This research aims to provide the characteristics of the world's first active lidar sensor Atmospheric Laser Doppler Instrument (ALADIN) wind data and Geostationary Korea Multi Purpose Satellite 2A (GK2A) Atmospheric Motion Vector (AMV) data by comparing two wind data. As a result of comparing the data from September 2019 to August 1, 2020, The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 177,681 which gives the Root Mean Square Error (RMSE) of 3.73 m/s and the correlation coefficient is 0.98. For a more detailed analysis, Comparison result considering altitude and latitude, the Normalized Root Mean Squared Error (NRMSE) is 0.2-0.3 at most latitude bands. However, the upper and middle layers in the lower latitudes and the lower layer in the southern hemispheric are larger than 0.4 at specific latitudes. These results are the same for the water vapor channel and the visible channel regardless of the season, and the channel-specific and seasonal characteristics do not appear prominently. Furthermore, as a result of analyzing the distribution of clouds in the latitude band with a large difference between the two wind data, Cirrus or cumulus clouds, which can lower the accuracy of height assignment of AMV, are distributed more than at other latitude bands. Accordingly, it is suggested that ALADIN wind data in the southern hemisphere and low latitude band, where the error of the AMV is large, can have a positive effect on the numerical forecast model.
Hong, Sei Sun;Lee, Choon Oh;Lim, Jaesoo;Lee, Jin Young;Ahn, Ung San
Economic and Environmental Geology
/
v.54
no.1
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pp.1-19
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2021
This study reports the Ar-Ar dating results for the volcanic rocks from small volcanoes(oreum) of the Hallasan Nature Reserve. According to the age of 40Ar/39Ar, the volcanic activity of the Hallasan Natural Reserve was started from about 192 ka ago. The basaltic trachyandesite and trachyte located in the Y valley near the Eorimok in the western part of the Hallasan Natural Reserve represent an age of about 191~192 ka, showing the oldest record of volcanic activity in the Hallasan Natural Reserve. In the Hallasan Natural Reserve, the small volcanoes older than 100 ka are Y Valley in Eorimok area (192±5 and 191±5 ka), Dongsu-Ak (184±19 ka), Mansedongsan (153±5 ka), Janggumok-Orum (135±6 ka), Eoseungsaengak (123±9 ka), Samgagbong (105±2 ka). And the small volcanoes younger than 100 ka are Witbangae-Oreum, Seongneol-Oreum, Muljangol, Yeongsil, Bori-Ak, Witsenueun-Oreum, Witsejokeun-Oreum, Heugbuleun-Oreum, Bangae-Oreum, Albangae-Oreum, Witsebuleun-Oreum, Baengnokdam, Nongo-Ak. According to the eruption of trachytes, the Hallasan Natural Reserve can be interpreted as having about 8 volcanic activities. Among them, 4 volcanic activities are related with the formation of trachyte dome, such as Wanggwanneung, Samgakbong, Yeongsil, and Baengnokdam, and 4 volcanic activities are related with flow or dyke of trachyte. The volcanic activity at the Hallasan Natural Reserve was started from northwest area, to in the southern area, and in the eastern area, and finally volcanic activity related to the formation of Baengnokdam.
In order to develop an air-stripping based remediation process to remove the TOC (Total Organic Carbon) in groundwater around the underground LPG storage cavern, the laboratory scale experiments at various conditions (change of air injection volume and temperature, the application of ultrasonic treatment, etc.) for two types of groundwater (initial TOC concentration of 608 mg/L and 153 mg/L, respectively). From results of experiment, as the air injection rate for stripping into groundwater increased from 2 L/min to 11 L/min and as the air-stripping time increased from 1 hour to 24 hour, the TOC removal efficiency of air-stripping increased. However, the TOC concentration of treated groundwater was higher than the discharge tolerance limit (100 mg/L) even after 24 hour stripping at the maximum air injection rate of 11 L/min. The main compounds of the TOC in groundwater were identified as methanol and propane and the long stripping time (more than 24 hour) was needed to separate the methanol from groundwater because of the affinity between water and methanol. At 20℃ and 4 L/min of air injection, the TOC removal efficiency increased to 59.1% after 24 hour air-stripping. When the temperature of groundwater increased to 30℃ and 40℃, the TOC removal efficiency increased up to 80.0% and 82.8%, suggesting that more than 24 hour air-stripping at 40℃ is needed to lower the TOC concentration to below 100 mg/L and the additional TOC removal process as well as the air-stripping is necessary. When the temperature increased to 60℃ and the ultrasonic treatment was conjugated with the air-stripping, the TOC removal efficiency increased to 87.8% within 5 hour stripping and the final TOC concentration (72.4 mg/L) was satisfied with the TOC discharge tolerance limit. The TOC removal efficiency for groundwater having low TOC concentration (153 mg/L) also showed similar removal efficiency of 89.7% (the final TOC concentration: 18.9 mg/L). Results in this study supported that the air-stripping conjugated with the ultrasonic treatment could remove successfully the TOC in groundwater around the underground LPG strorage cavern.
