• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.274-279
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• 2012

Daegu Solar Power Tower Plant of 200 kW thermal capacity was developed for the first time in Korea, 2011. Measurement of the heat flux distribution is essential to evaluate the solar energy concentrated by reflectors and to design a suitable receiver. The flux mapping technique, which uses a radiometer and a diffuse plate, is common for measurement of the heat flux distribution. Because the solar power tower plant has a wide concentration area, the flux mapping technique using a fixed diffuse plate is difficult to apply. Therefore, the flux distribution in the solar power tower plant should be measured by the flux mapping technique using a small moving bar. In this study, we measured flux distributions with the moving-bar system developed at the KIER solar furnace and evaluated its applicability for the solar power tower plant.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.6
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• pp.133-142
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• 2012

The objective of this study was to correct the bias of the Representative Concentration Pathways (RCP)-based future precipitation data using a quantile mapping method. This method was adopted to correct extreme values because it was designed to adjust simulated data using probability distribution function. The Generalized Extreme Value (GEV) distribution was used to fit distribution for precipitation data obtained from the Korea Meteorological Administration (KMA). The resolutions of precipitation data was 12.5 km in space and 3-hour in time. As the results of bias correction over the past 30 years (1976~2005), the annual precipitation was increased 16.3 % overall. And the results for 90 years (divided into 2011~2040, 2041~2070, 2071~2100) were that the future annual precipitation were increased 8.8 %, 9.6 %, 11.3 % respectively. It also had stronger correction effects on high value than low value. It was concluded that a quantile mapping appeared a good method of correcting extreme value.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.397-400
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• 2007

For mapping the distribution of heavy metals in the mining area, field spectroscopy and hyperspectral remote sensing were used in this study. Although heavy metals are spectrally featureless from the visible to the short wave infrared range, possible variations in spectral signal due to heavy metals bound onto minerals can be explained with the metal binding reaction onto the mineral surface. Variations in the spectral absorption shapes of lattice OH and oxygen on the mineral surface due to the combination of heavy metals were surveyed over the range from 420 to 2400 nm. Spectral parameters such as peak ratio and peak area were derived and statistically linked to metal concentration levels in the streambed samples collected from the dry stream channels. The spatial relationships between spectral parameters and concentrations of heavy metals were yielded as well. Based on the observation at a ground level for the relationship between spectral signal and metal concentration levels, the spectral parameters were classified in a hyperspectral image and the spatial distribution patterns of classified pixels were compared with the product of analysis at the ground level. The degree of similarity between ground dataset and image dataset was statistically validated. These techniques are expected to support assessment of dispersion of heavy metal contamination and decision on optimal sampling point.

• Journal of Environmental Health Sciences
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• v.27 no.2
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• pp.100-107
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• 2001

On the planning for the systematic management and control of the air pollution display methods were used to evaluate the spatial pollutant concentration status. This study were conducted to investigate the practical using of Geographic Information System(GIS) technology on the air pollution control and management which were computer-based systems that were used to store and manipulate geographic information in the macro city. In this study 137 samples were corrected by passive samplers and analysed by GC/MSD for 16 VOCs in Seoul (25 distincts) distributed by TM-coordinate(2 km$\times$2km), and finally displayed by Arciew program(version 3.2, ESRI Inc, USA) for windows. The concentration of benzene and toluene showed high level in whole area of seoul area of Seoul and distribution of butylbenzen, trothroloetylene, stylen showed high level in whole area of Seoul except a few distincts and the distribution of isopropylbenzene, 1,2-dichroloethane showed higher level in core area than that of Kangnam and Kangbuk area. In conclusion, products of this study of using GIS technology apply on the spatial distribution of VOCs concentration was very effective than that of other methods(e.g., contouring concentration method, pie or column chart, graduated symbols), especially in mapping and symbolization of pollution status evaluation.

• Korean Journal of Remote Sensing
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• v.18 no.3
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• pp.141-146
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• 2002

Chlorophyll-a concentration maps of Yongdam reservoir in September and October, 2001 were produced using Landsat ETM imagery and the in-situ water quality measurement data. In-situ water samples were collected on 16th September and 18th October during the satellite overpass. The correlations between the DN values of the imagery and the values of chlorophyll-a concentration were analyzed. The visible bands(band 1, 2, 3) and the near infrared band(band 4) data of September image showed the correlation coefficient values higher than 0.9. The October image showed correlation coefficient values of about 0.7 due to the low variations of chlorophyll-a concentration. Regression models between the DN values of the Landsat ETM image and the chlorophyll-a concentration have been developed for each image. The developed regression models were then applied to each image, and finally the chlorophyll-a distribution maps of Yongdam reservoir were produced. The produced maps showed the spatial distribution of the chlorophyll-a in Yongdam reservoir in a synoptic way so that the tropic state could be easily monitored and analysed in the spatial domain.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.71-84
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• 2017

