• Journal of Korean Society for Atmospheric Environment
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• v.33 no.2
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• pp.97-107
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• 2017

Columnar aerosol properties retrieved from solar radiation were investigated at the Yongin (YGN) SKYNET site over seven years from October 2008 to October 2015. Hourly averages were calculated when the data were available, and back trajectories were calculated to examine the effects of regional transport. Data recovery rate was low at 6.6%, primarily because solar radiation was measured only under daytime clear-sky conditions. Mean values of the fine-mode volume fraction (FMVF) as well as its seasonal variation were similar to those of $PM_{2.5}/PM_{10}$ although the coarse-mode fraction of column aerosols tended to be slightly larger. The values of single scattering albedo (SSA) and FMVF were lower in spring due to the effects of mineral dust, and higher in summer due to secondarily-formed inorganic ions. Back trajectories were grouped into five clusters according to the directions of transport paths. Aerosol loading was highest for Cluster 2 from the northwest, but SSA and FMVF were not particularly high or low because aerosols were composed of various materials with different properties. Aerosol loading was lowest for Cluster 5 from the Pacific Ocean passing through the south end of Japan, whose SSA and FMVF were highest as secondarily-formed inorganic ions were mixed.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.777-788
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• 2021

Since aerosol has a relatively short duration and significant spatial variation, satellite observations become more important for the spatially and temporally continuous quantification of aerosol. However, optical remote sensing has the disadvantage that it cannot detect AOD (Aerosol Optical Depth) for the regions covered by clouds or the regions with extremely high concentrations. Such missing values can increase the data uncertainty in the analyses of the Earth's environment. This paper presents a spatial gap-filling framework using a univariate statistical method such as DCT-PLS (Discrete Cosine Transform-based Penalized Least Square Regression) and FMM (Fast Matching Method) inpainting. We conducted a feasibility test for the hourly AOD product from AHI (Advanced Himawari Imager) between January 1 and December 31, 2019, and compared the accuracy statistics of the two spatial gap-filling methods. When the null-pixel area is not very large (null-pixel ratio < 0.6), the validation statistics of DCT-PLS and FMM techniques showed high accuracy of CC=0.988 (MAE=0.020) and CC=0.980 (MAE=0.028), respectively. Together with the AI-based gap-filling method using extra explanatory variables, the DCT-PLS and FMM techniques can be tested for the low-resolution images from the AMI (Advanced Meteorological Imager) of GK2A (Geostationary Korea Multi-purpose Satellite 2A), GEMS (Geostationary Environment Monitoring Spectrometer) and GOCI2 (Geostationary Ocean Color Imager) of GK2B (Geostationary Korea Multi-purpose Satellite 2B) and the high-resolution images from the CAS500 (Compact Advanced Satellite) series soon.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.12
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• pp.1027-1035
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• 2021

We presents a design of 3U cubesat MIMAN (Monochrome imaging for monitoring aerosol by nano-satellite) for aerosol monitoring mission with high spatial resolution. The main objective of MIMAN mission is to take images of aerosols around Korea and to provide auxiliary data for GK 2B cloud masking. For this mission, we derived mission requirements and constraints for the MIMAN mission. We designed the mission architecture and concept of operations. To reduce risk factors in space operation, we considered the safety of the communication. In every operation modes, UHF communication is available so that the cubesat can operate based on the ground commands. So, we can handle every problem at the ground station during mission operations. Based on the mission and concept of operations, we confirmed that the system design satisfied the system requirements. We designed the system interface considering data flow of each hardware, and evaluated the safety of the system with system budget analysis.

