• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.374-377
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• 2005

The distribution and changes of tropospheric nitrogen dioxide ($NO_2$) are analyzed using the satellite measurements data from GOME (Global Ozone Monitoring Experiment) and SCIMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY). We produced global maps of tropospheric $NO_2$ for 4 seasons using GOME measurements from January 1997 to June 2003. The global distribution shows high values in regions with dense population and high industrialization. Tropospheric $NO_2$ shows obvious seasonal changes depending on its emission and lifetime. Based on the good agreement between two instruments in the time period of overlapping measurements (January 2003-June2003), we linked SClAMACHY data to the GOME time series. The combined time series over the past decade indicate that $NO_2$ 1evels over China are rapidly increasing while those over Europe are decreasing. We also discussed potential application of spaceborne instruments in detecting and characterizing long-distance transport of $NO_2$.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2009.10a
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• pp.544-545
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• 2009

• Atmosphere
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• v.20 no.4
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• pp.495-503
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• 2010

Satellite data have an intrinsic problem due to a number of various physical parameters, which can have a similar effect on measured radiance. Most evaluations of satellite performance have relied on comparisons with limited spatial and temporal resolution of ground-based measurements such as soundings and in-situ measurements. In order to overcome this problem, a new way of satellite data evaluation is suggested with statistical tools such as empirical orthogonal function(EOF), and singular value decomposition(SVD). The EOF analyses with OMI and OMI HCHO over northeast Asia show that the spatial pattern show high correlation with population density. This suggests that human activity is a major source of as well as HCHO over this region. However, this analysis is contradictory to the previous finding with GOME HCHO that biogenic activity is the main driving mechanism(Fu et al., 2007). To verify the source of HCHO over this region, we performed the EOF analyses with vegetation and HCHO distribution. The results showed no coherence in the spatial and temporal pattern between two factors. Rather, the additional SVD analysis between $NO_2$ and HCHO shows consistency in spatial and temporal coherence. This outcome suggests that the anthropogenic emission is the main source of HCHO over the region. We speculate that the previous study appears to be due to low temporal and spatial resolution of GOME measurements or uncertainty in model input data.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.10a
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• pp.98-99
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• 2008

• Korean Journal of Plant Resources
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• v.35 no.2
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• pp.242-247
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• 2022

Richardia brasiliensis Gomes was found in agricultural land in Susan-ri, Seogwipo-si, Jeju-do. Richardia L. is closely related to Diodia L. in Korea, those are confirmed to features of calyx, collora and fruit. This species is similar to R. scabra L., but it differs morphologically from the letter in surface structure of mericarp. The mericarp of R. brasiliensis has line like kneel at the adaxial, whereas R. scabra has groove at the adaxial. R. brasiliensis is recorded for the first time in this study and a morphological description, distribution map, and photographs are presented.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.417-430
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• 2014

We have found an error in the operational OMI HCHO columns, and corrected it by applying a background parameterization derived on a 4th order polynomial fit to the time series of monthly average OMI HCHO data. The corrected OMI HCHO agrees with this understanding as well as with the other sensors measurements and has no unrealistic trends. A new scientific approach, statistical analyses with EOF and SVD, was adapted to reanalyze the consistency of the corrected OMI HCHO with other satellite measurements of HCHO, CO, $NO_2$, and fire counts over Africa. The EOF and SVD analyses with MOPITT CO, OMI $NO_2$, SCIAMAHCY, and OMI HCHO show the overall spatial and temporal pattern consistent with those of biomass burning over these regions. However, some discrepancies were observed from OMI HCHO over northern equatorial Africa during the northern biomass burning seasons: The maximum HCHO was found further downwind from where maximum fire counts occur and the minimum was found in January when biomass burning is strongest. The statistical analysis revealed that the influence of biogenic activity on HCHO wasn't strong enough to cause the discrepancies, but it is caused by the error in OMI HCHO from using the wrong Air Mass Factor (AMF) associated with biomass burning aerosol. If the error is properly taken into consideration, the biomass burning is the strongest source of HCHO seasonality over the regions. This study suggested that the statistical tools are a very efficient method for evaluating satellite data.