• Journal of Urban Science
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• v.9 no.2
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• pp.31-44
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• 2020

This study aims to analyze the relations among greenbelt, air pollution empirically in order to assess the environmental effects of the greenbelt in the Seoul metropolitan area, objectively. For this purpose, this study conducts an empirical analysis of impacts of greenbelt on urban air pollution using a multiple-regression model. The major findings are summarized as follows. As a result of an empirical analysis of the impacts of greenbelt on air pollution, it is found that the characteristics of the city have impacts on air pollution concentration. It is found that the population and employment are the causes of increases in CO and NO2 concentrations, and the number of employees in the manufacturers has impacts on increases of O3 and SO2, while power plants have impacts on PM10, CO and NO2. Intersections have impacts on O3 and SO2, while the areas of the roads have impacts on CO and NO2. In addition, as for the spatial distribution of air pollutants, it is found that CO and NO2 concentrations are relatively higher in the center of the Seoul metropolitan area, while PM10, O3 and SO2 concentrations are relatively higher in the suburbs. It is found that air pollution concentration is low in greenbelt zone. In the greenbelt zone, PM10, CO and SO2 concentrations are low.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.379-394
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• 2021

Air pollution data in South Korea is provided on a real-time basis by Air Korea stations since 2005. Previous studies have shown the feasibility of gridding air pollution data, but they were confined to a few cities. This paper examines the creation of nationwide gridded maps for PM10 concentration using 333 Air Korea stations with variogram optimization and ordinary kriging. The accuracy of the spatial interpolation was evaluated by various sampling schemes to avoid a too dense or too sparse distribution of the validation points. Using the 114,745 matchups, a four-round blind test was conducted by extracting random validation points for every 365 days in 2019. The overall accuracy was stably high with the MAE of 5.697 ㎍/m³ and the CC of 0.947. Approximately 1,500 cases for high PM10 concentration also showed a result with the MAE of about 12 ㎍/m³ and the CC over 0.87, which means that the proposed method was effective and applicable to various situations. The gridded maps for daily PM10 concentration at the resolution of 0.05° also showed a reasonable spatial distribution, which can be used as an input variable for a gridded prediction of tomorrow's PM10 concentration.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.23-33
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• 2011

In this study, the data that examined the chloride ion concentration of ground water wells in the west coast of Jeollabukdo applying the GIS spatial estimation method were analyzed. In particular, through the designation of a validation point among ground water wells and then the analysis of error characteristics of the chloride ion concentration by each method of IDW (Inverse Distance Weight), Spline, and Kriging Interpolation method which is proper for estimating salt water intrusion was selected. The main conclusion from this study is as follows. First, as a result of analyzing the error characteristics of various spatial estimation methods by using the data from the chloride ion concentration of 485 ground water wells, the IDW method was found to be the most appropriate for estimating chloride ion concentration by salt water intrusion. Second, analyzing the average chloride ion concentration of the targeted regions has revealed that Gunsan-si with the record of $541mg/{\ell}$ did not meet water quality standards even for industrial use. Both Gimje-si and Gochang-gun satisfied drinking water quality standards and Buan-gun with $272mg/{\ell}$ was slightly below the standards for drinking water. Third, concerning the results of analysis according to administrative districts, as the areas adjacent to the west coast such as Daemyeong-dong, Joong-dong, Jangjae-dong and Guemam-dong in Gunsan-si are found to have very high chloride ion concentration, and both Hoehyeon-myeon and Daeya-myeon bounded by the Mankeong river did not meet water quality standards even for industrial use. From these facts, it is concluded that salt water intrusion has a great effect on Gunsan-si generally.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.434-439
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• 2019

Recently, interest in urban temperature change and ground surface temperature change has been increasing due to weather phenomenon due to global warming, heat island phenomenon caused by urbanization in urban areas. In Korea, weather data such as temperature and precipitation have been collected since 1904. In recent years, there are 96 ASOS stations and 494 AWS weather observation stations. However, in the case of terrestrial networks, terrestrial meteorological data except measurement points are predicted through interpolation because they provide point data for each installation point. In this study, to improve the resolution of ground surface temperature measurement, the surface temperature using satellite image was calculated and its applicability was analyzed. For this purpose, the satellite images of Landsat 8 OLI TIRS were obtained for Seoul Metropolitan City by seasons and transformed to surface temperature by applying NASA equation to the thermal bands. The ground measurement data was based on the temperature data measured by AWS. Since the AWS temperature data is station based point data, interpolation is performed by Kriging interpolation method for comparison with Landsat image. As a result of comparing the satellite image base surface temperature with the AWS temperature data, the temperature difference according to the season was calculated as fall, winter, summer, based on the RMSE value, Spring, in order of applicability of Landsat satellite image. The use of that attribute and AWS support starts at $2.11^{\circ}C$ and RMSE ${\pm}3.84^{\circ}C$, which reflects information from the extended NASA.