• Atmosphere
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• v.33 no.3
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• pp.319-330
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• 2023

As the need for PM type observation increases, Surface Particulate Matter Network (SPARTAN), PM samplers analyzes aerosol samples for PM mass concentration and chemical composition, were recently installed at two sites: Yonsei University at Seoul and Ulsan Institute of Science and Technology (UNIST) at Ulsan. These SPARTAN filter samplers and nephelometers provide the PM_2.5 mass concentration and chemical speciation data with aerosol type information. We introduced the overall information and installation of SPARTAN at the field site in this study. After installation and observation, both Seoul and Ulsan sites showed a similar time series pattern with the daily PM_2.5 mass concentration of SPARTAN and the data of Airkorea. In particular, in the case of high concentrations of fine particles, daily average value of PM_2.5 was relatively well-matched. During the Yonsei University observation period, high concentrations were displayed in the order of sulfate, black carbon (BC), ammonium, and calcium ions on most measurement days. The case in which the concentration of nitrate ions showed significant value was confirmed as the period during which the fine dust alert was issued. From the data analysis, SPARTAN data can be analyzed in conjunction with the existing urban monitoring network, and it is expected to have a synergetic effect in the research field. Additionally, the possibility of being analyzed with optical data such as AERONET is presented. In addition, the method of installing and operating SPARTAN has been described in detail, which is expected to help set the stage for the observation system in the future.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.861-870
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• 2021

In this study, fused Aerosol Optical Depth (AOD) data were produced using AOD products from the Geostationary Ocean Color Imager (GOCI) onboard Communication, Oceanography and Meteorology Satellite (COMS)satellite and the Advanced Himawari Imager (AHI) onboard Himawari-8. Since the spatial resolution and the coordinate system between the satellite sensors are different, a preprocessing was first preceded. After that, using the level 1.5 AOD dataset of AErosol RObotic NETwork (AERONET), which is ground-based observation, correlations and trends between each satellite AOD and AERONET AOD were utilized to produce more accurate satellite AOD data than the originalsatellite AODs. The fused AOD were found to be more accurate than the originalsatellite AODs. Root Mean Square Error (RMSE) and mean bias of the fused AODs were calculated to be 0.13 and 0.05, respectively. We also compared errors of the fused AODs against those of the original GOCI AOD (RMSE: 0.15, mean bias: 0.11) and the original AHI AOD (RMSE: 0.15, mean bias: 0.05). It was confirmed that the fused AODs have betterspatial coverage than the original AODsin areas where there are no observations due to the presence of cloud from a single satellite.

• 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.

• Korean Journal of Remote Sensing
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• v.13 no.2
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• pp.99-120
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• 1997

Solar radiation is scattered and absorbed atmospheric compositions in the atmosphere before it reaches the surface and, then after reflected at the surface, until it reaches the satellite sensor. Therefore, consideration of the radiative transfer through the atmosphere is essential for the quantitave analysis of the satellite sensed data, specially at shortwave region. This study examined a feasibility of using radiative transfer code for estimating the atmospheric effects on satellite remote sensing data. To do this, the flux simulated by LOWTRAN7 is compared with CAGEX data in shortwave region. The CAGEX (CERES/ARM/GEWEX Experiment) data provides a dataset of (1) atmospheric soundings, aerosol optical depth and albedo, (2) ARM(Aerosol Radiation Measurement) radiation flux measured by pyrgeometers, pyrheliometer and shadow pyranometer and (3) broadband shortwave flux simulated by Fu-Liou's radiative transfer code. To simulate aerosol effect using the radiative transfer model, the aerosol optical characteristics were extracted from observed aerosol column optical depth, Spinhirne's experimental vertical distribution of scattering coefficient and D'Almeida's statistical atmospheric aerosols radiative characteristics. Simulation of LOWTRAN7 are performed on 31 sample of completely clear days. LOWTRAN's result and CAGEX data are compared on upward, downward direct, downward diffuse solar flux at the surface and upward solar flux at the top of the atmosphere(TOA). The standard errors in LOWTRAN7 simulation of the above components are within 5% except for the downward diffuse solar flux at the surface(6.9%). The results show that a large part of error in LOWTRAN7 flux simulation appeared in the diffuse component due to scattering mainly by atmispheric aerosol. For improving the accuracy of radiative transfer simulation by model, there is a need to provide better information about the radiative charateristrics of atmospheric aerosols.

