• Proceedings of the Korea Air Pollution Research Association Conference
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• 1999.10a
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• pp.307-308
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• 1999

오존전량은 대류권계면 고도[Hoinka at al., 19961, 기온과 지위 고도[Spankuch and Schulz, 1995], 잠재 와도[Vaughan and Price, 1991] 또는 상대 와도와 같은 기상 변수와 높은 상관관계를 나타낸다. 그리고 성층권 오존량의 감소는 지표 유해자외선을 증가시킨다는 연구 결과가 발표되고 있다(예: 조회구 등 1998; Zerefos et al. 1997).(중략)

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.04a
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• pp.351-352
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• 2000

최근 도시계획으로 인해 변화되는 도시기후에 대한 관심이 집중되면서 도시 환경 문제와 관련되어 중요시되는 기상변수중 하나가 바람이라 할 수 있다. 도시 바람장에 대한 연구는 향후 2000년대에는 전세계 인구의 60%가 5000명 이상이 거주하는 도시에 밀집될 것이라는 예측(Sievers and Zdunkowski, 1986) 과 더불어 도시민의 생활 환경 개선에 미치는 영향이 매우 클 것으로 생각된다. 도시 바람장 연구를 위해서는 3차원적인 순환구조에 대한 파악이 필요한데 이는 모델을 이용한 수치모의로서 가능하다. (중략)

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.11a
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• pp.432-433
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• 2000

고농도의 오존은 인간이나 동물의 건강뿐이 아닌 식물 및 토양등에도 중요한 영향을 미치는 것으로 확인되었으며, 따라서 날로 그 관심도가 증대되고 있다. 오존의 효율적 제어를 위해서는 오존 생성 메카니즘을 면밀히 분석하여 어떠한 물질 혹은 물질의 비율이 고농도 오존 발생과 연관성이 있는지에 대한 연구가 선행되어야 한다. 본 연구에서는 광화학 반응모델에서 초기농도 등에 따른 민감도 분석결과를 바탕으로 고농도 오존에 영향을 주는 지표를 살펴보고, 오존농도의 효율적 제어를 위한 기초자료로 활용하고자 한다. (중략)

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.11a
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• pp.249-250
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• 2000

최근에 환경 오염물질의 제거를 위한 코로나 방전기술이 큰 관심을 끌고 있으며 재래적인 기술들을 능가하는 여러 가지 장점으로 인해 강도 높은 연구들이 이루어지고 있다. 전기방전을 이용하는 공정에서 방전특성은 공정의 효율과 에너지 소모량에 중요한 영향을 미친다. 공정에 따라서 요구되는 방전특성이 다르며, 효율적인 공정이 되기 위해서는 최적의 방전특성에 대한 규명이 이루어져야 한다. 방전에 의해 생성되는 플라즈마 상태는 공정의 전기적 변수들, 반응기의 기하학적 형태, 유입기체의 조성 등 다양한 요소들에 의해 영향을 받으므로 특성에 대한 규명이 매우 어렵다. (중략)

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2001.11a
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• pp.375-376
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• 2001

건축 기술의 발달과 도시토지 이용의 효율성을 강화하기 위하여 최근에는 초고층 건물이 많이 건설되고 있다. 이러한 초고층 건물은 에너지 절약적인 설계로 높은 기밀성을 가질 뿐 아니라, 외주부 또한 개폐할 수 없는 유리벽에 의하여 구획되므로 자연 환기에 열악한 특성을 지니고 있다. 초고층 건물의 경우 배기시스템으로 중앙집중배기방식을 채택하고 있으며, 중앙배기시스템은 배기입상덕트의 형상 및 규격, 흡출기의 성능, 그리고 기상조건 등 여러 변수들에 의해 영향을 받는다. (중략)

• Horticultural Science & Technology
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• v.32 no.3
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• pp.318-329
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• 2014

