• Title/Summary/Keyword: Atmospheric Environment Information

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Evaluating GHG Emissions Reduced by Real-time Traffic Information in Gasoline Vehicle (실시간교통정보 이용에 따른 가솔린차량의 온실가스 저감효과 평가)

  • Kim, Jun-Hyung;Um, Jung-Sup
    • Journal of Korean Society for Atmospheric Environment
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    • v.27 no.4
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    • pp.443-453
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    • 2011
  • Real-time Traffic Information Service could play a key role in reducing incomplete combustion time remarkably since it can provide traffic information in real-time basis. Emission characteristics of test engines were studied in terms of travel distance and speed. The present study focused on a north district in Daegu, 12 km. The driving for the emission test was done at 8AM, 3PM, 7PM which represents various traffic conditions. The reduced emissions of Greenhouse Gases (GHG) have been measured for a travel distance running at different loads (conventional shortest route and Real-time Traffic Information) and GHG ($CO_2$, $CH_4$, $N_2O$) are all inventoried and calculated in terms of existing emission factors. The emission of GHG has been shown to reduce linearly with travel distance: $CO_2$ (9.15%), $CH_4$ (18.43%), $N_2O$(18.62%).

A Study of the Urban Heat Island in Seoul using Local Analysis System (지역규모 분석 모델을 이용한 서울 도시열섬 특성 연구)

  • Chun, Ji Min;Lee, Seon-Yong;Kim, Kyu Rang;Choi, Young-Jean
    • Journal of Korean Society for Atmospheric Environment
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    • v.30 no.2
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    • pp.119-127
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    • 2014
  • A very high resolution weather analysis system (VHRAS) of 50 m horizontal resolution is established based on LAPS. VHRAS utilizes the 3 hourly forecast data of the Unified Model (UM) of the Korea Meteorological Administration (KMA) with the horizontal resolution of 12 km as initial guess fields. The analysis system ingests the automatic weather station (AWS) data as input observations. The analysis system operates every hour for Seoul, Korea region in real time basis. It takes less than 10 minutes for one analysis cycle. The size of grid of the analysis domain is $800{\times}660$, respectively. The analysis results from December 2010 to February 2011 showed that the mean biases of temperature, maximum and minimum temperature were -0.07, 1.6, $0.2^{\circ}C$, respectively. The temperature in the central part of the city revealed relatively higher value than that of the surrounding mountainous areas, which showed a heat island feature. The heat island appears in zonal direction since the central city region is developed along a large river. Along the heat island, the eastern region was warmer than the western region. The warmer temperature in the western part of the heat island was caused by anthropogenic heat change in conjunction with the change of land use. This system will provide more reliable weather data and information in Seoul.

Development of atmospheric environment information collection system using drone (드론을 이용한 대기환경정보 수집장치 개발 및 응용 연구)

  • Kim, Nam Ho
    • Smart Media Journal
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    • v.7 no.4
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    • pp.44-51
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    • 2018
  • The purpose of this research is to collect atmospheric environmental information at specific altitudes in a range of 0 to 1 km above the surface and to monitor it using drones. The corresponding temperature and humidity were measured with the meteorological factors, and the amounts of fine dust and $CO_2$ were observed by the environmental factors so that they could receive the normal values. Monitoring the status of atmospheric gas emission in specific enterprises, industrial complexes and regions through the measurement is meant to help establish policies to reduce pollution factors. In conventional means previously practiced, exhaust gas detection accompanies a great deal of risks in terms of safety because the surveyor is directly exposed to the source of contamination such as the holes installed in the chimney. However, in our proposed method, the drone can collect information in a wide range under safe circumstances, which can be utilized through wide industrial areas.

