• Proceedings of the KSRS Conference
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• v.1
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• pp.308-311
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• 2006

This study compared between tropospheric column ozone by applying the SAM method to TOMS and OMI data for northern summer. Tropospheric ozone from the SAM represents a peak over the tropical Atlantic, where it is related with biomass burning. This feature is also seen in the distribution of the model and CO. Additionally, enhancement of the SAM ozone over the Middle East, and South and North America agrees well with the model and CO distribution. However, the SAM results show more ozone than the model results over the northern hemisphere, especially the ocean (e.g. the North Pacific and the North Atlantic). The tropospheric ozone distribution from OMI data shows more ozone than that from TOMS data. This can be caused by different viewing angle, sampling frequency, and a-priori ozone profiles between OMI and TOMS. The correlation between the SAM tropospheric ozone and CO is better than that between the model and CO in the tropics. However, that correlation is reversed in the midlatitude.

• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.663-670
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• 2013

Total Vertical Column Density (VCD) of $NO_2$, a key component in air quality and tropospheric chemistry was measured using a ground-based instrument, Pandora, in Seoul from March 2012 to October 2013. The $NO_2$ measurements using Pandora were compared with those obtained by satellite remote sensing from Ozone Monitoring Instrument (OMI) where the intercomparison characteristics were analyzed as a function of measurement geometry, cloud amount and aerosol loading. The negative biases of the OMI $NO_2$ VCD were larger when cloud amount and Aerosol Optical Depth (AOD) were higher. The correlation coefficient between $NO_2$ VCDs from Pandora and OMI was 0.53 for the entire measurement period, whereas the correlation coefficient between the two was 0.74 when the cloud amount and AOD were low (cloud amount<3, AOD<0.4). The low bias of OMI data was associated with the shielding effect of the cloud and the aerosols.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.503-516
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• 2019

We have estimated the vertical column density (VCD) of formaldehyde (HCHO) on a global scale using a multiple linear regression method (MRM) with Ozone Monitoring Instrument (OMI) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. HCHO VCDs were estimated in regions of biogenic, pyrogenic, and anthropogenic emissions using independent variables, including $NO_2$ VCD, land surface temperature (LST), an enhanced vegetation index (EVI), and the mean fire radiative power (MFRP), which are strongly correlated with HCHO. To evaluate the HCHO estimates obtained using the MRM, we compared estimates of HCHO VCD data measured by OMI ($HCHO_{OMI}$) with those estimated by multiple linear regression equations (MRE) ($HCHO_{MRE}$). Good MRM performances were found, having the average statistical values (R = 0.91, slope = 1.03, mean bias = $-0.12{\times}10^{15}molecules\;cm^{-2}$, percent difference = 11.27%) between $HCHO_{MRE}$ and $HCHO_{OMI}$ in our study regions where high HCHO levels are present. Our results demonstrate that the MRM can be a useful tool for estimating atmospheric HCHO levels.

• Atmosphere
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• v.25 no.1
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• pp.117-127
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• 2015

The presences of clouds significantly influence the accuracy of ozone retrievals from satellite measurements. This study focuses on the influence of clouds on Ozone Monitoring instrument (OMI) ozone profile retrieval based on an optimal estimation. There are two operational OMI cloud products; OMCLDO2, based on absorption in $O_2-O_2$ at 477 nm, and OMCLDRR, based on filling in Fraunhofer lines by rotational Raman scattering (RRS) at 350 nm. Firstly, we characterize differences between $O_2-O_2$ and RRS effective cloud pressures using MODIS cloud optical thickness (COT), and then compare ozone profile retrievals with different cloud input data. $O_2-O_2$ cloud pressures are significantly smaller than RRS by ~200 hPa in thin clouds, which corresponds to either low COT or cloud fraction (CF). On the other hand, the effect of Optical centroid pressure (OCP) on ozone retrievals becomes significant at high CF. Tropospheric ozone retrievals could differ by up to ${\pm}10$ DU with the different cloud inputs. The layer column ozone below 300 hPa shows the cloud-induced ozone retrieval error of more than 20%. Finally, OMI total ozone is validated with respect to Brewer ground-based total ozone. A better agreement is observed when $O_2-O_2$ cloud data are used in OMI ozone profile retrieval algorithm. This is distinctly observed at low OCP and high CF.