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.
The physical properties of the ocean interior are determined by temperature and salinity. To observe them, we rely on satellite observations for broad regions of oceans. However, the satellite for salinity measurement, Soil Moisture Active Passive (SMAP), has low temporal and spatial resolutions; thus, more is needed to resolve the fast-changing coastal environment. To overcome these limitations, the algorithm to use the Geostationary Ocean Color Imager-II (GOCI-II) of the Geo-Kompsat-2B (GK-2B) was developed as the inputs for a Multi-layer Perceptron Neural Network (MPNN). The result shows that coefficient of determination (R2), root mean square error (RMSE), and relative root mean square error (RRMSE) between GOCI-II based sea surface salinity (SSS) (GOCI-II SSS) and SMAP was 0.94, 0.58 psu, and 1.87%, respectively. Furthermore, the spatial variation of GOCI-II SSS was also very uniform, with over 0.8 of R2 and less than 1 psu of RMSE. In addition, GOCI-II SSS was also compared with SSS of Ieodo Ocean Research Station (I-ORS), suggesting that the result was slightly low, which was further analyzed for the following reasons. We further illustrated the valuable information of high spatial and temporal variation of GOCI-II SSS to analyze SSS variation by the 11th typhoon, Hinnamnor, in 2022. We used the mean and standard deviation (STD) of one day of GOCI-II SSS, revealing the high spatial and temporal changes. Thus, this study will shed light on the research for monitoring the highly changing marine environment.
Park, Hyun-A;Han, Ji-Sun;Kim, Chang-Gyun;Lee, Jin-Young
Journal of Korean Society of Environmental Engineers
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v.28
no.2
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pp.128-136
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2006
This study was conducted for developing the stability parameter in uncontrolled landfill by using a biomolecular investigation on the microbial community growing through leachate plume. Landfill J(which is in Cheonan) and landfill T(which is in Wonju) were chosen for this study among a total of 244 closed uncontrolled landfills. It addressed the genetic diversity of the microbial community in the leachate by 165 rDNA gene cloning using PCR and compared quantitative analysis of denitrifiers and methanotrophs with the conventional water quality parameters. From the BLAST search, genes of 47.6% in landfill J, and 32.5% in landfill T, respectively, showed more than 97% of the similarity where Proteobacteria phylum was most significantly observed. It showed that the numbers of denitrification genes, i.e. nirS gene and cnorB gene in the J site are 7 and 4 times higher than those in T site, which is well reflecting from a difference of site closure showing 7 and 13 years after being closed, respectively. In addition, the quantitative analysis on methane formation gene showed that J1 spot immediately bordering with the sources has the greatest number of methane formation bacteria, and it was decreased rapidly according to distribute toward the outer boundary of landfill. The comparative investigation between the number of genes, i.e. nirS gene, cnorB gene and MCR gene, md the conventional monitoring parameters, i.e. TOC, $NH_3-N,\;NO_3-N,\;NO_2-N,\;Cl^-$, alkalinity, addressed that more than 99% of the correlation was observed except for the $NO_3-N$. It was concluded that biomolecular investigation was well consistent with the conventional monitoring parameters to interpret their influences and stability made by leachate plume formed in downgradient around the uncontrolled sites.
