• Journal of Korean Society for Atmospheric Environment
• /
• v.11 no.3
• /
• pp.279-290
• /
• 1995

Recently in Korea, due to the significant drop of lead and bromine levels as a marker of autoemission source in the urban areas, the conventional application of receptor methods has many difficulties to properly apportion mass contribution of some sources. It is then needed to urgently develop alternative source profiles and identify new emission markers. Thus, the study has extensively examined the results obtained from using PAHs and elemental data for receptor modeling and has provided an opportunity to identify alternative source compositions and to determine a proper number of the ambient emission sources in Seoul area. The purpose of the study is to identify the sources of PM-10 and to estimate their mass contributions in Seoul area. Thus, a receptor model, target transformation factor analysis(TTFA) has been massively applied. The TTFA offers the possibility of determining the number of sources and their mass contributions. The input data used in this study are composed of two separate sets: fine (d$_{p}$ < 2.5.mu.m) and coarse (2.5.mu.m < d$_{p}$ < 10.mu.m) mode aerosol samples. Each sample was simultaneously collected by a PM-10 dichotomous sampler during the daytime(8 AM to 8 PM) and the nighttime(8 PM to 8 AM) from February to October 1993 on the Sungdong-Gu, Seoul. All the samples were analyzed to determine the levels of 10 inorganic elements by an XRF system as well as 14 PAHs by a HPLC. However, only 8 inorganic elements and 7 PAHs were used for the various statistical analysis.sis.

• Environmental Engineering Research
• /
• v.25 no.3
• /
• pp.281-289
• /
• 2020

Environmental fate of ionizable organic pollutants such as perfluoroalkyl acids (PFAAs) are of increasing interest but has not been well understood because of uncertain values for parameters related with atmospheric interphase partitioning behavior. In the present study, not only the values for air-water partition coefficient (K_AW) and dissociation constant (pKa) of PFAAs were induced by adjusting to in situ measurements of air-water distribution coefficient between vapor phase and rainwater but also gas-particle partition coefficients were also estimated using three-phase partitioning model of ionizable organic pollutants, in situ measurements of PFAAs in aerosol and air vapor phase, and obtained parameter values. The pKa values of PFAAs we obtained were close to the minimum values suggested in literature except for perfluorooctane sulfonic acids, and COSMOtherm-modeled K_AW values were assessed to more appropriate among suggested values. When applying parameter values we obtained, it was predicted that air particle-associated fate and transport of PFAAs could be negligible and PFAAs could distribute ubiquitously along the transection from urban to rural region by pH-dependent phase transfer in air. Our study is expected to have some implications in prediction of the environmental redistribution of other ionizable organic compounds.

• Advances in environmental research
• /
• v.1 no.1
• /
• pp.69-82
• /
• 2012

Based on the measurement data of the particulate matter with an aerodynamic diameter of less than or equal to a nominal 10 ${\mu}m$ (PM10) by the ${\beta}$-ray absorption method (BAM) equipped with an inlet heater and the gravimetric method (GMM) at two coastal sites in Korea, the optimal inlet heater temperature was estimated. By using a gas/particle equilibrium model, Simulating Composition of Atmospheric Particles at Equilibrium 2 (SCAPE2), water content in aerosols was estimated with varying temperature to find the optimal temperature increase to make the PM10 concentration by BAM comparable to that by GMM. It was estimated that the heated air temperature inside the BAM should be increased up to $35{\sim}45^{\circ}C$ at both sites. At this temperature range, evaporation of volatile aerosol components was minor. Similar ($30{\sim}50^{\circ}C$) temperature range was also obtained from the calculation based on the absolute humidity which changed with ambient absolute humidity and chemical composition of hygroscopic species.

