• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.37-37
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• 2010

2008년 동아시아 대륙에서 발생기원이 다른 황사와 인위적 오염입자의 광역적 이동 사례를 NOAA위성 RGB 합성영상과 지상 TSP, PM10, PM2.5 질량농도 관측으로 구별하였다. 또한 Terra/Aqua 위성MODIS (MODerate Imaging Spectroradiometer) 센서의AOD (Aerosol Optical Depth)와 FW (Fine aerosol Weighting)를 통해 동아시아 지역에서 발생기원이 다른 대기 에어로졸의 분포와 입자 크기 특성을 분석하였다. 중국 북부와 몽골, 그리고 중국 황토고원에서 모래폭풍이 발생하여 광역적으로 이동하여 청원에 먼지입자(황사)로 영향을 주는 6 사례를 분석했다. 질량농도 TSP중 PM10 은 70%, PM2.5 는 16% 로 조대입자 (> $2.5{\mu}m$)의 비율이 큰 것은 사막과 반사막의 자연적 발생원에서 생성되었기 때문이다. 그러나, 모래 폭풍이 이동 과정에서 중국 동부의 산업 지역을 거쳐 유입 되는 사례에서는 TSP 중 PM2.5 가 23% 까지 증가하기도 했다. 중국 동부로부터 황해를 거쳐 한반도로 유입하고 있는 다른5사례는 TSP 중 PM10, PM2.5가 각각 82%, 65% 로 PM2.5 의 비율이 높았는데 인위적 오염입자의 영향 때문이다. 동아시아 지역에서 인위적 오염입자의 광역적 이동 사례에 대한 평균 AOD는 $0.42{\pm}0.17$로 황사에 의한 AOD ($0.36{\pm}0.13$)와 비교하여 대기 에어로졸에 대한 비율이 높게 나타났다. 특히, 중국 동부에서 황해, 한반도, 동해에 이르는 광역적 지역에 높은 AOD값이 분포했다. 인위적 오염입자의 사례는 FW가 평균 $0.63{\pm}0.16$로 모래폭풍의 이동 사례의 $0.52{\pm}0.13$ 보다 높은 값을 보이고 있어, 대기 에어로졸에 대한 인위적 미세 오염입자의 기여가 크게 나타나고 있었다.

• Asian Journal of Atmospheric Environment
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• v.12 no.2
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• pp.165-177
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• 2018

Particulate matter (PM) measurements are important in air quality, public health, epidemiological studies and decision making for short and long-term policies implementation. However, only few cities in the word have advance air quality-monitoring networks able to provide reliable information of PM leaves in the ambient air, trends and extent of the pollution. In Colombia, only major cities measure PM concentrations. Available measurements from Bogota, Medellin and Bucaramanga show that PM concentration are well above World Health Organization guidelines, but up to now levels and trends of PM in other cities and regions of the country are not well known. Satellite measurements serve as an alternative approach to study air quality in regions were surface measurements are not available. The aim of this study is to perform a spatial and temporal assessment of PM in the ambient air of Colombia. We used Aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite of NASA and surface measurements from the air quality networks of Bogota, Medellin and Bucaramanga. In a first step, we estimated the correlation between MODIS-AOD and monthly average surface measurements (2000 to 2015) from these three cities, obtaining correlation coefficient R values over 0.4 for the cities under study. After, we used AOD and $PM_{10}$ measurements to study the temporal evolution of PM in different cities and regions. Finally, we used AOD measurements to identify cities and regions with the highest AOD levels in Colombia. All the methods presented in this paper may serve as an example for other countries or regions to identify and prioritize locations that require the implementation of more accurate air quality measurements.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2002.11a
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• pp.324-325
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• 2002

