• Geomechanics and Engineering
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• v.37 no.6
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• pp.577-590
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• 2024

With increasing demand for nuclear power generation, nuclear structures are being planned and constructed worldwide. A grave safety concern is that these structures are sensitive to large-magnitude shaking, e.g., during earthquakes. Seismic response analysis, which requires P- and S-wave velocities, is a key element in nuclear structure design. Accordingly, it is important to determine the P- and S-wave velocities in the Gyeongju and Pohang regions of South Korea, which are home to nuclear power plants and have a history of seismic activity. P- and S-wave velocities can be obtained indirectly through a correlation with physical properties (e.g., N values, Young's modulus, and uniaxial compressive strength), and researchers worldwide have proposed regression equations. However, the Gyeongju and Pohang regions of Korea have not been considered in previous studies. Therefore, a database was constructed for these regions. The database includes physical properties such as N values and P- and S-wave velocities of the soil layer, as well as the uniaxial compressive strength, Young's modulus, and P- and S-wave velocities of the bedrock layer. Using the constructed database, the geological characteristics and distribution of physical properties of the study region were analyzed. Furthermore, models for predicting P- and S-wave velocities were developed for soil and bedrock layers in the Gyeongju and Pohang regions. In particular, the model for predicting the S-wave velocity for the soil layers was compared with models from previous studies, and the results indicated its effectiveness in predicting the S-wave velocity for the soil layers in the Gyeongju and Pohang regions using the N values. The proposed models for predicting P- and S-wave velocities will contribute to predicting the damage caused by earthquakes.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.649-655
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• 2021

This study purposes to cross-validate its performance by applying the optimal seismic vulnerability assessment model based on previous studies conducted in Gyeongju to other regions. The test area was Pohang City, the occurrence site for the 2017 Pohang Earthquake, and the dataset was built the same influencing factors and earthquake-damaged buildings as in the previous studies. The validation dataset was built via random sampling, and the prediction accuracy was derived by applying it to a model based on a random forest (RF) of Gyeongju. The accuracy of the model success and prediction in Gyeongju was 100% and 94.9%, respectively, and as a result of confirming the prediction accuracy by applying the Pohang validation dataset, it appeared as 70.4%.

• Journal of Environmental Health Sciences
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• v.31 no.5 s.86
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• pp.349-359
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• 2005

In this study, we focused on the geographical and the meterological conditions, the atmospheric examination, the soil contents and compositions in order to establish cultural properties conservation plan in Gyeongju and its surroundings. Also, the transport route with environmental contaminants in Ulsan and Pohang was examined. The results could be summarized as follows ; Air pollutant and environmental contaminant was transported by two types of winds. One is induced by local winds, the other is induced by synoptic winds. Air contaminant transported from coastal regions to inland regions were associated with wind velocity. Gyeongju had good atmospheric conditions, i.e. $SO_2\;0.009{\sim}0.011ppm,\;CO\;0.6{\sim}0.8ppm,\;NO_2\;0.015{\sim}0.020ppm,\;O_3\;0.017{\sim}0.032ppm,\;PM_{10}\;46{\sim}62{\mu}g/m^3\;and\;Pb\;0.034{\sim}0.060{\mu}g/m^3$, which was below environmental air qualify standards and was little lower than those of Pohang and Ulsan. However, Ulsan and Pohang city are located on south-east coast and have many industrial facilities. Hence, air pollution problems become serious issues in Ulsan, Pohang, Busan, Daegu and other cities due to the emission of air pollutants from the various industrial facilities, incinerator and power plants, etc. The soil of Gyeongju had heavy metals conditions, i.e. $Cd\;0.01{\sim}0.08mg/kg,\;Cu\;N.D{\sim}2.39mg/kg,\;As\;N.D{\sim}0.07mg/kg,\;Hg\;N.D{\sim}0.15mg/kg,\;Pb\;0.49{\sim}1.39mg/kg,\;Cr^{+6}\;0.02{\sim}0.42mg/kg,\;Fe\;0.74{\sim}1.55mg/kg,\;Mn\;0.11{\sim}0.49mg/kg\;and\;Zn\;1.11{\sim}3.56mg/kg$. However, pH value of soil had range of $4.12{\sim}7.45$. The results showed that high pH concentration of soil could occur due to air pollution diffusion and environmental contaminant transport at Ulsan and Pohang city.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.4
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• pp.517-533
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• 2018