In recent years, there are strong social demands to characterize the spatial distribution of mountains in Korea. This study aims to develop a 'Sanjulgi-Jido(mountain ridge map)' that might be used not only to satisfy these social demands but also to effectively present the spatial distribution of mountains and drainage basins in the Korean Peninsular. The 'Sanjulgi-Jido' developed in this study is a map that presents the continuity of mountains based on the drainage divides that are delineated by a pre-defined drainage basin size and elevation. This study first validated the Bakdudaegan system through the analyses of a digital elevation model. The Bakdudaegan system has long been recognized as the Koreans traditional conceptual framework to characterize the spatial distribution of mountains. The analyses showed that the Bakdudaegan system has several problems to represent the mountain systems in Korea, which includes 1) the lack of the representativeness of drainage basins, 2) inaccuracy to depict the boundary of drainage basins, 3) the lack of representativeness of mountains, and 4) geo-polical issue that confines the spatial extent of mountain systems within the Korean Peninsular. In order to represent the mountains system in a more quantitative manner, we applied several terrain analysis techniques to understand the spatial distribution of mountains and drainage basins. Based on these analyses, we developed an hierarchical system to classify the continuity (If mountains, which are presented as the spatial distribution of drainage divides with a certain elevation. The first-order Sanjulgi is the drainage divides whose drainage basin are bigger than $5,000km^2$ and the point elevation is above 100m. The next order Sanjulgi is delineated as the size of drainage basin is successively divided by two. This kind of design is able to provide a logical framework to present the mountain systems at different details, depending on the purpose and scale of maps. We also provide several empirical functions to calculate various geomorphological indices for each order of Sanjulgi. The 'Sanjulgi Jido' is similar with the Bakdudaegan system, since it characterizes the continuity of mountains based on the spatial distribution of the drainage divide. It, however, has more scientific criteria to define the scale and continuity of mountains. It should be also noted that the 'Sanjulgi Jido' proposed has different logical and methodological background, compared with the mountain range map that explains the genesis of mountain systems in addition to the continuity of mountains.
Journal of Korean Tunnelling and Underground Space Association
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v.19
no.6
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pp.915-936
/
2017
In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.
Cloud detection means determining the presence or absence of clouds in a pixel in a satellite image, and acts as an important factor affecting the utility and accuracy of the satellite image. In this study, among the satellites of various advanced organizations that provide cloud detection data, we intend to perform quantitative and qualitative comparative analysis on the difference between the cloud detection data of GK-2A/AMI, Terra/MODIS, and Suomi-NPP/VIIRS. As a result of quantitative comparison, the Proportion Correct (PC) index values in January were 74.16% for GK-2A & MODIS, 75.39% for GK-2A & VIIRS, and 87.35% for GK-2A & MODIS in April, and GK-2A & VIIRS showed that 87.71% of clouds were detected in April compared to January without much difference by satellite. As for the qualitative comparison results, when compared with RGB images, it was confirmed that the results corresponding to April rather than January detected clouds better than the previous quantitative results. However, if thin clouds or snow cover exist, each satellite were some differences in the cloud detection results.
This study aimed to develop and apply guidelines for calculating greenhouse gas emissions to activate the contribution of the Korea Institute of Geoscience and Mineral Resources (KIGAM) for institutional-level research activities. In addition, we intended to improve awareness by identifying greenhouse gas emissions from KIGAM's basic research and development (R&D) activities in fiscal 2022. Herein, the research plan and budget contents of individual projects were analyzed, whilst the boundaries and scopes of greenhouse gas emissions were determined, with 22 cases being derived as either direct, indirect, or other sources of emissions. Subsequently, research activity emissions were calculated by emission source. The greenhouse gas emissions of KIGAM's 2022 basic project R&D activities were 2,041.506 tCO2eq, of which direct emissions were 793.235 tCO2eq (38.86%), indirect emissions comprised 305.647 tCO2eq (14.97%), whilst other emissions were 942.624 tCO2eq (46.18%). In particular, greenhouse gas emissions per 100 million won in the KIGAM's basic projects for fiscal 2022 (a total of 96.661 billion won) was calculated as 2.11 tCO2eq, whilst greenhouse gas emissions per participating researcher (was 4.800 tCO2eq. Such calculations should be carried out annually rather than once and accumulated for at least 5 years. Accordingly, it will be possible to standardize specific matters that influence emissions according to differences in research field characteristics and methods, thus guiding greenhouse gas emission reduction management in the future and evaluating the contributions of Environmental, Social and Governance (ESG) management to the environmental sector.
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