• 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 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.

• Atmosphere
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• v.30 no.2
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• pp.169-181
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• 2020

We conducted a study on the impact of observation station density; this was done in order to enable the accurate estimation of spatial meteorological variables. The purpose of this study is to help operate an efficient observation network by examining distributions of temperature, relative humidity, and wind speed in a test area of a three-dimensional meteorological observation project in the Yeongdong region in 2019. For our analysis, we grouped the observation stations as follows: 41 stations (for Step 4), 34 stations (for Step 3), 17 stations (for Step 2), and 10 stations (for Step 1). Grid values were interpolated using the kriging method. We compared the spatial accuracy of the estimated meteorological grid by using station density. The effect of increased observation network density varied and was dependent on meteorological variables and weather conditions. The temperature is sufficient for the current weather observation network (featuring an average distance about 9.30 km between stations), and the relative humidity is sufficient when the average distance between stations is about 5.04 km. However, it is recommended that all observation networks, with an average distance of approximately 4.59 km between stations, be utilized for monitoring wind speed. In addition, this also enables the operation of an effective observation network through the classification of outliers.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.171-171
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• 2017

전산유체역학(CFD: Computational Fluid Dynamics)를 이용한 스마트팜 환경 내부의 정밀 제어 연구가 진행 중이다. 시계열 데이터의 난해한 동적 해석을 극복하기위해, 비선형 모델링 기법의 일종인 인공신경망을 이용하는 방안을 고려하였다. 선행 연구를 통하여 환경 데이터의 비선형 모델링을 위한 Tensorflow활용 방법이 하드웨어 가속 기능을 바탕으로 월등한 성능을 보임을 확인하였다. 그럼에도 오프라인 일괄(Offline batch)처리 방식의 한계가 있는 인공신경망 모델링 기법과 현장 보급이 불가능한 고성능 하드웨어 연산 장치에 대한 대안 마련이 필요하다고 판단되었다. CFD 해석을 위한 Solver로 SU2(http://su2.stanford.edu)를 이용하였다. 운영 체제 및 컴파일러는 1) Mac OS X Sierra 10.12.2 Apple LLVM version 8.0.0 (clang-800.0.38), 2) Windows 10 x64: Intel C++ Compiler version 16.0, update 2, 3) Linux (Ubuntu 16.04 x64): g++ 5.4.0, 4) Clustered Linux (Ubuntu 16.04 x32): MPICC 3.3.a2를 선정하였다. 4번째 개발환경인 병렬 시스템의 경우 하드웨어 가속는 OpenCL(https://www.khronos.org/opencl/) 엔진을 이용하고 저전력 ARM 프로세서의 일종인 옥타코어 Samsung Exynos5422 칩을 장착한 ODROID-XU4(Hardkernel, AnYang, Korea) SBC(Single Board Computer)를 32식 병렬 구성하였다. 분산 컴퓨팅을 위한 환경은 Gbit 로컬 네트워크 기반 NFS(Network File System)과 MPICH(http://www.mpich.org/)로 구성하였다. 공간 분해능을 계측 주기보다 작게 분할할 경우 발생하는 미지의 바운더리 정보를 정의하기 위하여 3차원 Kriging Spatial Interpolation Method를 실험적으로 적용하였다. 한편 병렬 시스템 구성이 불가능한 1,2,3번 환경의 경우 내부적으로 이미 존재하는 멀티코어를 활용하고자 OpenMP(http://www.openmp.org/) 라이브러리를 활용하였다. 64비트 병렬 8코어로 동작하는 1,2,3번 운영환경의 경우 32비트 병렬 128코어로 동작하는 환경에 비하여 근소하게 2배 내외로 연산 속도가 빨랐다. 실시간 CFD 수행을 위한 분산 컴퓨팅 기술이 프로세서의 속도 및 운영체제의 정보 분배 능력에 따라 결정된다고 판단할 수 있었다. 이를 검증하기 위하여 4번 개발환경에서 운영체제를 64비트로 개선하여 5번째 환경을 구성하여 검증하였다. 상반되는 결과로 64비트 72코어로 동작하는 분산 컴퓨팅 환경에서 단일 프로세서 기반 멀티 코어(1,2,3번) 환경보다 보다 2.5배 내외 연산속도 향상이 있었다. ARM 프로세서용 64비트 운영체제의 완성도가 낮은 시점에서 추후 성공적인 실시간 CFD 모델링을 위한 지속적인 검토가 필요하다.