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.3
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• pp.195-206
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• 2014

In this study, soil contamination maps related to Cu and Pb were created at the Busan abandoned mine in Korea using a handheld X-Ray Fluorescence(XRF) and Geographic Information Systems(GIS). Hydrological analysis was performed using the Digital Elevation Model(DEM) of the study area to identify the flow directions of surface runoff where pollutants can be dispersed from the soil contamination sources. 24 locations for measuring the soil contamination related to Cu and Pb were selected by considering the result of hydrological analysis. The results measured at the 24 locations using the handheld XRF showed that the highest value of Cu contamination is 8,255ppm and that of Pb is 2,146ppm. The field investigation data were entered into ArcGIS software, and then soil contamination maps regarding Cu and Pb with a 5m grid-spacing were created after performing spatial interpolations using the ordinary kriging method. As a result, we could know that high concentrations of Cu and Pb are presented at the waste and tailings dumps around the abandoned mine openings. This study also showed that the handheld XRF and GIS can be utilized to create soil contamination maps related to Cu and Pb in the field.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.3-13
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• 2012

This study evaluated the feasibility of the location for PM-10 Monitoring Stations utilizing through GIS analysis. In addition, optimal sites were investigated to properly manage PM-10 which are closely related with public health. There are 11 PM-10 monitoring stations in Daegu area and the PM-10 data monitored at these stations are utilized to understand the overall status of PM-10 pollution. However, there are contrastive issues on the locations of current monitoring stations. Thus, this study prepared the map of PM-10 concentrations in Daegu area using IDW and Kriging techniques. Furthermore, average PM-10 concentrations were calculated using zonal statistical methods according to legal divisions and then, the current monitoring stations were evaluated whether their location is appropriate or not for PM-10 pollution distribution. It was found that, on the basis of yearly, seasonal and daily concentration analysis, the location of current PM-10 monitoring stations were not appropriate, particularly as they could not represent regional PM-10 pollution characteristics. In order to supplement this deficiency, seven sites(Namsandong, Namildong, Dongildong, Buksungro 1, Jongro 1, Hyangchondong and Haejeondong) commonly selected from each analytical step are suggested as additional PM-10 monitoring sites. It is further suggested that this kind of scientific evaluation for the location of PM-10 monitoring stations are needed in order to properly manage public heath in other cities as well as Daegu area.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.4_1
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• pp.107-119
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• 1992

A heavy storm hit the central part of the Korean Peninsula especially on the Chungju Dam Basin from the 9th to 12th of September 1990. The Chungju multipurpose dam is the largest water project in Korea completed in 1986. The storm recorded a peak inflow of about $21,000m^3/sec$ at the dam site which is equivalent to 500 to 1000 years recurring frequency according to the designed concept. Extensive hydrological analyses including field investigation were performed to identify the storm. The result of the field investigation showed that 6 gages among the 22 telemetering rain-gages located in the basin were proved to be out-of-normal operation during the storm. The corrected basin average rainfall was estimated to be 458.6 mm ranging from 206 to 665 mm. The correction of the rainfall depth included the adjustment of the rainfall depths of the 6 gages using the Kriging interpolation technique, and adjustment according to the heights of the gage mouths. For the maintenance and operation of the Chungju Dam, new design floods were suggested from the trend analysis which showed that the design flood have to be increased because of the increasing tendency of the annual flood peaks.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.1
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• pp.101-119
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• 2018

In this study, we analyzed long-term measurements and air quality simulation results of four criteria air pollutants ($PM_{10}$, $O_3$, $NO_2$, and $SO_2$) for 10 years, from 2006 to 2015, with emphasis on trends of annual variabilities. With the observation data, we conducted spatial interpolation using the Kriging method to estimate spatial distribution of pollutant concentrations. We also performed air quality simulations using the CMAQ model to consider the nonlinearity of the secondary air pollutants such as $O_3$ and the influence of long-range transport. In addition, these simulations are used to deduce the effect of long-term meteorological variations on trends of air quality changes because we fixed the emissions inventory while changing meteorological inputs. The nation-wide inter-annual variability of modeled $PM_{10}$ concentrations was $-0.11{\mu}g/m^3/yr$, while that of observed concentrations was $-0.84{\mu}g/m^3/yr$. For the Seoul Metropolitan Area, the inter-annual variability of observed $PM_{10}$ concentrations was $-1.64{\mu}g/m^3/yr$ that is two times rapid improvement compared to other regions. On the other hand, the inter-annual variability of observed $O_3$ concentrations is 0.62 ppb/yr which is larger than the simulated result of 0.13 ppb/yr. Magnitudes of differences between the modeled and observed inter-annual variabilities indicated that decreasing trend of $PM_{10}$ and increasing trend of $O_3$ are more influenced by emissions and oxidation states than meteorological conditions. We also found similar patterns in $NO_2$. However, $NO_2$ trends showed greater regional and seasonal differences than other pollutants. The analytic approach used in this study can be applicable to estimate changes in factors determining air quality such as emissions, weather, and surrounding conditions over a long term. Then analysis results can be used as important data for air quality management planning and evaluation of the chronic impact of air quality.