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    Towards integrated catchment management : challenges surrounding implementation in the Gamtoos River catchment

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    Water resource management has become a pertinent issue of global environmental concern in response to the conditions of a growing global population, increasing development and a limited freshwater supply. It is against the backdrop of such conditions that effective water resource management has gained popularity in seeking to ensure that the needs of the growing population will be met and secured for future generations. The notion of integrated water resource management (IWRM) is a perspective on water resource management that has evolved out of the global opinion that social and ecological systems are linked and therefore cannot be managed separately. The department of water affairs (DWA) in South Africa highlights the importance of approaching management of water resources from a catchment perspective which forms the basis for a particular integrated approach to management called integrated catchment management (ICM). ICM recognizes the catchment as the correct administrative unit for management. It integrates water resources and the land that forms the catchment area in planning and management. Researchers have described the implementation of ICM as being complicated and difficult. This is no exception to South Africa. Principles of ICM have received widespread prominence in South Africa as they have been incorporated into national water policy. Actual implementation however is still in its infancy. The study is therefore a case study of ICM with respect to factors influencing implementation amongst different stakeholders. The study aims to explore the theme of implementation of ICM within the context of the Gamtoos River Catchment with a view toward identifying and addressing challenges that may be more broadly applicable. The study adopts an inductive, exploratory approach to the connection between theory and practice. A systems-based framework characterized by sequential steps similar to that employed in a case study conducted by Bellamy et al. (2001) in Queensland Australia is used to facilitate the evaluation of ICM in the Gamtoos River Catchment. The evaluation is achieved through a three step process of exploration in the current study. Triangulation is applied to the choice of methods of analysis which involves the use of a global analysis method, the use of learning scenarios and a grounded theory method. Findings reveal seven core themes which help to provide a detailed, contextual understanding relating