We utilize the CAMx (Comprehensive Air Quality Model with eXtensions) system and the PSAT (Particulate Source Apportionment Technology) diagnostic tool to determine the $PM_{2.5}$ concentration and to perform its source apportionment in the southeastern region of South Korea. For a year-long simulation, eight local authorities in the region such as Pohang, Daegu, Gyeongju, Ulsan, Busan-Gimhae, Gosung-Changwon, Hadong, and all remaining areas in Gyeongsangnam-do, are selected as source areas based on the emission rates of $NO_x$, $SO_x$, VOC, and primary PM in CAPSS (Clean Air Policy Support System) 2013 emissions inventory. The CAMx-PSAT simulation shows that Pohang has the highest $PM_{2.5}$ self-contribution rate (25%), followed by Hadong (15%) and Busan-Gimhae (14%). With the exception of Pohang, which has intense fugitive dust emissions, other authorities are strongly affected by emissions from their neighboring areas. This may be measured as much as 1 to 2 times higher than that of the self-contribution rate. Based on these estimations, we conclude that the efficiency of emission reduction measures to mitigate $PM_{2.5}$ concentrations in the southeastern region of South Korea can be maximized when the efforts of local or regional emission controls are combined with those from neighboring regions. A comprehensive control policy planning based on the collaboration between neighboring jurisdictional boundaries is required.

• Weed & Turfgrass Science
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• v.3 no.1
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• pp.6-12
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• 2014

The survey of weed occurrence was conducted to identify problem weed species in the paddy field. The 320 sites of the 13 regions in Gyeongbuk Province were investigated from June to July, 2013. In the whole region, 51 weed species were identified including 34 annuals and 17 perennials. The highest importance value of annual weed and perennial was dominant 88.4 and 48.7%, in Gumi region, respectively. The most dominant weed species in paddy fields of Gyeongbuk Province were Monochoria vaginalis var. plantaginea (10.80%), followed by Lemna paucicostata (10.74%), Bidens tripartite (8.77%), Echinochloa oryzoides (7.17%), and Scirpus juncoides (6.20%). The similarity of paddy weeds in 13 regions observed through TWINSPAN analysis was distinguished by Ludwigia prostrata, Scirpus juncoides, Lindernia dubia, Polygonum aviculare, and Alopecurus aequalis var. amurensis. The weed species, Ludwigia prostrata, was divided into the four regions, Gyeongju, Gumi(direct seeding cultivation), Youngcheon and Euiseong and ten regions, Gumi, Bonghwa, Sanju, Goryung, Youngdeog, Pohang, Sanju, Andong, Yecheon, and Cheongdo.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.6
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• pp.323-332
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• 2018

Recent two moderate earthquakes (2016 $M_w=5.4$ Gyeongju and 2017 $M_w=5.5$ Pohang) in Korea provided the unique chance of developing a set of relations to estimate instrumental seismic intensity in Korea by augmenting the time-history data from MMI seismic intensity regions above V to the insufficient data previously accumulated from the MMI regions limited up to IV. The MMI intensity regions of V and VI was identified by delineating the epicentral distance from the reference intensity statistics in distance derived by using the integrated MMI data obtained by combining the intensity survey results of KMA (Korea Meteorological Administration) and 'DYFI (Did You Feel It)' MMIs of USGS. The time-histories of the seismic stations from the MMI intensity regions above V were then preprocessed by applying the previously developed site-correction filters to be converted to a site-equivalent condition in a manner consistent with the previous study. The average values of the ground-motion parameters for the three ground motion parameters of PGA, PGV and BSPGA (Bracketed Summation of PGA per second for 30 seconds) were calculated for the MMI=V and VI and used to generate the dataset of the average values of the ground-motion parameters for the individual MMIs from I to VI. Based on this dataset, the linear regression analysis resulted in the following relations with proposed valid ranges of MMI. $MMI=2.36{\times}log_{10}(PGA(gal))+1.44$ ($I{\leq}MMI$$MMI=2.44{\times}log_{10}(PGV(kine))+4.86$ ($I{\leq}MMI$$MMI=2.59{\times}log_{10}(BSPGA(gal{\cdot}sec))-1.02$ ($I{\leq}MMI$

• Journal of Radiation Industry
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• v.17 no.3
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• pp.299-307
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• 2023