• Journal of the Optical Society of Korea
• /
• v.14 no.3
• /
• pp.178-184
• /
• 2010

We present linear particle depolarization ratios (LPDRs) retrieved from measurements with an AERONET Sun photometer at the Gwangju Institute of Science and Technology (GIST), Korea ($35.10^{/circ}N$, $126.53^{\circ}E$) between 19 October and 3 November 2009. The Sun photometer data were classified into three categories according to ${\AA}$ngstr$\ddot{o}$ exponent and size distribution: 1) pure Asian dust (19 October 2009), 2) Asian dust mixed with urban pollution observed in the period from 20-26 October 2009, and 3) clean conditions (3 November). We show that the LPDRs can be used to distinguish among Asian dust, mixed aerosol, and non-Asian dust in the atmosphere. The mean LPDR of the pure Asian dust case is 23 %. Mean LPDRs are 13 % for the mixed case. The lowest mean LPDR is 6 % in the clean case. We compare our results to vertically resolved LPDRs (at 532 nm) measured by a Raman LIDAR system at the same site. In most cases, we find good agreement between LPDRs derived with Sun photometer and measured by LIDAR.

• Proceedings of the KSRS Conference
• /
• 2009.03a
• /
• pp.254-259
• /
• 2009

In this study, Simplified Method for the Atmospheric Correction (SMAC) radiative transfer model (RTM) used to retrieve surface reflectance from MODIS Top Of Atmosphere (TOA) reflectance (MOD02). SMAC code provides coefficients which were previously yielded by Second Simulation of the Satellite Signal in the Solar Spectrum (6S) for each satellite sensor. We conducted error analysis of SMAC RTM using MOD02 over comparison with MODIS surface reflectance (MOD09) which was provided from 6S. It showed that low accuracy values such as, $R^2$ : 0.6196, Root Means Square Error (RMSE) : 0.00031, bias : - 0.0859. Thus sensitivity analysis of input parameters and coefficients was conducted to searching error sources. Coefficients about $\tau_p$ (average AOD) are more influence than any other coefficients of $\tau_{a550}$ (Aerosol Optical Depth at 550nm) from sensitivity test. Calibrated coefficients of $\tau_p$ from regression analysis were used to surface reflectance which showed that improve accuracy of surface reflectance ($R^2$ : 0.827, RMSE : 0.00672, bias : - 0.000762).

• Journal of Electrical Engineering and Technology
• /
• v.13 no.4
• /
• pp.1697-1704
• /
• 2018

As a key component of lighting location system (LLS) for lightning warning, the atmospheric electric field measuring is required to have high accuracy. The Conventional methods of the existent electric field measurement meter can only detect the vertical component of the atmospheric electric field, which cannot acquire the realistic electric field in the thunderstorm. This paper proposed a three dimensional (3D) electric field system for atmospheric electric field measurement, which is capable of three orthogonal directions in X, Y, Z, measuring. By analyzing the relationship between the electric field and the relative permittivity of ground surface, the permittivity is calculated, and an efficiency 3D measurement model is derived. On this basis, a three-dimensional electric field sensor and a permittivity sensor are adopted to detect the spatial electric field. Moreover, the elevation and azimuth of the detected target are calculated, which reveal the location information of the target. Experimental results show that the proposed 3D electric field meter has satisfactory sensitivity to the three components of electric field. Additionally, several observation results in the fair and thunderstorm weather have been presented.

• Korean Journal of Remote Sensing
• /
• v.32 no.1
• /
• pp.67-71
• /
• 2016

The surface reflectance is essential to retrieval various indicators related land properties such as vegetation index, albedo and etc. In this study, we estimated surface reflectance using Himawari-8 / Advanced Himawari Imager (AHI) channel data. In order to estimate surface reflectance from Top of Atmosphere (TOA) reflectance, the atmospheric correction is necessary because all of the TOA reflectance from optical sensor is affected by gas molecules and aerosol in the atmosphere. We used Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) Radiative Transfer Model (RTM) to correct atmospheric effect, and Look-Up Table (LUT) to shorten the calculation time. We verified through comparison Himawri-8 / AHI surface reflectance and Proba-V S1 products. As a result, bias and Root Mean Square Error (RMSE) are calculated about -0.02 and 0.05.

• Asia-Pacific Journal of Atmospheric Sciences
• /
• v.54 no.4
• /
• pp.527-544
• /
• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• Journal of the Korean earth science society
• /
• v.38 no.4
• /
• pp.283-292
• /
• 2017

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that $M_6$ was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.

• Korean Journal of Remote Sensing
• /
• v.37 no.2
• /
• pp.321-335
• /
• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.