대기중의 에어로졸은 시정악화나 호흡기 질환의 원인이 되는 오염물질중의 하나이다. 또한, 지구의 복사수지와 관련하여 기후변화에도 영향을 미치는 물질이다. 이러한 대기중의 에어로졸에 관한 연구에 라이다를 이용한 관측기술이 활용되면서부터 기존의 한계를 극복하고, 에어로졸의 연직분포에 관한 연구가 가능하게 되었다. 라이다는 일정한 파장의 레이저를 투과하여 대기중의 에어로졸이나 기체에 dlk여 산란되어 반사되어 오는 빛을 측정하는 기기로, 이 측정자료를 분석함으로써 대기중의 물질의 분포를 알수 있다. (중략)

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.629-637
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• 2016

In this present study, we identified the $SO_2$ effect on $O_3$ retrieval from the Ozone Monitoring Instrument (OMI) measurement over Chinese Industrial region from 2005 through 2007. The Planetary boundary layer (PBL) $SO_2$ data measured by OMI sensor is used in this present study. OMI-Total Ozone Mapping Spectrometer (TOMS) total $O_3$ is compared with OMI-Differential Optical Absorption Spectrometer (DOAS) total $O_3$ in various $SO_2$ condition in PBL. The difference between OMI-TOMS and OMI-DOAS total $O_3$ (T-D) shows dependency on $SO_2$ (R (Correlation coefficient) = 0.36). Since aerosol has been reported to cause uncertainty of both OMI-TOMS and OMI-DOAS total $O_3$ retrieval, the aerosol effect on relationship between PBL $SO_2$ and T-D is investigated with changing Aerosol Optical Depth (AOD). There is negligible aerosol effect on the relationship showing similar slope ($1.83{\leq}slope{\leq}2.36$) between PBL $SO_2$ and T-D in various AOD conditions. We also found that the rate of change in T-D per 1.0 DU change in PBL, middle troposphere (TRM), and upper troposphere and stratosphere (STL) are 1.6 DU, 3.9 DU and 4.9 DU, respectively. It shows that the altitude where $SO_2$ exist can affect the value of T-D, which could be due to reduced absolute radiance sensitivity in the boundary layer at 317.5 nm which is used to retrieve OMI-TOMS ozone in boundary layer.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.289-298
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• 2019

In this present study, the effects of Signal to Noise Ratio (SNR), Full Width Half Maximum (FWHM), Aerosol Optical Depth (AOD), $O_3$ Vertical Column Density ($O_3$ VCD), and Solar Zenith Angle (SZA) on the accuracy of sulfur dioxide Vertical Column Density ($SO_2$ VCD) retrieval have been quantified using the Differential Optical Absorption Spectroscopy (DOAS) method with the ground-based direct-sun synthetic radiances. The synthetic radiances produced based on the Beer-Lambert-Bouguer law without consideration of the diffuse effect. In the SNR condition of 650 (1300) with FWHM = 0.6 nm, AOD = 0.2, $O_3$ VCD = 300 DU, and $SZA=30^{\circ}$, the Absolute Percentage Difference (APD) between the true $SO_2$ VCD values and those retrieved ranges from 80% (28%) to 16% (5%) for the $SO_2$ VCD of $8.1{\times}10^{15}$ and $2.7{\times}10^{16}molecules\;cm^{-2}$, respectively. For an FWHM of 0.2 nm (1.0 nm) with the $SO_2$ VCD values equal to or greater than $2.7{\times}10^{16}molecules\;cm^{-2}$, the APD ranges from 6.4% (29%) to 6.2% (10%). Additionally, when FWHM, SZA, AOD, and $O_3$ VCD values increase, APDs tend to be large. On the other hand, SNR values increase, APDs are found to decrease. Eventually, it is revealed that the effects of FWHM and SZA on $SO_2$ VCD retrieval accuracy are larger than those of $O_3$ VCD and AOD. The SZA effects on the reduction of $SO_2$ VCD retrieval accuracy is found to be dominant over the that of FWHM for the condition of $SO_2$ VCD larger than $2.7{\times}10^{16}molecules\;cm^{-2}$.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.370-373
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• 2005

