[논문]서울의 기상 조건에 따른 미세먼지와 시정의 상관성

김민석; 이서영; 조예슬; 구자호; 염성수; 김준

doi:10.14191/atmos.2020.30.4.391

[국내논문] 서울의 기상 조건에 따른 미세먼지와 시정의 상관성
The Relationship of Particulate Matter and Visibility Under Different Meteorological Conditions in Seoul, South Korea 원문보기

대기 = Atmosphere, v.30 no.4, 2020년, pp.391 - 404

김민석 (연세대학교 대기과학과) , 이서영 (연세대학교 대기과학과) , 조예슬 (연세대학교 대기과학과) , 구자호 (연세대학교 대기과학과) , 염성수 (연세대학교 대기과학과) , 김준 (연세대학교 대기과학과)

Abstract ▼ AI-Helper

To understand the characteristics of the relationship between visibility and particulate matter (PM) in different meteorological conditions, we investigated the contributions of PM and relative humidity (RH) to visibility in Seoul, South Korea. For the period from 2001 to 2018, both PM and RH show descending trends, resulting in a visibility increase. PM has little impact on the hourly variation of visibility, which could be explained more by the RH variability. Meanwhile, the daily change of PM accounts for daily visibility variation. For the monthly variation of visibility, both PM and RH showed similar influence. The correlation coefficients of PM10, PM2.5, and RH with visibility was -0.486, -0.644, and -0.556, respectively, which became higher during the high PM seasons of spring and winter. The correlation coefficient between PM2.5 and visibility was -0.454 for RH higher than 80%, and -0.780 for RH between 40% and 60%. From 2017 to 2018, there were 10 cases of extreme visibility impairment, among which five cases were incurred by high PM pollution, and two cases were by high humidity. Further analysis with PM chemical composition measurements is required to better understand the characteristics of visibility in Seoul.

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표/그림 (13)

그림 Fig. 1. Flowchart of visibility impairment determination criteria using visibility and RH observation.
그림 Fig. 2. Long-term change of relative frequency of haze cases from 1989 to 2018 in Seoul.
그림 Fig. 3. Long-term change of (a) visibility, (b) PM₁₀, (c) PM_2.5, (d) RH from 2001 to 2018 in Seoul. The representative value of visibility is annual median, and that of RH is annual mean. The ends of whiskers represent 5th and 95th percentiles, the ends of the boxes represent 25th and 75th percentiles and red dot represents annual mean.
그림 Fig. 4. Monthly variation of visibility (red line), RH (blue line), PM₁₀ (black line) and PM_2.5 (black dashed line).
그림 Fig. 5. Weekly variation of visibility (red line), PM_10n (black line) and PM_2.5 (black dashed line). (b), (c), (d) and (e) are the same plot for spring, summer, autumn and winter, respectively.
그림 Fig. 6. Diurnal variation of visibility (red line), RH (blue line), PM₁₀ (black line) and PM_2.5 (black dashed line).
표 Table 1. Annual and seasonal means and standard deviations of visibility, PM and meteorological variables.
표 Table 2. Pearson correlation coefficients between visibility and relevant variables. Asterisk (*) means the best correlated season of each variable.
표 Table 4. Duration, mean visibility, mean PM mass concentration, mean RH, mean wind speed, mean sea surface temperature, temperature anomaly (mean temperature - monthly mean temperature) for the 10 continuous haze cases
표 Table 3. (Top row) Correlations between visibility and PM_2.5 over 5 different RH intervals, and (bottom 2 rows) correlations between visibility and RH over 4 different PM_2.5 and PM₁₀ intervals.
그림 Fig. 7. Scatter plot of mean PM2.5 concentrations and mean RH during 10 continuous haze cases in Seoul for 2017 and 2018.
그림 Fig. 8. Hourly variation of visibility (red line), RH (blue line), PM₁₀ (black line), and PM_2.5 (black dashed line) during 10 continuous haze cases in Seoul for 2017 and 2018. Red box on the left represents continuous haze caused by severe PM pollution, blue box on the right represents continuous haze caused by high humidity.
그림 Fig. 9. Scatter plots of visibility with PM₁₀ (a), and PM_2.5 (b). Logarithmic fitting curves for relative humidity ranges are also shown in each plot.

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