Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Estimation of surface nitrogen dioxide mixing ratio in Seoul using the OMI satellite data

OMI 위성자료를 활용한 서울 지표 이산화질소 혼합비 추정 연구

  • 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)
  • 김대원 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공) ;
  • 홍현기 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공) ;
  • 최원이 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공) ;
  • 박준성 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공) ;
  • 양지원 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공) ;
  • 류재용 (경남대학교 도시환경공학과) ;
  • 이한림 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공)
  • Received : 2017.03.03
  • Accepted : 2017.03.28
  • Published : 2017.04.30
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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.

본 연구에서는 처음으로 한반도 서울지역에서 OMI (Ozone Monitoring Instrument) 센서로 관측된 대류권 이산화질소 칼럼농도를 이용하여 OMI 센서의 관측시간인 13:45에서의 월 평균 및 일별 위성 지표 이산화질소 혼합비를 추정하였다. 본 연구에서는 세 가지 회귀모델들이 이용되었다. 첫 번째 회귀모델(M1)은 OMI 대류권 이산화질소 칼럼농도와 지점 측정값과의 선형회귀를 통한 회귀계수로 구성되어있다. 두번째 회귀모델(M2)은 OMI 대류권 이산화질소 칼럼농도와 AIRS (Atmospheric Infrared Sounder) 센서로 관측한 행성경계층 높이, 온도, 압력 자료 모두가 반영된 회귀모델이다. 세 번째 회귀모델(M3M, M3D)은 다중회귀모델로서 앞서 고려된 이산화질소 칼럼농도와 행성경계층 높이와 다양한 기상변수를 추가적으로 반영하는 회귀모델이다. 본 연구에서는 2009년에서 2011년까지를 회귀모델의 훈련기간으로 하여서 각 회귀식의 회귀계수를 도출하였으며 2012년도는 검증기간으로서 훈련기간에 도출된 회귀모델들의 성능을 평가하였다. 회귀모델들로 추정된 월 평균 지표 이산화질소 혼합비와 지점 관측소에서 지점 측정장비로 측정된 월평균 지표 이산화질소 혼합비와 가장 높은 상관성(avg. R = 0.77)을 보이는 회귀분석방법은 다중회귀분석방법(M3M)이다. 또한, 회귀모델들로 추정된 13:45에서의 일 지표 이산화질소 혼합비와 지점 관측소에서 지점장비로 측정된 지표 이산화질소 혼합비와 가장 좋은 상관성(avg. R = 0.55)을 보인 것도 다중회귀분석방법(M3D)이다. 회귀모델들로 추정된 지표 이산화질소 혼합비는 지점 측정값에 비해 과소추정 되는 경향이 나타났다. 회귀모델들로 추정된 지표 이산화질소 혼합비를 평가하기 위해 지점 측정값과의 RMSE (Root Mean Square Error), mean bias, MAE (Mean Absolute Error), percent difference와 같은 통계분석을 실시하였다. 본 연구는 위성을 통한 지표 이산화질소 혼합비 산출 가능성을 보여준다.

Keywords

References

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