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http://dx.doi.org/10.7780/kjrs.2017.33.2.3

Estimation of surface nitrogen dioxide mixing ratio in Seoul using the OMI satellite data  

Kim, Daewon (Division of Earth Environmental System Science Major of Spatial Information Engineering, Pukyong National University)
Hong, Hyunkee (Division of Earth Environmental System Science Major of Spatial Information Engineering, Pukyong National University)
Choi, Wonei (Division of Earth Environmental System Science Major of Spatial Information Engineering, Pukyong National University)
Park, Junsung (Division of Earth Environmental System Science Major of Spatial Information Engineering, Pukyong National University)
Yang, Jiwon (Division of Earth Environmental System Science Major of Spatial Information Engineering, Pukyong National University)
Ryu, Jaeyong (Department of Urban Environmental Engineering, Kyungnam University)
Lee, Hanlim (Division of Earth Environmental System Science Major of Spatial Information Engineering, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.33, no.2, 2017 , pp. 135-147 More about this Journal
Abstract
We, for the first time, estimated daily and monthly surface nitrogen dioxide ($NO_2$) volume mixing ratio (VMR) using three regression models with $NO_2$ tropospheric vertical column density (OMIT-rop $NO_2$ VCD) data obtained from Ozone Monitoring Instrument (OMI) in Seoul in South Korea at OMI overpass time (13:45 local time). First linear regression model (M1) is a linear regression equation between OMI-Trop $NO_2$ VCD and in situ $NO_2$ VMR, whereas second linear regression model (M2) incorporates boundary layer height (BLH), temperature, and pressure obtained from Atmospheric Infrared Sounder (AIRS) and OMI-Trop $NO_2$ VCD. Last models (M3M & M3D) are a multiple linear regression equations which include OMI-Trop $NO_2$ VCD, BLH and various meteorological data. In this study, we determined three types of regression models for the training period between 2009 and 2011, and the performance of those regression models was evaluated via comparison with the surface $NO_2$ VMR data obtained from in situ measurements (in situ $NO_2$ VMR) in 2012. The monthly mean surface $NO_2$ VMRs estimated by M3M showed good agreements with those of in situ measurements(avg. R = 0.77). In terms of the daily (13:45LT) $NO_2$ estimation, the highest correlations were found between the daily surface $NO_2$ VMRs estimated by M3D and in-situ $NO_2$ VMRs (avg. R = 0.55). The estimated surface $NO_2$ VMRs by three modelstend to be underestimated. We also discussed the performance of these empirical modelsfor surface $NO_2$ VMR estimation with respect to otherstatistical data such asroot mean square error (RMSE), mean bias, mean absolute error (MAE), and percent difference. This present study shows a possibility of estimating surface $NO_2$ VMR using the satellite measurement.
Keywords
nitrogen dioxide; $NO_2$ mixing ratio; multiple regression; OMI $NO_2$ column; trace gas estimation;
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