• The korean journal of orthodontics
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• v.48 no.1
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• pp.30-38
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• 2018

Objective: The purpose of this study was to investigate factors influencing the success rate of orthodontic microimplants (OMIs) using panoramic radiographs (PRs). Methods: We examined 160 OMIs inserted bilaterally in the maxillary buccal alveolar bone between the second premolars and first molars of 80 patients (51 women, 29 men; mean age, $18.0{\pm}6.1years$) undergoing treatment for malocclusion. The angulation and position of OMIs, as well as other parameters, were measured on PRs. The correlation between each measurement and the OMI success rate was then evaluated. Results: The overall success rate was 85.0% (136/160). Age was found to be a significant predictor of implant success (p < 0.05), while sex, side of placement, extraction, and position of the OMI tip were not significant predictors (p > 0.05). The highest success rate was observed for OMIs with tips positioned on the interradicular midline (IRML; central position). Univariate analyses revealed that the OMI success rate significantly increased with an increase in the OMI length and placement height of OMI (p = 0.001). However, in simultaneous analyses, only length remained significant (p = 0.027). Root proximity, distance between the OMI tip and IRML, interradicular distance, alveolar crest width, distance between the OMI head and IRML, and placement angle were not factors for success. Correlations between the placement angle and all other measurements except root proximity were statistically significant (p < 0.05). Conclusions: Our findings suggest that OMIs positioned more apically with a lesser angulation, as observed on PRs, exhibit high success rates.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.981-992
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• 2017

We, for the first time, retrieved tropospheric nitrogen dioxide ($Trop.NO_2$) vertical column density (VCD) from ground-based instrument, Pandora, using the optical density fitting based on Differential Optical Absorption Spectroscopy (DOAS)in Seoul for the period from May 2014 to December 2014. The $Trop.NO_2$ VCDs retrieved from Pandora were compared with those obtained from Ozone Monitoring Instrument (OMI). A correlation coefficient (R) between those retrieved from Pandora and those obtained from OMI is 0.55. To compare with surface $NO_2$ VMRs obtained from in-situ, Trop. $NO_2$ VCDs retrieved from Pandora and those obtained from OMI are converted into $NO_2$ VMRs in boundary layer (BLH $NO_2$ VMRs) using data measured from Atmospheric Infrared Sounder (AIRS). Surface $NO_2$ VMRs obtained from in-situ range from 5.5 ppbv to 61.5 ppbv. BLH $NO_2$ VMRs retrieved from Pandora and OMI range from 2.1 ppbv to 44.2 ppbv and from 0.9 ppbv to 11.6 ppbv, respectively. The range of BLH $NO_2$ VMRs retrieved from OMI is narrower than that of BLH $NO_2$ VMRs retrieved from Pandora and surface $NO_2$ VMRs obtained from in-situ. There is a batter correlation between surface $NO_2$ VMRs obtained from in-situ and BLH $NO_2$ VMRs retrieved from Pandora (R= 0.50)than the correlation between surface $NO_2$ VMRs obtained from in-situ and BLH $NO_2$ VMRs retrieved from OMI (R = 0.36). This poor correlation is thought to be due to the lower near-surface sensitivity of the satellite-based instrument (OMI) than Pandora, the ground-based instrument.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.235-241
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• 2013