to the status quo for ICM in the catchment. Results from a grounded theory analysis summarized the main challenges to implementation into five broad categories. Based on this analysis method and the application of the three learning scenarios for the Gamtoos River Catchment, the extent to which these challenges exist was discovered. The state of ICM in the catchment was classified as falling within a condition of a level of success being achieved with room for improvement to a condition of optimal ICM. The study concludes that based on the context of ICM being an example of a Complex Adaptive Systems (CAS), this state of ICM in the Gamtoos River Catchment is subject to change. This therefore necessitates the consideration of approaches to implementation that are adaptive to change. Findings may serve to inform decision making on how ICM can be effectively implemented elsewhere in a South African context

    ์ค‘๊ตญ๋ฐœ์ „ ์ค‘ ๋„์‹œ-์—ฐ๋ณ€์—์„œ์˜ ๋Œ€๊ธฐ ์ค‘ ์ด์‚ฐํ™”ํƒ„์†Œ์˜ ์—ฐ์†๊ด€์ธก ๋ฐ ์ง„๋‹จ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    High-Precision (ยฑ0.1 ppm), high-frequency (hourly averaged) measurements of atmospheric carbon dioxide (CO2) were made for the first time from May 2005 to November 2011 at Yanbian in China, using a Non-Dispersive Infrared (NDIR, Licor-6262) analyzer system with National Oceanic & Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) standards. This study presents about six and a half of years results from these measurements, including discussions on data quality control, data selection based on the characteristics of stations conditions, background data characteristics through comparing with other background observatories data in Northern Hemisphere (NH) and on the techniques for determining the CO2 emission source regions in northeast Asia according to the value of the emission strengths. Especially this study discusses the results at Yanbian compared with the results at Gosan-remote site to investigate the similarities and differences. In order to obtain adequate observation data, high-precision, high-frequency in situ measurements of CO2 based on well-designed measurement equipment is very important. Strict data quality control and assurance procedures were carried out throughout the data acquisition. Data selection was carefully performed based on the characteristics in Yanbian. In this study, local data, regional data were divided from the total valid measurement data, and regional background data and regional emission data were classified from the regional data. Each step, we apply different methods which were discussed respectively. We discussed the characteristics of sampling data and the