Atmospheric Correction of Sentinel-2 Images Using GK2A AOD: A Comparison between FLAASH, Sen2Cor, 6SV1.1, and 6SV2.1 (GK2A AOD를 이용한 Sentinel-2 영상의 대기보정: FLAASH, Sen2Cor, 6SV1.1, 6SV2.1의 비교평가)

  • Kim, Seoyeon;Youn, Youjeong;Jeong, Yemin;Park, Chan-Won;Na, Sang-Il;Ahn, Hoyong;Ryu, Jae-Hyun;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.38 no.5_1
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    • pp.647-660
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    • 2022
  • To prepare an atmospheric correction model suitable for CAS500-4 (Compact Advanced Satellite 500-4), this letter examined an atmospheric correction experiment using Sentinel-2 images having similar spectral characteristics to CAS500-4. Studies to compare the atmospheric correction results depending on different Aerosol Optical Depth (AOD) data are rarely found. We conducted a comparison of Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Sen2Cor, and Second Simulation of the Satellite Signal in the Solar Spectrum - Vector (6SV) version 1.1 and 2.1, using Geo-Kompsat 2A (GK2A) Advanced Meteorological Imager (AMI) and Aerosol Robotic Network (AERONET) AOD data. In this experiment, 6SV2.1 seemed more stable than others when considering the correlation matrices and the output images for each band and Normalized Difference Vegetation Index (NDVI).

Decomposition of Trchloroethylene/Air Mixture by Electron Beam Irradiation in a Flow Reactor (전자빔을 이용한 흐름반응기에서의 Trichloroethylene/Air 분해)

  • ;;;Tatiana Stuchinskaya
    • Journal of Korean Society for Atmospheric Environment
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    • v.17 no.1
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    • pp.97-104
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    • 2001
  • Decomposition of trichloroethlyene(TCE) in electron beam irradiation was examined on order to obtain information on the treatment of VOC in air. Air containing vaporized TCE has been studied in a flow reactor with different reaction environments, at various initial TCE concentration and in the presence and absence of water vapor. Maximum decomposition was observed in oxygen reaction environment and the degree of decomposition was about 99% at 20kGy for 2,000ppm initial TCE. The concentration of TCE exponentially decreased with dose in air and pure oxygen. The effect of water vapor on TCE decomposition efficiency was examined. The decomposition rate of TCE in the presence of water vapor (5,600 ppm) was approximately 10% higher than that in the absence of water vapor. Dichloroacetic acid, dichloroacethyl chloride and dichloroethyl ester acid were identified as primary products of this reaction adn were decomposed and oxidized to yield CO and $CO_2$. Perchloroethylene, hexachloroethane, chloroform and carbon tetrachloride were also observed as highly chlorinat-ed by products.

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On Input Information of Land Surface Model Considered in Field Experiment (야외 관측에서 고려해야 할 지면 모형의 입력 정보에 관하여)

  • Chae, Nam-yi;Hong, Jin-kyu;Kim, Joon
    • Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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    • 2001.06a
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    • pp.8-10
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    • 2001
  • 1. 연구목적 : 지면에서 관측되는 질량 및 에너지 플럭스 등의 검증을 위해 지면 모형이 사용된다. 이러한 지면 모형 중의 하나인, Simple Biosphere model 2(SiB2)의 입력 정보와 모형의 검정을 위해 필요한 자료를 중심으로 야외 관측시 고려할 점을 논의한다.(중략)

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Study on the Sea Level Pressure Prediction of Typhoon Period in South Coast of the Korean Peninsula Using the Neural Networks (신경망 모형을 이용한 태풍시기의 남해안 기압예측 연구)

  • Park, Jong-Kil;Kim, Byung-Soo;Jung, Woo-Sik;Seo, Jang-Won;Shon, Yong-Hee;Lee, Dae-Geun;Kim, Eun-Byul
    • Atmosphere
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    • v.16 no.1
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    • pp.19-31
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    • 2006
  • The purpose of this study is to develop the statistical model to predict sea level pressure of typhoon period in south coast of the Korean Peninsula. Seven typhoons, which struck south coast of the Korean Peninsula, are selected for this study, and the data for analysis include the central pressure and location of typhoon, and sea level pressure and location of 19 observing site. Models employed in this study are the first order regression, the second order regression and the neural network. The dependent variable of each model is a 3-hr interval sea level pressure at each station. The cause variables are the central pressure of typhoon, distance between typhoon center and observing site, and sea level pressure of 3 hrs before, whereas the indicative variable reveals whether it is before or after typhoon passing. The data are classified into two groups - one is the full data obtained during typhoon period and the other is the data that sea level pressure is less than 1000 hPa. The stepwise selection method is used in the regression model while the node number is selected in the neural network by the Schwarz's Bayesian Criterion. The performance of each model is compared in terms of the root-mean square error. It turns out that the neural network shows better performance than other models, and the case using the full data produces similar or better results than the case using the other data.