[논문]두 층 관측 기상인자의 주성분-다중회귀분석으로 도출되는 고농도 미세먼지의 부산-서울 지역차이 해석

최다니엘; 장임석; 김철희

doi:10.14191/atmos.2021.31.1.029

두 층 관측 기상인자의 주성분-다중회귀분석으로 도출되는 고농도 미세먼지의 부산-서울 지역차이 해석
Interpretation and Comparison of High PM2.5 Characteristics in Seoul and Busan based on the PCA/MLR Statistics from Two Level Meteorological Observations 원문보기

대기 = Atmosphere, v.31 no.1, 2021년, pp.29 - 43

최다니엘 (부산대학교 지구환경시스템학부 대기환경과학과) , 장임석 (국립환경과학원 기후대기연구부) , 김철희 (부산대학교 지구환경시스템학부 대기환경과학과)

Abstract ▼ AI-Helper

In this study, two-step statistical approach including Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) was employed, and main meteorological factors explaining the high-PM2.5 episodes were identified in two regions: Seoul and Busan. We first performed PCA to isolate the Principal Component (PC) that is linear combination of the meteorological variables observed at two levels: surface and 850 hPa level. The employed variables at surface are: temperature (T2m), wind speed, sea level pressure, south-north and west-east wind component and those at 850 hPa upper level variables are: south-north (v850) and west-east (u850) wind component and vertical stability. Secondly we carried out MLR analysis and verified the relationships between PM2.5 daily mean concentration and meteorological PCs. Our two-step statistical approach revealed that in Seoul, dominant factors for influencing the high PM2.5 days are mainly composed of upper wind characteristics in winter including positive u850 and negative v850, indicating that continental (or Siberian) anticyclone had a strong influence. In Busan, however, the dominant factors in explanaining in high PM2.5 concentrations were associated with high T2m and negative u850 in summer. This is suggesting that marine anticyclone had a considerable effect on Busan's high PM2.5 with high temperature which is relevant to the vigorous photochemical secondary generation. Our results of both differences and similarities between two regions derived from only statistical approaches imply the high-PM2.5 episodes in Korea show their own unique characteristics and seasonality which are mostly explainable by two layer (surface and upper) mesoscale meteorological variables.

주제어

표/그림 (12)

그림 Fig. 1. Locations of PM measurement stations and ASOS meteorological observation stations
표 Table 1. Air pollutant data and meteorological data used in this study.
표 Table 2. (a) The result of the statistical analysis (PCA/MLR) with the PCs of meteorological variables and PM_2.5 concentration in Busan, and (b) in Seoul, respectively.
그림 Fig. 2. Time series of PM_2.5 in Busan and Seoul in 2018~2019.
그림 Fig. 3. Flow chart of two-step statistical analysis with Principal component analysis (PCA) and multiple linear regression (MLR) analysis.
그림 Fig. 4. Schematic diagram of PM_2.5 transport and distributions based on meteorological characteristics of two-layer atmosphere.
그림 Fig. 5. Factor Loadings of main PCs in 2018~2019.
그림 Fig. 6. Scree Plots of calculated PCs.
그림 Fig. 7. Scatter charts of calculated PCs with meteorological variables.
그림 Fig. 8. (a) Comparison of PC - PM_2.5 time series in Seoul. (b) Comparison of PC - PM_2.5 time series in Busan.
그림 Fig. 9. Weather chart of the high PM_2.5 case days in (a) March 04, 2019, and (b) July 17, 2018, respectively.
그림 Fig. 10. HYSPLIT Backward trajectories during the high PM_2.5 Case days.

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