potential source direction with the method of wind weighted CO2 concentration and variation with meteorological conditions. We also investigated the local and regional data contributing effect with the method of wind weighted appropriated CO2 concentration. The increasing trend of regional background data of all sampling period with about 1.9 ppm/yr was in good agreement with the global growth rate in recent years. The characteristics of regional background CO2 concentrations of in-situ measurement at Yanbian from August 2005 to July 2007 were discussed in detail and compared with those of other NH global baseline and regional background stations. A comparison of the regional background data at Yanbian with concentrations at other background observatories in the middle-to-high Northern Hemisphere demonstrated the regional representativeness of the former. The large seasonal variation observed from the regional background data at Yanbian was in excellent agreement overall with those found by other observatories in the middle-to-high latitudes in the Northern Hemisphere and represents special characteristics: the summer concentration drops to a slightly lower than Ulaan Uul, and the winter concentration rises to be on par with Anmyeon-do. Particularly, the distinctive characteristics at Yanbian were evident through comparing with the similar latitude and altitude regional background site โ€“Longfengshan, and with the lower latitude and higher altitude global baseline site-Mauna Loa. And the study also discussed the results of Yanbian compared with that of Gosan- remote site as the representative of Northeast Asia. Because the period of dataset of two sites does not overlap, at first datasets were detrended appropriately at both sites to compare the characteristics objectively. From those results, the diurnal and seasonal variation at Yanbian was shown to be stronger than at Gosan, increasing trend was similar with at Gosan. The regional emission data of two-year from August 2005 to July 2007, the elevated concentration above background, combined with three-dimensional synoptic meteorology from the HYSPLIT4 (Hybrid Single-Particle Lagrange Integrated Trajectory) model were used to estimate the regional source region according to the value of emission strength contributing to receptor site (Yanbian). We limited the modeling period to between November-April (cold period) to reduce uncertainties in the model. Due to the winter monsoon, air mass predominantly comes from north China and allow for better capturing of the signal from heating period. The model results indicate the strongest potential source areas contributing to measurement site at