Given that toxic substances are diffused by the various movements of the atmosphere, it is very important to evaluate the risks associated with this phenomenon. When analyzing the behavioral characteristics of these atmospheric diffusion models, the main input data are the wind speed and wind direction among the meteorological data. In particular, it is known that a certain wind direction occurs in summer and winter in Korea under the influence of westerlies and monsoons. In this study, synoptic meteorological observation data provided by the Korea Meteorological Administration were analyzed from January 1, 2012 to the end of August of 2022 to understand the regional wind characteristics of nuclear power plants and surrounding areas. The selected target areas consisted of 16 weather stations around the Hanbit, Kori, Wolsong, Hanul, and Saeul nuclear power plants that are currently in operation. The analysis was based on the temperature, wind direction, and wind speed data at those locations. Average, maximum, minimum, median, and mode values were analyzed using long-term annual temperature, wind speed, and wind direction data. Correlation coefficient values were also analyzed to determine the linear relationships among the temperature, wind direction, and wind speed. Among the 16 districts, Uljin had the highest wind speed. The median wind speed values for each region were lower than the average wind speed values. For regions where the average wind speed exceeds the median wind speed, Yeongju, Gochang, Gyeongju, Yeonggwang, and Gimhae were calculated as 0.69 m s^-1, 0.54m s^-1, 0.45m s^-1, 0.4m s^-1, and 0.36m s^-1, respectively. The average temperature in the 16 regions was 13.52 degrees Celsius; the median temperature was 14.31 degrees and the mode temperature was 20.69 degrees. The average regional temperature standard deviation was calculated and found to be 9.83 degrees. The maximum summer temperatures were 39.7, 39.5, and 39.3 in Yeongdeok, Pohang, and Yeongcheon, respectively. The wind directions and speeds in the 16 regions were plotted as a wind rose graph, and the characteristics of the wind direction and speed of each region were investigated. It was found that there is a dominant wind direction correlated with the topographical characteristics in each region. However, the linear relationship between the wind speed and direction by region varied from 0.53 to 0.07. Through this study, by evaluating meteorological observation data on a long-term synoptic scale of ten years, regional characteristics were found.

• Korean Journal of Environmental Biology
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• v.31 no.3
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• pp.225-231
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• 2013

To study the characteristics of rocky intertidal invertebrate fauna on the coastal areas of the East Sea, seven regions including Dokdo, Ulleungdo, Gyeongju, Pohang, Yeongdeok, Uljin, and Gangwondo, the common species ratio (%) and similarity index using Bray-Curtis similarity matrix were calculated. The contributed species for dissimilarity between Dokdo and the other East Sea's coastal areas were selected by using SIMPER. The common species ratio and the cluster analysis showed that Ulleungdo presented the highest similarity. However, Yeongdeok showed the highest similarity in the eastern costal areas, and Gangwondo showed the lowest one. However the cluster analysis revealed the discrimination of the rocky intertidal invertebrate community on Dokdo with others region caused by the particularity of rocky shores exposed to strong wave action and by the particular distribution of rocky intertidal invertebrate fauna in Dokdo.

• Korean Journal of Veterinary Service
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• v.34 no.3
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• pp.251-258
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• 2011

This study was performed to determine the prevalence of Theileria sergenti (T. sergenti) in cattle reared in eastern areas of Gyeongbuk province by PCR. Among 443 samples collected from 42 cattle farms, 96 (21.7%) samples from 13 (31.0%) farms were positive for T. sergenti. By regions, 87 (26.6%) out of 327 cattle in Gyeongju, 8 (9.5%) out of 84 cattle in Pohang, and 1 (3.1%) out of 32 cattle in Ulleung were positive for T. sergenti. T. sergenti infection in dairy cattle (27.9%) was significantly higher than that in Korean cattle (9.4%, P<0.0001). Accordingly, Korean cattle were more resistant to T. sergenti infection. Prevalence of T. sergenti in cattle was increased with age (P<0.0001). The infection rate in cows (23.3%) was significantly higher than that in bulls (5.0%, P<0.01). Seasonally, prevalence of T. sergenti in cattle was highest in autumn (32.7%, P<0.01). Prevalence of T. sergenti in grazing cattle (66.7%) was significantly higher than that in non-grazing cattle (15.8%, P<0.0001). Since prevalence of T. sergenti infection is high in cattle reared in eastern areas of Gyeongbuk province, survey on other hemoparasites and appropriate control programs need to be established in this region.

• Journal of the Economic Geographical Society of Korea
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• v.23 no.1
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• pp.1-17
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• 2020

The study aims to identify economic interdependencies between regions and define functional economic areas of Korea by analyzing inter-firm transaction networks. Previous research has relied on pre-given administrative boundaries or cultural homogeneity and used data such as commuting, population movement, and cargo flows which could not fully explain economic activities. To overcome the limitations, this study applies a community detection method to inter-firm transaction networks derived from the CRETOP+ database of Korean corporate data. The novel dataset and the network analysis enables us to identify Korea's functional economic areas based on actual inter-firm linkages. The result shows that there are six to seven economic blocs in the networks as of 2018. In particular, one huge economic bloc is formed integrating the Seoul metropolitan area, Chungcheong, and Gangwon provinces. Meanwhile, North Jeolla and South Jeolla provinces form two economic blocs separately rather than being tied up in one bloc due to the low frequency of transactions between each other. The two big economic blocs of Daegu-Gyeongbuk and Busan-Gyeongnam exist, and interestingly, Ulsan, Gyeongju, and Pohang form a separate middle-sized bloc across the administrative boundaries. The results reveal that the future balanced national development policies should be implemented based on functional economic areas derived from empirical data.