• Asian Journal of Atmospheric Environment
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• v.9 no.1
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• pp.78-85
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• 2015

In the past several decades, biofuels have emerged as candidates to help mitigate the issues of global warming, fossil fuel depletion and, in some cases, atmospheric pollution. To date, the only biofuels that have achieved any significant penetration in the global transportation sector are ethanol and biodiesel. The global consumption of biodiesel was rapidly increased from 2005. The goal of this study was to examine the chemical composition on particulate pollutant emissions from a diesel engine operating on several different biodiesels. Tests were performed on non-road diesel engine. Experiments were performed on 5 different fuel blends at 2 different engine loading conditions (50% and 75%). 5 different fuel blends were ultra-low sulfur diesel (ULSD, 100%), soy biodiesel (Blend 20% and Blend 100%) and canola biodiesel (Blend 20% and Blend 100%). The chemical properties of particulate pollutants were characterized using an Aerodyne High Resolution Time of Flight Aerosol Mass Spectrometer (HR-ToF-AMS). Organic matter and nitrate were generally the most abundant aerosol components and exhibited maximum concentration of $1207{\mu}g/m^3$ and $30{\mu}g/m^3$, respectively. On average, the oxidized fragment families ($C_xH_yO_1{^+}$, and $C_xH_yO_z{^+}$) account for ~13% of the three family sum, while ~87% comes from the $C_xH_y{^+}$ family. The two peaks of $C_2H_3O_2$ (m/z 59.01) and $C_3H_7O$ (m/z 59.04) located at approximately m/z 59 could be used to identify atmospheric particulate matter directly to biodiesel exhaust, as distinguished from that created by petroleum diesel in the AMS data.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.6
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• pp.662-673
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• 2008

Two hybrid receptor models, potential source contribution function (PSCF) and concentration weighted tracjectory (CWT), were compared for locating $PM_{2.5}$ sources contributing to the atmospheric $PM_{2.5}$ concentrations in Seoul. The source contribution estimates by chemical receptor model (CMB) receptor model were used to identify better source areas, Among the sources, soil, agricultural burning, marine aerosol, coal-fired power plant and Chinese aerosol were only considered for the study because these sources were more likely to be associated with the long-range transport of air pollutant. Both methods are based on combining chemical data with calculated air parcel backward trajectories. However, the PSCF analyses were performed with trajectories above the $75^{th}$ percentile criterion values, while the CWT analyses used all trajectories. This difference resulted in locating of different sources, which might be helpful to interpret locating of $PM_{2.5}$ sources, High possible source areas in source contribution of soil and agricultural burning contributing to the Seoul $PM_{2.5}$ were inland areas of Heibei and Shandong provinces (highest density areas of agricultural production and population) in China. The "Chinese aerosol" was used as a representative source for the $PM_{2.5}$ originated from urban area in China. High possible source areas for the aerosol were the cities in China where are relatively close to the receptor. This result suggests that Chinese aerosol is likely to be a useful tool in studies on source apportionment and identification in Korea.

• Proceedings of the KSRS Conference
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• v.1
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• pp.236-238
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-functional Transport Satellite-1 Replacement (MTSAT-1R) data. The algorithm is the hybrid algorithm that has used two methods combined together. The first method used the differential absorption in brightness temperature difference between $11{\mu}m$ and $12{\mu}m$ (BTD1). The radiation at 11 ${\mu}m$ is absorbed more than at 12 ${\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m$ and $11{\mu}m$ (BTD2). The technique would be most sensitive to dust loading during the day when the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. We have applied the three methods to MTSAT-1R for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. As produced Principle Component Image (PCI) through the PCA is the correlation between BTD1 and BTD2, errors of about 10% that have a low correlation are eliminated for aerosol detection. For the region of aerosol detection, aerosol index (AI) is produced to the scale of BTD1 and BTD2 values over land and ocean respectively. AI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between AI and OMI aerosol index (AI) shows remarkable good correlations during daytime and relatively good correlations over the land.