Yanbian are the southwestern part of Shandong Province including Jinan, Beijing & Tianjin metropolitan region and Vladivostok. In addition, the tracer-ratio (CO as a trace) technique was used to make preliminary estimates the CO2 emission of China in 2006. The results showed emissions twice as high as the previous inventory. Granted that the results of this study may be somewhat overestimated because we refer the ratio of 6 months averages as a year value, our results suggest that the previous CO2 emission inventory of China maybe underestimated in not fully considering the fossil fuel emissions for the heating period in northern China. The results of this study reveal the usefulness of in situ CO2 measurements at Yanbian for establishing the scientific foundation for monitoring the large CO2 emission areas in northern China and eastern Russia. Although situated near large local emission sources, with careful and scientific data selection, our results conformed that continued monitoring of CO2 at Yanbian within a regional network can make significant contributions in understanding both the global/regional carbon cycle and constraining top-down emissions in northeast Asia.์ค‘๊ตญ์—ฐ๋ณ€์‚ฌ์ดํŠธ์—์„œ ์ฒ˜์Œ์œผ๋กœ 2005๋…„ 8์›”๋ถ€ํ„ฐ ํ˜„์žฌ๊นŒ์ง€ ๋น„๋ถ„์‚ฐํ˜•์ ์™ธ์„ ๋ถ„์„๊ธฐ (NDIR, Licor-6262) ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ณ  ์ •๋ฐ€ (ยฑ0.1 ppm), ๊ณ  ๋นˆ๋„ (hour-1) ์˜ ๋Œ€๊ธฐ ์ค‘ ์ด์‚ฐํ™”ํƒ„์†Œ์˜ ๊ด€์ธก์ด ์ด๋ฃจ์–ด์กŒ์œผ๋ฉฐ NOAA์˜ CMDL/CCGG์—์„œ ์ œ๊ณตํ•˜๋Š” ์ด์‚ฐํ™”ํƒ„์†Œ ํ‘œ์ค€์‹œ๋ฃŒ๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” 6๋…„ ๋ฐ˜์˜ ๋ฐ์ดํ„ฐ๊ฐ€ ์‚ฌ์šฉ์ด ๋˜์—ˆ์œผ๋ฉฐ ๋ฐ์ดํ„ฐ์˜ ๊ฒ€์ฆ, ๋ฐ์ดํ„ฐ์˜ ๋ถ„์„ ๋ฐ ๋ถ๋ฐ˜๊ตฌ์— ์œ„์น˜ํ•œ ๋‹ค๋ฅธ ๋ฐฐ๊ฒฝ๊ด€์ธก์†Œ์™€์˜ ๋น„๊ต๋ถ„์„์„ ํ†ตํ•˜์—ฌ ์ง€์—ญ์  ๋ฐฐ๊ฒฝ๋Œ€๊ธฐ๋†๋„์˜ ํŠน์ง•์— ๋Œ€ํ•˜์—ฌ ํ† ๋ก ํ•˜์˜€์œผ๋ฉฐ ์ง€์—ญ์  ๋ฐฐ์ถœ๊ฐ•๋„์— ๊ทผ๊ฑฐํ•˜์—ฌ ๋™๋ถ์•„์‚ฌ์•„์—์„œ์˜ ์ž ์žฌ์ ์ธ ์†Œ์Šค์ง€์—ญ์„ ์ถ”์ •ํ•˜๋Š” ๊ธฐ๋ฒ•์— ๋Œ€ํ•˜์—ฌ ํ† ๋ก ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํŠน๋ณ„ํžˆ ๊ณ ์‚ฐ, Longfengshan, ๋ฐ ๋งˆ์šฐ๋‚˜ ๋กœ์•„์™€์˜ ์„ธ๋ฐ€ํ•œ ๋น„๊ต๋ถ„์„์„ ํ†ตํ•˜์—ฌ ์—ฐ๋ณ€์‚ฌ์ดํŠธ์˜ ํŠน์ง•์„ ํ•œ์ธต ๋” ์‹ฌ๋„์žˆ๊ฒŒ ์‚ดํŽด๋ณด์•˜๋‹ค. ์ •ํ™•ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ํš๋“ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ณ  ์ •๋ฐ€, ๊ณ  ๋นˆ๋„์˜ ์—ฐ์†๊ด€์ธก์ด ๊ฐ€๋Šฅ ํ•œ ์ •ํ™•ํ•œ ์žฅ๋น„๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์—„๊ฒฉํ•œ ์ž๋ฃŒ๊ฒ€์ฆ์ ˆ์ฐจ๋Š” ์ž๋ฃŒ๋ฅผ ํš๋“ํ•˜๋Š” ์ „๋ฐ˜์— ๊ฑธ์ณ ๋ชจ๋‘ ์‹ค์‹œ๋œ๋‹ค. ์—ฐ๋ณ€์‚ฌ์ดํŠธ์˜ ํŠน์ง•์— ๊ทผ๊ฑฐํ•˜์—ฌ ์ž๋ฃŒ์˜ ์„ ๋ณ„์ด ์„ธ๋ฐ€ํžˆ ์ด๋ฃจ์–ด์ง„๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์šฐ์„  ์ „์ฒด ์œ ํšจํ•œ ๋ฐ์ดํ„ฐ๋ฅผ local data์™€ regional data๋กœ ๋ถ„๋ฅ˜ํ•˜์˜€์œผ๋ฉฐ regional data๋ฅผ ์ง„์ผ๋ณด