• Journal of Environmental Impact Assessment
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• v.20 no.1
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• pp.49-59
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• 2011

The results of comparing $PM_{10}$ concentration between 'Namsan' and 'Yongsan-gu' air quality monitoring stations show similar values with averaged concentration in the whole Seoul. The correlation factors in both sites were 0.865, 0.828 in 2005, 2006, respectively. For 'Bukhansan' and 'Gangbuk-gu' air quality monitoring stations, different from the results mentioned above, they showed clear differences as altitude changes. PM10 concentration in 'Bukhansan' monitoring stations was 10 ${\mu}g/m^3$ lower than 'Gangbuk-gu' monitoring station which is located near the ground. Also, averaged PM10 concentration in 'Bukhansan' and 'Gangbuk-gu' monitoring stations was lower than that in the whole Seoul. When comparing $NO_2$ concentration between 'Namsan' and 'Yongsan-gu' monitoring stations, $NO_2$ concentration in 'Namsan' monitoring station was lower than 'Yongsan-gu' monitoring station. For $NO_2$ concentration in 'Bukhansan', 'Gangbuk-gu' and 'the whole Seoul', there were the same pattern in 'Gangbuk-gu' and the 'the whole Seoul' and low values in 'Bukhansan' monitoring station. The correlation factors of $NO_2$ concentration in 'Bukhansan' and 'Gangbukgu' was 0.525, 0.549 in 2005, 2006, respectively, which stands for low correlationship.

• Proceedings of the Korean Society of Applied Pharmacology
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• 2002.07a
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• pp.240-240
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• 2002

• Proceedings of the PSK Conference
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• 2001.04a
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• pp.238.1-238.1
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• 2001

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.140-148
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• 2022

Hydrogen is expected to be widely applied in most sectors within the current energy system, such as transportation and logistics, and is expected to be economically and technologically utilized as a power source to achieve vehiclebon emission reduction. In particular, the construction of hydrogen charging station infrastructure will not only support the distribution of hydrogen electric vehicles, but also play an important role in building a hydrogen logistics system. Therefore, This paper suggest additional charging infrastructure areas in Seoul with a focus on supply according to the annual average growth rate (CAGR), centering on Seoul, where hydrogen vehicles are most widely distributed. As of February 2022, hydrogen charging infrastructures were installed in Gangseo-gu, Gangdong-gu, Mapo-gu, Jung-gu, and Seocho-gu in downtown Seoul. Next, looking at the number of hydrogen vehicles by administrative dong in Seoul from 2018 to 2022, Seocho-gu has the most with 246 as of 2022, and Dongjak-gu has the highest average growth rate of 215.4% with a CAGR of 215.4%. Therefore, as a result of CAGR analysis, Dongjak-gu is expected to supply the most hydrogen vehicles in the future, and Seocho-gu currently has the most hydrogen vehicles, so it is likely that additional hydrogen charging infrastructure will be needed between Dongjak-gu and Seocho-gu.

• Journal of Korean Society for Atmospheric Environment
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• v.31 no.1
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• pp.83-91
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• 2015

Biomass burning is known to be one of the main sectors emitting greenhouse gases as well as air pollutants. Unfortunately, the inventory of biomass burning sector has not been established well. We estimated greenhouse gas (GHG) and air pollution (AP) integrated emissions from biomass burning sector in Seoul during year 2010. The data of GHG and AP emissions from biomass burning, classified into open burning, residential fireplace and wood stove, meat cooking, fires, and cremation, were obtained from Statistics Korea and Seoul City. Estimation methodologies and emission factors were gathered from reports and published literatures. Estimated GHG and AP integrated emissions during year 2010 were $3,867tonCO_{2eq}$, and 2,320 tonAP, respectively. Major sources of GHG were forest fires ($1,533tonCO_{2eq}$) and waste open burning ($1,466tonCO_{2eq}$), while those of AP were meat cooking (1,240 tonAP) and fire incidence (907 tonAP). Total emissions by administrative district in Seoul, representing similar patterns in both GHG and AP, indicated that Seocho-gu and Gangseo-gu were the largest emitters whereas Jung-gu was the smallest emitter, ranged in $2{\sim}165tonCO_{2eq}$ and 0.1~8.31 tonAP. GHG emissions per $km^2$ showed different results from total emissions in that Gwanak-gu, Jungnang-gu, Gangdong-gu and Seodaemun-gu were the largest emitters, while Seocho-gu and Gangseo-gu were near-averaged emission districts, ranged in $0.2{\sim}21tonCO_{2eq}/km^2$. However, AP emissions per $km^2$ revealed relatively minor differences among districts, ranged in $2.3{\sim}6.1tonAP/km^2$.

• Korean Journal of Health Education and Promotion
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• v.29 no.1
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• pp.39-57
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• 2012

Objectives: This study was to measure community capacity using individual-level and organizational-level capacity indicators and illuminated the relationship of community capacity and self-rated health status in two regions in Seoul, Korea. Methods: The data from individual surveys were obtained by quota sampling the residents of two autonomous gu in Seoul (N=1,000). The data from organizational surveys were obtained by snowball sampling lists of organizations in the possession of gu offices with a sampling frame (N=153). The survey tools were 6 indicators regarding residents' social capital and a sense of community and 5 indicators regarding community-based organizations and their networks. The analysis methods consisted of the effect of the components of capacity on health status and social network analysis. Results: As for capacity on individual levels, while D-gu was mainly developed inn individual capacity in terms of social interaction, Y-gu was stronger in a sense of community and cohesion among residents. As for capacity on organizational levels, Y-gu was more developed than was D-gu in associational networks. Conclusion: It is necessary to develop health promotion program per community and to strengthen partnerships with and among grassroots organizations based in local communities through the measurement of community capacity.