Radon, which enters the interior environment from soil, rocks, and groundwater, is a radioactive gas that poses a serious risk to humans. Indoor radon concentrations are measured to investigate the risk of radon gas exposure and reliable radon concentration mapping is then performed for further analysis. In this study, we compared the predictive performance of various univariate kriging algorithms, including ordinary kriging and three nonlinear transform-based kriging algorithms (log-normal, multi-Gaussian, and indicator kriging), for mapping radon concentrations with an asymmetric distribution. To compare and analyze the predictive performance, we carried out jackknife-based validation and analyzed the errors according to the differences in the data intervals and sampling densities. From a case study in South Korea, the overall nonlinear transform-based kriging algorithms showed better predictive performance than ordinary kriging. Among the nonlinear transform-based kriging algorithms, log-normal kriging had the best performance, followed by multi-Gaussian kriging. Ordinary kriging was the best for predicting high values within the spatial pattern. The results from this study are expected to be useful in the selection of kriging algorithms for the spatial prediction of data with an asymmetric distribution.

• Journal of Environmental Science International
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• v.20 no.12
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• pp.1647-1661
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• 2011

The Government had devised legislation of Special Act and drew up guidelines for improving air quality in Seoul Metropolitan area. In 2007 local government of Seoul, Incheon and Gyeonggi conducted the results of application policy by reduced air pollutants emission for the first time. Although there was reduction of air pollutant emission in each local government, it was ineffective as expected using air pollution monitoring database. Therefore we worked out a way to prepare modeling input data using the results of enforcement plan. And we simulated surface $NO_2$ and PM10 before and after decrease in air pollutants emission and examine reduction effects of air pollution according to enforcement regulation except other influence, by using MM5-SMOKE-CMAQ system. Each local government calculated the amount of emission reduction under application policy, and we developed to prepare input data so as to apply to SMOKE system using emission reduction of enforcement plan. Distribution factor of emission reduction were classified into detailed source and fuel codes using code mapping method in order to allocate the decreased emission. The code mapping method also included a way to allocate spatial distribution by CAPSS distribution. According to predicted result using the reduction of NOx emission, $NO_2$ concentration was decreased from 19.1 ppb to 18.0 ppb in Seoul. In Gyeonggi and Incheon $NO^2$ concentrations were down to 0.65 ppb and 0.68 ppb after application of enforcement plan. PM10 concentration was reduced from 18.2 ${\mu}g/m^3$ to 17.5 ${\mu}g/m^3$ in Seoul. In Gyeonggi PM10 concentration was down to 0.51 ${\mu}g/m^3$ and in Incheon PM10 concentration was decreased about 0.47 ${\mu}g/m^3$ which was the lower concentration than any other cities.

• Journal of Environmental Impact Assessment
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• v.10 no.2
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• pp.135-145
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• 2001

This study was conducted to investigate the practical using of Geographic Information System(GIS) technology which are computer-based systems that are used to store and manipulate geographic information on the air pollution control and management in the macro city. For this study 130 samples were corrected by passive sampler in Seoul (25 distincts) distributed by TM-coordinate during November in 1997 to January 1998, and analysed by GC/MSD for 16 VOCs e.g., toluene, benzene and display using Arc/view GIS(version 3.2, ESRI Inc, U.S.A) for windows. The most VOCs concentration distribution in November, 1997 was higher than that of January, 1998 except benzene and 1,1,2-trichroloethan, bromobenzene. And products of the distribution of VOCs concentration display using GIS technology was effective as well as other display methods(e.g., contouring method, pie or column chart, graduated symbols), especially in mapping and symbolization capabilities for spatial pollutant status evaluation were very effective than other display methods.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.73-82
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• 2013

The main objective of this study was to evaluate Representative Concentration Pathways (RCP) scenarios-based flood risk at a Si-Gun level. A bias correction using a quantile mapping method with the Generalized Extreme Value (GEV) distribution was performed to correct future precipitation data provided by the Korea Meteorological Administration (KMA). A series of proxy variables including CN80 (Number of days over 80 mm) and CX3h (Maximum precipitation during 3-hr) etc. were used to carry out flood risk assessment. Indicators were normalized by a Z-score method and weighted by factors estimated by principal component analysis (PCA). Flood risk evaluation was conducted for the four different time periods, i.e. 1990s, 2025s, 2055s, and 2085s, which correspond to 1976~2005, 2011~2040, 2041~2070, and 2071~2100. The average flood risk indices based on RCP4.5 scenario were 0.08, 0.16, 0.22, and 0.13 for the corresponding periods in the order of time, which increased steadily up to 2055s period and decreased. The average indices based on RCP8.5 scenario were 0.08, 0.23, 0.11, and 0.21, which decreased in the 2055s period and then increased again. Considering the average index during entire period of the future, RCP8.5 scenario resulted in greater risk than RCP4.5 scenario.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).