regional background data ์™€ regional emission data๋กœ ๋ถ„๋ฅ˜ํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์ž๋ฃŒ์„ ๋ณ„์˜ ๋งค๊ฐœ ์ ˆ์ฐจ์—์„œ ์‚ฌ์šฉ ๋˜์–ด์ง„ ๋ถ€๋™ํ•œ ๋ฐฉ๋ฒ•์€ ๊ฐ๊ฐ ์ƒ์„ธํžˆ ์„ค๋ช…์„ ๋“œ๋ ธ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ „์ฒด ๋ฐ์ดํ„ฐ์˜ ํŠน์ง•์— ๋Œ€ํ•˜์—ฌ ํ† ๋ก ์„ ํ•˜์˜€๊ณ  wind weighted CO2 concentration๋ฐฉ๋ฒ•์œผ๋กœ ์ž ์žฌ์ ์ธ ์†Œ์Šค๋ฐฉํ–ฅ์„ ์ถ”์ •ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  local๊ณผ regional๋ฐ์ดํ„ฐ์— ๊ทผ๊ฑฐํ•˜์—ฌ wind weighted ์ƒ์‘ํ•œ CO2 concentration. ํ•˜์—ฌ local ๋ฐ regional์˜ ์ž ์žฌ์ ์ธ ์†Œ์Šค๋ฐฉํ–ฅ์„ ์ถ”์ •ํ•˜์˜€๋‹ค. ์ „์ฒด๊ด€์ธก๊ธฐ๊ฐ„ ๋™์•ˆ regional background data์˜ ์ฆ๊ฐ€์ถ”์„ธ๋Š” 1.9 ppm/yr๋กœ์„œ ์ตœ๊ทผ์˜ ์ „์ง€๊ตฌ์ ์ธ ์ฆ๊ฐ€์ถ”์„ธ์™€ ์ผ์น˜ํ•˜๋‹ค. 2005๋…„ 8์›”๋ถ€ํ„ฐ 2007๋…„ 7์›”๊นŒ์ง€์˜ ์—ฐ์†๊ด€์ธก์—์„œ์˜ regional ๋ฐฐ๊ฒฝ CO2 ๋†๋„์˜ ํŠน์ง•์— ๋Œ€ํ•˜์—ฌ ์ž์„ธํ•œ ํ† ๋ก ์„ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฉฐ ๋ถ๋ฐ˜๊ตฌ์— ์œ„์น˜ํ•œ ๋‹ค๋ฅธ global baseline ๊ณผ regional background์˜ ๊ด€์ธก์†Œ์™€ ๋น„๊ต๋ถ„์„์„ ์ง„ํ–‰ํ•จ์œผ๋กœ์จ ์—ฐ๋ณ€์ง€์—ญ์ ์ธ ๋Œ€ํ‘œ์„ฑ์„ ํ•œ์ธต ๋” ์ฆ๋ช…ํ•˜์—ฌ ์ฃผ์—ˆ๋‹ค. ์—ฐ๋ณ€์‚ฌ์ดํŠธ์—์„œ์˜ regional background data์˜ ํฐ ๊ณ„์ ˆ์ ์ธ ๋ณ€ํ™”๋Š” ๋ถ๋ฐ˜๊ตฌ์— ์œ„์น˜ํ•œ ๋‹ค๋ฅธ ๊ด€์ธก์†Œ์™€์˜ ๊ฐ’๊ณผ ๋น„์Šทํ•œ ํŠน์ง•์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ์˜ˆ๋ฅผ ๋“ค๋ฉด ์—ฌ๋ฆ„์ฒ ์—๋Š” Ulaan Uul๋ณด๋‹ค ์กฐ๊ธˆ ๋‚ฎ์€ ๊ฐ’์„ ๋‚˜ํƒ€๋‚ด๊ณ  ๊ฒจ์šธ์ฒ ์—๋Š” ์•ˆ๋ฉด๋„์™€ ๋น„์Šทํ•œ ๊ฐ’์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ํŠน๋ณ„ํ•˜๊ฒŒ, ๋น„์Šทํ•œ ์œ„๋„ ๋ฐ ๊ณ ๋„์— ์œ„์น˜ํ•œ Longfengshan์‚ฌ์ดํŠธ์™€์˜ ๋น„๊ต ๋ฐ๋” ๋‚ฎ์€ ์œ„๋„ ๋ฐ ๋†’์€ ๊ณ ๋„์— ์œ„์น˜ํ•œ Mauna Loa์‚ฌ์ดํŠธ์™€์˜ ๋น„๊ต๋ถ„์„์„ ํ†ตํ•˜์—ฌ ์—ฐ๋ณ€์‚ฌ์ดํŠธ์˜ ํŠน์ง•์ด ๋” ๋‘๋“œ๋Ÿฌ์ง€๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์—ฐ๋ณ€์—์„œ์˜ ๊ด€์ธก ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋™๋ถ์•„์‹œ์•„๋ฅผ ๋Œ€ํ‘œํ•˜๋Š” ๋จผ ์‚ฌ์ดํŠธ์ธ ๊ณ ์‚ฐ์˜ ๊ด€์ธก์น˜์™€ ๋น„๊ต๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋‘ dataset์˜ ๊ด€์ธก๊ธฐ๊ฐ„์ด ๊ฒน์น˜์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— ์šฐ์„  ๋ชฉ์ ์„ฑ ์žˆ๊ฒŒ ์ผ์ •ํ•œ ๊ฐ’์— ๋Œ€ํ•˜์—ฌ detrend๋ฅผ ํ•˜์—ฌ ๊ทธ ํŠน์ง•์— ๋Œ€ํ•˜์—ฌ ๋น„๊ต ๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ๋ฅผ ์‚ดํŽด๋ณด๋ฉด ์—ฐ๋ณ€์—์„œ์˜ ์ผ๋ณ€ํ™” ๋ฐ ๊ณ„์ ˆ์ ์ธ ๋ณ€ํ™”๋Š” ๊ณ ์‚ฐ๋ณด๋‹ค ๋” ๊ฐ•ํ•จ์„ ๋‚˜ํƒ€๋‚ด์—ˆ๊ณ  ์ฆ๊ฐ€์ถ”์„ธ๋Š” ๊ณ ์‚ฐ๊ณผ ์œ ์‚ฌํ•œ ๊ฐ’์„ ๋‚˜ํƒ€๋ƒˆ๋‹ค. 2005๋…„ 8์›”๋ถ€ํ„ฐ 2007๋…„ 7์›”๊นŒ์ง€์˜ 2๋…„์˜ regional emission data์—์„œ regional background data๋ณด๋‹ค ์ฆ๊ฐ€ ๋œ ๊ฐ’์„ HYSPLIT4 (Hybrid Single-Particle Lagrange Integrated Trajectory) ๋ชจ๋ธ๋กœ ์–ป์€ ์‚ผ์ฐจ์›์˜ ๊ณต๊ฐ„์  ๊ธฐ์ƒ์ž๋ฃŒ์™€ ๊ฒฐํ•ฉํ•˜์—ฌ ํš๋“ํ•œ ๋ฐฐ์ถœ๊ฐ•๋„์— ๊ทผ๊ฑฐํ•˜์—ฌ receptor site (Yanbian) ์— ์˜ํ–ฅ์„ ์ฃผ๋Š” potential source region์„ ์ถ”์ •ํ•˜์˜€๋‹ค. ์šฐ๋ฆฌ๋Š” ๋ชจ๋ธ์˜ ๋ถˆ ์ •ํ™•๋„๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•˜์—ฌ ๋ชจ๋ธ๊ธฐ๊ฐ„์„ 11์›”๋ถ€ํ„ฐ ์ด๋“ฌํ•ด 4์›”๊นŒ์ง€(์ถ”์šด ๊ณ„์ ˆ)๋กœ ์ •ํ•˜์˜€์œผ๋ฉฐ ์ด ์‹œ๊ธฐ์— ๊ฒจ์šธ๋ชฌ์ˆœ์— ์˜ํ•˜์—ฌ ๊ธฐ๋‹จ์ด ๋Œ€๋ถ€๋ถ„ ์ค‘๊ตญ์˜ ๋ถ๋ฐฉ์—์„œ ์˜ค๊ณ  ์žˆ์–ด ๋ชจ๋ธ์„ ์‘์šฉํ•˜๊ธฐ ์ ํ•ฉํ•œ ์‹œ๊ธฐ์ธ ๊ฒƒ์œผ๋กœ ์—ฌ๊ฒจ์ง„๋‹ค. ๋ชจ๋ธ๊ฒฐ๊ณผ๋ฅผ ์‚ดํŽด๋ณด๋ฉด ์—ฐ๋ณ€์‚ฌ์ดํŠธ์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฐ€์žฅ ํฐ ์ž ์žฌ์ ์ธ ์†Œ์Šค์ง€์—ญ์œผ๋กœ๋Š” ์ œ๋‚จ์„ ํฌํ•จํ•œ ์‚ฐ๋™์„ฑ ์„œ๋‚จ์ชฝ, ๋ถ๊ฒฝ ๋ฐ ์ฒœ์ง€๋Œ€๋„์‹œ ์ง€์—ญ, ๊ทธ๋ฆฌ๊ณ  ์šธ๋ผ๋””๋ณด์Šค๋…์œผ๋กœ ์ถ”์ •์ด ๋˜์—ˆ๋‹ค. ์ด์™ธ์— tracer-ratio (CO as a tracer) ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ 2006๋…„ ์ค‘๊ตญ์˜ CO2 ๋ฐฐ์ถœ์–‘์„ ์ดˆ๋ณด์ ์œผ๋กœ ์ถ”์ •ํ•˜์—ฌ ๋ณธ ๊ฒฐ๊ณผ ๊ธฐ์กด์˜ ๋ฐฐ์ถœ์–‘๋ณด๋‹ค ๋‘ ๋ฐฐ ๋˜๋Š” ๊ฐ’์„ ๋ณด์˜€๋‹ค. ์ด ๊ฒฐ๊ณผ๋Š” 6๊ฐœ์›” ์ถ”์šด ์‹œ๊ธฐ์˜ ๋น„์œจ์„ ์ „์ฒด ์ผ๋…„์˜ ๋น„์œจ๋กœ ๊ฐ„์ฃผํ•˜์—ฌ ๋ฐฐ์ถœ ์–‘์„ ์‚ฐ์ •ํ•˜์˜€๊ธฐ์— ๊ณผ๋Œ€ํ‰๊ฐ€๋  ๊ฐ€๋Šฅ์„ฑ์ด ํฌ๋‹ค ํ• ์ง€๋ผ๋„ ๊ธฐ์กด์˜ ๋ฐฐ์ถœ์–‘์€ ์ค‘๊ตญ ๋ถ๋ฐฉ์˜ ๋‚œ๋ฐฉ๊ธฐ๊ฐ„ ๋™์•ˆ ํ™”์„์—ฐ๋ฃŒ์— ์˜ํ•œ ๋ฐฐ์ถœ์„ ์ถฉ๋ถ„ํžˆ ๊ณ ๋ คํ•˜์ง€ ๋ชปํ•œ ๊ด€๊ณ„๋กœ ๊ณผ์†Œํ‰๊ฐ€๋จ์„ ์ œ์‹œํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ์—ฐ๋ณ€์—์„œ์˜ CO2 ์—ฐ์†์ ์ธ ๊ด€์ธก์ด ์ค‘๊ตญ๋ถ๋ฐฉ๊ณผ ๋Ÿฌ์‹œ์•„๋™๋ฐฉ์—์„œ์˜ ํฐ CO2 ๋ฐฐ์ถœ์ง€์—ญ์˜ ๋ชจ๋‹ˆํ„ฐ๋ง์— ๊ณผํ•™์ ์ธ ๊ธฐ๋ฐ˜์„ ๋งˆ๋ จํ•จ์— ์žˆ์–ด์„œ ์œ ์šฉํ•  ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋น„๋ก ๋ฐฐ์ถœ์ง€์—ญ๊ณผ ๊ฐ€๊นŒ์šด ๊ณณ์— ์œ„์น˜ํ•˜๊ณ  ์žˆ์ง€๋งŒ ์„ธ์‹ฌํ•˜๊ณ  ๊ณผํ•™์ ์ธ ์ž๋ฃŒ์„ ๋ณ„์„ ์ง„ํ–‰ํ•œ๋‹ค๋ฉด ์ง€์—ญ์ ์ธ ๋„คํŠธ์›Œํฌ์†์—์„œ์˜ ์—ฐ๋ณ€์—์„œ์˜ CO2์˜ ์ง€์†์ ์ธ ๋ชจ๋‹ˆํ„ฐ๋ง์€ global/regional์˜ ํƒ„์†Œ์ˆœํ™˜์„ ์ดํ•ดํ•˜๊ณ  ๋™๋ถ์•„์‹œ์•„์ง€์—ญ์—์„œ์˜ top-down ๋ฐฐ์ถœ ์–‘์„ ์‚ฐ์ •ํ•จ์— ์žˆ์–ด์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Docto

    Estimation of Potential Source Region in Northeast Asia through Continuous In-situ Measurement of Atmospheric CO2 at Gosan, Jeju Island, Korea

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    High-Precision (ยก_0.1 ppm), high-frequency (hourly averaged) measurement of atmospheric carbon dioxide (CO2) was made at Gosan Station on Jeju Island, Korea, using a Non-dispersive Infrared (NDIR) analyzer calibrated with National Oceanic and Atmospheric Administration/Earth Sys tem Re search Laboratory standards. This paper presents the one-year results from these measurements, including discussions on data quality control and data selection, data characteristics through comparing with other regional data and on the techniques for estimating potential source regions of pollution emissions in Northeast Asia with pollution events in the record

    Theoretical Analysis of the Catalytic Hydrolysis Mechanism of HCN over Cu-ZSM-5