$NO_2$ vertical column densities were retrieved via ground based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements for the first time for 6 months over the spring season in 2007 and 2008 in Seoul, one the megacities in the Northeast Asia. The retrieved $NO_2$ vertical column densities were compared with those obtained from space borneOzone Monitoring Instrument (OMI). Over the entire measurement period, the $NO_2$ vertical column densities measured by MAX-DOAS ranged from $1.0{\times}10^{15}molec{\cdot}cm^{-2}$ to $6.0{\times}10^{16}molec{\cdot}cm^{-2}$ while those obtained by OMI ranged $1.0{\times}10^{15}molec{\cdot}cm^{-2}$ to $7.0{\times}10^{16}molec{\cdot}cm^{-2}$. The correlation coefficient between $NO_2$ vertical column densities obtained from MAX-DOAS and OMI is 0.73 for the entire measurement period whereas the correlation coefficient of 0.85 is found for the dates under the clear sky condition. The cloudy condition is thought to play a major role in increase in uncertainty of the retrieved OMI $NO_2$ vertical column densities since air mass factor may induce high uncertainty due to the lack of cloud and aerosol vertical distribution information.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.523-530
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• 2015

A Multiple Regression Method (MRM) is used for the first time with Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate formaldehyde (HCHO) Vertical Column Density (VCD). For a 3.5-year period from January 2005 through July 2008, HCHO VCD estimation is investigated in cities over Asia in two categorized areas: (1) Major cities in Northeast Asia (Beijing, Seoul, and Tokyo), (2) Major cities in Southeast Asia (New Delhi, Dhaka, and Bangkok). In the Major cities in Northeast Asia, there are good agreements between HCHO estimated by the multiple linear regression method ($HCHO_{MRM}$) and HCHO measured by OMI ($HCHO_{OMI}$) (0.78 < $R^2$ < 0.82). However, in Major cities in Southeast Asia, there were poor agreements between $HCHO_{OMI}$ and $HCHO_{MRM}$ (0.24 < $R^2$ < 0.39). In addition, an unbiased assessment of the MRM performance using modeling and validation groups shows that the performance of the MRM based on separate modeling and validation groups is comparable to that using all the data for deriving Multiple Regression Equations (MREs). This study demonstrates that MRM can be an alternative tool for HCHO estimation in certain areas over Asia.

• Korean Journal of Remote Sensing
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• v.22 no.4
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• pp.235-242
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• 2006

This study compared between tropospheric column ozone by applying the SAM method to TOMS and OMI data for northern summer. Tropospheric ozone from the SAM represents a peak over the tropical Atlantic, where it is related with biomass burning. This feature is also seen in the distribution of the model and CO. Additionally, enhancement of the SAM ozone over the Middle East, and South and North America agrees well with the model and CO distribution. However, the SAM results show more ozone than the model results over the northern hemisphere, especially the ocean (e.g. the North Pacific and the North Atlantic). The tropospheric ozone distribution from OMI data shows more ozone than that from TOMS data. This can be caused by different viewing angle, sampling frequency, and a-priori ozone profiles between OMI and TOMS. The correlation between the SAM tropospheric ozone and CO is better than that between the model and CO in the tropics. However, that correlation is reversed in the mid-latitude.

• Atmosphere
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• v.23 no.2
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• pp.123-130
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• 2013

Despite the extensive investigations to understand the difference between ground-based and space-borne measurements, there still exist differences in total ozone (TO) measured at those two different platforms. Comparisons were carried out for the first time between TO data obtaiend from the ground based Dobson and Brewer spectrophotometers, and the Ozone Monitoring Instrument (OMI) on board EOS-Aura satellite in a megacity site in Northeast Asia. The TO values retrieved by the OMI-DOAS (Differential optical absorption spectroscopy) algorithm tend to be lower than those measured by the ground based sensors in spring and summer as well as the low solar zenith angle condition. We found that such underestimation of the OMI-DOAS TO is caused by tropospheric ozone underestimated by the OMI-DOAS algorithm when tropospheric ozone are significantly enhanced.