• Journal of the Korean Institute of Landscape Architecture
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• v.35 no.1 s.120
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• pp.59-68
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• 2007

This article statistically analyzed contributive levels of district image based on an effect and a similarity index through the evaluation of citizens and suggested the efficient management system of a city image according to the results. For this study, Busan City was selected as a case city by the preceding literature and was investigated concerning district image and city image through a questionnaire. The new evaluation method for analysis of a city image was presented in this process. The results of this research are as follows: 1. Busan City has a substantial positive and culturally unique image, and each of its districts have other image characteristics. for example, the CBD district has a positive image, and the sea shore district has a busy and prosperous image, but the backward sea shore district has an image of stagnancy. 2. The image of Yeonje-gu has the largest effect on the image of Busan. Next in influence are Jung-gu, Saha-gu, Suyoung-gu, respectively. The effect index is closely connected with the variance of evaluative adjectives. 3. Busanjin-gu and Haeundae-gu have similar images to Busan City. Next in similarity are Nam-gu, Jung-gu, Youngdo-gu, Suyoung-gu, respectively. The similarity index is closely connected with the correlation of evaluative adjectives. Busan City and its districts can establish their image strategies with the above analyzed results. This study is meaningful in that a statistical evaluative method was proposed. With continued follow-up research, this study may serve as a systematic and logical model to improve the urban landscape and image.

• Journal of Environmental Impact Assessment
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• v.25 no.3
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• pp.222-232
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• 2016

Water Footprint is utilized to analyze direct and indirect water consumption for sustainable water resource management. This study aims to understand potential applicability of water footprint concept by analyzing the status of water consumption and related water policies in Seoul. We analyzed a direct gray water footprint and the blue water footprint in Seoul affected by the social and economic characteristics of the consumers in the city. In particular, in order to analyze the blue water footprint represented by both surface and underground water for the provision and consumption of products, we calculated the actual water consumptions of surface and underground water for 25 districts in Seoul. Our analysis in consideration of population and households indicates that Jung-gu has the highest blue water footprint followed by Jongro-gu, Gangnam-gu, Yongsan-gu, and Seocho-gu. Gray water footprint was calculated by estimating the amount of water for purifying wastewater to meet the water quality standard (above BOD 3.5ppm) for each district. As a result, Jung-gu has the highest gray water footprint, followed by Jongro-gu, Gangnam-gu, Yongsan-gu, Seocho-gu, and Youngdeungpo-gu. Our study suggests the potential value of using water footprint concept to complement the current limitations of water use management focusing on water supply control. We expect that our analysis will provide an important basis for considering water use management which is economically and socially more resilient and sustainable.

• The Journal of the Korean dental association
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• v.54 no.8
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• pp.613-629
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• 2016

This study assayed regional distribution of dental hospital & dental clinics, the number of population & households per one dental hospital & clinic, operation condition & duration. This study used public data that display from 1946 years(the first dental clinic open in republic of korea) to 2016 years. We collected present condition of 21,686 dental hospital and clinics available in public data portal site on 28. Feb.2016. Data were classified by scale, location, permission year, operation duration of dental hospital & clinics and were analyzed using SPSS 20.0 program. Surveyed on Feb. 2016. Best top 10 regions of permission dental clinics are (1) Gangnam-gu, Seoul(1,337), (2) Seongnamsi, Gyeonggi-do(555), (3) Songpa-gu, Seoul(491), (4) Yeongdeungpo-gu, Seoul(472), (5) Suwon-si, Gyeonggi-do(443), (6) Seocho-gu, Seoul(428), (7) Nowon-gu, Seoul(417), (8) Goyang-si, Gyeonggi-do(413), (9) Jung-gu, Seoul(380), (10) Yongin-si, Gyeonggi-do(353). Whereas best top 10 regions of operating dental clinics are (1) Gangnam-gu, Seoul(581), (2) Seongnamsi, Gyeonggi-do(415), (3) Suwon-si, Gyeonggi-do(382), (4) Seocho-gu, Seoul(320), (5) Changwon-si, Gyeongsangnam-do(303), (6) Songpa-gu, Seoul(295) (7) Goyang-si, Gyeonggi-do(290), (8) Bucheon-si and Yongin-si, Gyeonggi-do(262), (9) Jeonju-si, Jeollabuk-do(224). Average population per one dental hospital & clinic by regional local government are 3,120 people. Best five region of population per one dental hospital & clinic are (1) Sejong-si(5,272), (2) Gangwon-do(4,653), (3) Chungcheongbuk-do(4,513), (4) Gyeongsangbuk-do(4,490), (5) Chungcheongnam-do(4,402). Average households per one dental hospital & clinic by regional local government are 1,316 households. Best three region of households per one dental hospital & clinic are (1) Sejong-si(2,126), (2) Gangwon-do(2,057), (3) Gyeongsangbuk-do(1,946). From 1946 to 1986, permission and operating dental hospital and clinics was steadily increasing. On 1986-1990, 1991-1995, permission, operation and closure of dental hospital and clinics increase rapidly. From the 2011-2015 to 2016(present), permission, operation and closure of dental hospital and clinics is decreasing. Average operating duration of closured dental hospital and clinics are 14.054 years. We need to map of dental hospital and clinics for open and operation of one, base on analyzed results. In an era of 30,000 dentist, we should to be concerned about operation of dental clinics in the light of past operating condition.

• Proceedings of the Zoological Society Korea Conference
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• 1998.10b
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• pp.282.2-282
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• 1998

No Abstract, See Full Text