In order to gain insight into the physiological responses of plants to high temperature stress, the effects of temperature on Chinese cabbage (Brassica campestris subsp. napus var. pekinensis cv. Detong) were investigated through analyses of photosynthesis and chlorophyll fluorescence under 3 different temperatures in the temperature gradient tunnel. Growth (leaf length and number of leaves) during the rosette stage was greater at ambient $+4^{\circ}C$ and ambient $+7^{\circ}C$ temperatures than at ambient temperature. Photosynthetic $CO_2$ fixation rates of Chinese cabbage grown under the different temperatures did not differ significantly. However, dark respiration rate was significantly higher in the cabbage that developed under ambient temperature relative to elevated temperature. Furthermore, elevated growth temperature increased transpiration rate and stomatal conductance resulting in an overall decrease of water use efficiency. The chlorophyll a fluorescence transient was also considerably affected by high temperature stress; the fluorescence yield $F_J$, $F_I$, and $F_P$ decreased considerably at ambient $+4^{\circ}C$ and ambient $+7^{\circ}C$ temperatures, with induction of $F_K$ and decrease of $F_V/F_O$. The values of RC/CS, ABS/CS, TRo/CS, and ETo/CS decreased considerably, while DIo/CS increased with increased growth temperature. The symptoms of soft-rot disease were observed in the inner part of the cabbage heads after 7, 9, and/or 10 weeks of cultivation at ambient $+4^{\circ}C$ and ambient $+7^{\circ}C$ temperatures, but not in the cabbage heads growing at ambient temperature. These results show that Chinese cabbage could be negatively affected by high temperature under a future climate change scenario. Therefore, to maintain the high productivity and quality of Chinese cabbage, it may be necessary to develop new high temperature tolerant cultivars or to markedly improve cropping systems. In addition, it would be possible to use the non-invasive fluorescence parameters $F_O$, $F_V/F_M$, and $F_V/F_O$, as well as $F_K$, $M_O$, $S_M$, RC/CS, ETo/CS, $PI_{abs}$, and $SFI_{abs}$ (which were selected in this study), to quantitatively determine the physiological status of plants in response to high temperature stresses.

• Fire Science and Engineering
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• v.32 no.3
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• pp.76-87
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• 2018

This study describes the damage effects modeling for a quantitative prediction about the hazardous distances from pressurized chlorine saturated liquid tank, which has two-phase leakage. The heavy gas, chlorine is an accidental substance that is used as a raw material and intermediate in chemical plants. Based on the evaluation method for damage prediction and accident effects assessment models, the operating conditions were set as the standard conditions to reveal the optimal variables on an accident due to the leakage of a liquid chlorine storage vessel. A model of the atmospheric diffusion model, ALOHA (V5.4.4) developed by USEPA and NOAA, which is used for a risk assessment of Off-site Risk Assessment (ORA), was used. The Yeosu National Industrial Complex is designated as a model site, which manufactures and handles large quantities of chemical substances. Weather-related variables and process variables for each scenario need to be modelled to derive the characteristics of leakage accidents. The estimated levels of concern (LOC) were calculated based on the Gaussian diffusion model. As a result of ALOHA modeling, the hazardous distance due to chlorine diffusion increased with increasing air temperature and the wind speed decreased and the atmospheric stability was stabilized.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.26-36
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• 2021

In this study, a deep learning model for predicting the queue length was developed using the information collected from the image detector. Then, a multiple regression analysis model, a statistical technique, was derived and compared using two indices of mean absolute error(MAE) and root mean square error(RMSE). From the results of multiple regression analysis, time, day of the week, occupancy, and bus traffic were found to be statistically significant variables. Occupancy showed the most strong impact on the queue length among the variables. For the optimal deep learning model, 4 hidden layers and 6 lookback were determined, and MAE and RMSE were 6.34 and 8.99. As a result of evaluating the two models, the MAE of the multiple regression model and the deep learning model were 13.65 and 6.44, respectively, and the RMSE were 19.10 and 9.11, respectively. The deep learning model reduced the MAE by 52.8% and the RMSE by 52.3% compared to the multiple regression model.

• Journal of the Korean Regional Science Association
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• v.39 no.4
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• pp.111-125
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• 2023

China's rapid economic growth and accelerated urbanization have significantly increased labor force mobility. The choice of city residence and long-term residence intentions of migrants will have significant consequences on both economic output and societal dynamics. This paper investigates the impact of air quality on the long-term residence intention of migrants. By matching an individual-level survey on willingness to stay in the long run and the city-level air quality, represented by PM2.5, we find that the willingness to stay in the long run increases with better air quality. This implies that public policy to maintain good air quality is crucial in keeping those who moved to the city.

• Journal of Korea Water Resources Association
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• v.36 no.3 s.134
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• pp.345-363
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• 2003

From the General Circulation Models(GCMs), it is known that the increases of concentrations of greenhouse gases will have significant implications for climate change in global and regional scales. The GCM has an uncertainty in analyzing the meteorologic processes at individual sites and so the 'downscaling' techniques are used to bridge the spatial and temporal resolution gaps between what, at present, climate modellers can provide and what impact assessors require. This paper describes a method for assessing local climate change impacts using a robust statistical downscaling technique. The method facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future regional climate forcing. The construction of climate change scenarios based on spatial regression(transfer function) downscaling and on the use of a local stochastic weather generator is described. Regression downscaling translates the GCM grid-box predictions with coarse resolution of climate change to site-specific values and the values were then used to perturb the parameters of the stochastic weather generator in order to simulate site-specific daily weather values. In this study, the global climate change scenarios are constructed using the YONU GCM control run and transient experiments.