruginosa in the experimental group were discovered, indicating the growth inhibitory effect of Microcystis aeruginosa according to the ultrasonic irradiation time was confirmed.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.

• The Journal of Korean Academy of Prosthodontics
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• v.61 no.3
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• pp.189-197
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• 2023

Purpose. This study aimed to compare and evaluate the shear bond strength between various temporary prostheses resin blocks fabricated by subtractive and additive manufacturing methods bonded to self-curing reline resin. Materials and methods. The experimental groups were divided into 4 groups according to the manufacturing methods of the resin block specimens and each specimen was fabricated by subtractive manufacturing (SM), additive manufacturing stereolithography apparatus manufacturing (AMS), additive manufacturing digital light processing manufacturing (AMD) and conventional self-curing (CON). To bond the resin block specimens and self-curing resin, the reline resin was injected and polymerized into the same location of each resin block using a silicone mold. The shear bond strength was measured using a universal testing machine, and the surface of the adhesive interface was examined by scanning electron microscopy. To compare between groups, one-way ANOVA was done followed by Tukey post hoc test (α = 0.05). Results. The shear bond strength showed higher values in the order of CON, SM, AMS, and AMD group. There were significant differences between CON and AMS groups, as well as between CON and AMD groups. but there were no significant differences between CON and SM groups (P > .05). There were significant differences between SM and AMD groups, but there were no significant differences between SM and AMS groups. The AMS group was significantly different from the AMD group (P < .001). The most frequent failure mode was mixed failures in CON and AMS groups, and adhesive failures in SM and AMD groups. Conclusion. The shear bond strength of SM group showed lower but not significant bond strength compared to the CON group. The additive manufacturing method groups (AMS and AMD) showed significantly lower bond strength than the CON group, with the AMD group the lowest. There was also a significant difference between the AMD and SM group.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.426-441
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• 2023

This study investigated the predictive accuracy of a model of landslide displacement in Jecheon-si, where a great number of landslides were triggered by heavy rain on both natural (non-clear-cut) and clear-cut slopes during August 2020. This was accomplished by applying three flow direction methods (single flow direction, SFD; multiple flow direction, MFD; infinite flow direction, IFD) and the degree of root cohesion to an infinite slope stability equation. The application assumed that the soil saturation and any changes in root cohesion occurred following the timber harvest (clear-cutting). In the study area, 830 landslide locations were identified via landslide inventory mapping from satellite images and 25 cm resolution aerial photographs. The results of the landslide modeling comparison showed the accuracy of the models that considered changes in the root cohesion following clear-cutting to be improved by 1.3% to 2.6% when compared with those not considered in the area under the receiver operating characteristics (AUROC) analysis. Furthermore, the accuracy of the models that used the MFD algorithm improved by up to 1.3% when compared with the models that used the other algorithms in the AUROC analysis. These results suggest that the discriminatory application of the root cohesion, which considers changes in the vegetation condition, and the selection of the flow direction method may influence the accuracy of landslide predictive modeling. In the future, the results of this study should be verified by examining the root cohesion and its dynamic changes according to the tree species using the field hydrological monitoring technique.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Korean Journal of Soil Science and Fertilizer
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• v.21 no.4
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• pp.386-408
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• 1988

A study was conducted to develop a model for estimating evapotranspiration and yield of Chinese cabbages from meteorological factors from 1981 to 1986 in Suweon, Korea. Lysimeters with water table maintained at 50cm depth were used to measure the potential evapotranspiration and the maximum evapotranspiration in situ. The actual evapotranspiration and the yield were measured in the field plots irrigated with different soil moisture regimes of -0.2, -0.5, and -1.0 bars, respectively. The soil water content throughout the profile was monitored by a neutron moisture depth gauge and the soil water potentials were measured using gypsum block and tensiometer. The fresh weight of Chinese cabbages at harvest was measured as yield. The data collected in situ were analyzed to obtain parameters related to modeling. The results were summarized as followings: 1. The 5-year mean of potential evapotranspiration (PET) gradually increased from 2.38 mm/day in early April to 3.98 mm/day in mid-June, and thereafter, decreased to 1.06 mm/day in mid-November. The estimated PET by Penman, Radiation or Blanney-Criddle methods were overestimated in comparison with the measured PET, while those by Pan-evaporation method were underestimated. The correlation between the estimated and the measured PET, however, showed high significance except for July and August by Blanney-Criddle method, which implied that the coefficients should be adjusted to the Korean conditions. 2. The meteorological factors which showed hgih correlation with the measured PET were temperature, vapour pressure deficit, sunshine hours, solar radiation and pan-evaporation. Several multiple regression equations using meteorological factors were formulated to estimate PET. The equation with pan-evaporation (Eo) was the simplest but highly accurate. PET = 0.712 + 0.705Eo 3. The crop coefficient of Chinese cabbages (Kc), the ratio of the maximum evapotranspiration (ETm) to PET, ranged from 0.5 to 0.7 at early growth stage and from 0.9 to 1.2 at mid and late growth stages. The regression equation with respect to the growth progress degree (G), ranging from 0.0 at transplanting day to 1.0 at the harvesting day, were: $$Kc=0.598+0.959G-0.501G^2$$ for spring cabbages $$Kc=0.402+1.887G-1.432G^2$$ for autumn cabbages 4. The soil factor (Kf), the ratio of the actual evapotranspiration to the maximum evapotranspiration, showed 1.0 when the available soil water fraction (f) was higher than a threshold value (fp) and decreased linearly with decreasing f below fp. The relationships were: Kf=1.0 for $$f{\geq}fp$$ Kf=a+bf for f$$I{\leq}Esm$$ Es = Esm for I > Esm 6. The model for estimating actual evapotranspiration (ETa) was based on the water balance neglecting capillary rise as: ETa=PET. Kc. Kf+Es 7. The model for estimating relative yield (Y/Ym) was selected among the regression equations with the measured ETa as: Y/Ym=a+bln(ETa) The coefficients and b were 0.07 and 0.73 for spring Chinese cabbages and 0.37 and 0.66 for autumn Chinese cabbages, respectively. 8. The estimated ETa and Y/Ym were compared with the measured values to verify the model established above. The estimated ETa showed disparities within 0.29mm/day for spring Chinese cabbages and 0.19mm/day for autumn Chinese cabbages. The average deviation of the estimated relative yield were 0.14 and 0.09, respectively. 9. The deviations between the estimated values by the model and the actual values obtained from three cropping field experiments after the completion of the model calibration were within reasonable confidence range. Therefore, this model was validated to be used in practical purpose.