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    HCN catalytic hydrolysis mechanism over Cu-ZSM-5 was investigated based on the density functional theory (DFT) with 6-31++g (d, p) basis set. Five paths (A, B, C, D, and E) were designed. For path A and path B, the first step is the nucleophilic attack of water molecule. Next, the hydrogen atom of H2O is transferred to the nitrogen atom first for path A, while in path B, the hydrogen atom of the HCN is first transferred to the nitrogen atom. In path C, HCN isomerizes to HNC initially, and the remaining steps are similar to that of path A. The H atom of HCN shifts to Cu-ZSM-5 initially in path D, and the H atom is transferred to N atom subsequently. The last step is the attack on water molecule. The first step for path E is similar to that of path D. The next step is the attack on water molecule, in which the H atom of water molecule shifts to N atom, and the H on Cu-ZSM-5 shifts to the N atom. Meanwhile, the H atom of oxygen atom is transferred to the N atom. The results show that path C is the most favorable path, with the lowest free energy barrier (35.45 kcal/mol). The results indicate that the Cu-ZSM-5 strongly reduces the energy barrier of HCN and isomerizes to HNC, making it an effective catalyst for HCN hydrolysis

    Theoretical Analysis of the Catalytic Hydrolysis Mechanism of HCN over Cu-ZSM-5

    No full text
    HCN catalytic hydrolysis mechanism over Cu-ZSM-5 was investigated based on the density functional theory (DFT) with 6-31++g (d, p) basis set. Five paths (A, B, C, D, and E) were designed. For path A and path B, the first step is the nucleophilic attack of water molecule. Next, the hydrogen atom of H2O is transferred to the nitrogen atom first for path A, while in path B, the hydrogen atom of the HCN is first transferred to the nitrogen atom. In path C, HCN isomerizes to HNC initially, and the remaining steps are similar to that of path A. The H atom of HCN shifts to Cu-ZSM-5 initially in path D, and the H atom is transferred to N atom subsequently. The last step is the attack on water molecule. The first step for path E is similar to that of path D. The next step is the attack on water molecule, in which the H atom of water molecule shifts to N atom, and the H on Cu-ZSM-5 shifts to the N atom. Meanwhile, the H atom of oxygen atom is transferred to the N atom. The results show that path C is the most favorable path, with the lowest free energy barrier (35.45 kcal/mol). The results indicate that the Cu-ZSM-5 strongly reduces the energy barrier of HCN and isomerizes to HNC, making it an effective catalyst for HCN hydrolysis
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