We have developed Hybrid algorithm for yellow sand detection. Hybrid algorithm is composed of three methods using infrared bands. The first method used the differential absorption in brightness temperature difference between $11\mu m\;and\;12\mu m$ (BID _1), through which help distinguish the yellow sand from various meteorological clouds. The second method uses the brightness temperature difference between $3.7\mu m\;and\;11\mu m$ (BID_2). The technique would be most sensitive to dust loading during the day when the BID _2 is enhanced by reflection of $3.7\mu m$ solar radiation. The third one is a newly developed algorithm from our research, the so-called surface temperature variation method (STY). We have applied the three methods to MODIS for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. PCI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between PCI and MODIS aerosols optical depth (AOD) shows remarkable good correlations during daytime and relatively good correlations over the land.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1891-1900
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• 2021

Fine particulate matter (FPM; diameter ≤ 2.5 ㎛) is frequently found in metropolitan areas due to activities associated with rapid urbanization and population growth. Many adolescents spend a substantial amount of time at school where, for various reasons, FPM generated outdoors may flow into indoor areas. The aims of this study were to estimate FPM concentrations and categorize types of FPM in schools. Meteorological and chemical variables as well as satellite-based aerosol optical depth were analyzed as input data in a random forest model, which applied 10-fold cross validation and a grid-search method, to estimate school FPM concentrations, with four statistical indicators used to evaluate accuracy. Loose and strict standards were established to categorize types of FPM in schools. Under the former classification scheme, FPM in most schools was classified as type 2 or 3, whereas under strict standards, school FPM was mostly classified as type 3 or 4.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1191-1205
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• 2022

Particulate matter(PM) among air pollutants with complex and widespread causes is classified according to particle size. Among them, PM_2.5 is very small in size and can cause diseases in the human respiratory tract or cardiovascular system if inhaled by humans. In order to prepare for these risks, state-centered management and preventable monitoring and forecasting are important. This study tried to predict PM_2.5 in Seoul, where high concentrations of fine dust occur frequently, using two ensemble models, random forest (RF) and extreme gradient boosting (XGB) using 15 local data assimilation and prediction system (LDAPS) weather-related factors, aerosol optical depth (AOD) and 4 chemical factors as independent variables. Performance evaluation and factor importance evaluation of the two models used for prediction were performed, and seasonal model analysis was also performed. As a result of prediction accuracy, RF showed high prediction accuracy of R² = 0.85 and XGB R² = 0.91, and it was confirmed that XGB was a more suitable model for PM_2.5 prediction than RF. As a result of the seasonal model analysis, it can be said that the prediction performance was good compared to the observed values with high concentrations in spring. In this study, PM_2.5 of Seoul was predicted using various factors, and an ensemble-based PM_2.5 prediction model showing good performance was constructed.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1881-1890
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• 2021

Particulate matter (PM) affects the human, ecosystems, and weather. Motorized vehicles and combustion generate fine particulate matter (PM_2.5), which can contain toxic substances and, therefore, requires systematic management. Consequently, it is important to monitor and predict PM_2.5 concentrations, especially in large cities with dense populations and infrastructures. This study aimed to predict PM_2.5 concentrations in large cities using meteorological and chemical variables as well as satellite-based aerosol optical depth. For PM_2.5 concentrations prediction, a random forest (RF) model showing excellent performance in PM concentrations prediction among machine learning models was selected. Based on the performance indicators R², RMSE, MAE, and MAPE with training accuracies of 0.97, 3.09, 2.18, and 13.31 and testing accuracies of 0.82, 6.03, 4.36, and 25.79 for R², RMSE, MAE, and MAPE, respectively. The variables used in this study showed high correlation to PM_2.5 concentrations. Therefore, we conclude that these variables can be used in a random forest model to generate reliable PM_2.5 concentrations predictions, which can then be used to assess the vulnerability of schools to PM_2.5.