Journal of Environmental Impact Assessment (환경영향평가)

Korean Society of Environmental Impact Assessment (한국환경영향평가학회)
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Analysis of PM2.5 Concentration and Contribution Characteristics in South Korea according to Seasonal Weather Patternsin East Asia: Focusing on the Intensive Measurement Periodsin 2015

동아시아 지역의 계절별 기상패턴에 따른 우리나라 PM2.5 농도 및 기여도 특성 분석: 2015년 집중측정 기간을 중심으로

  • Nam, Ki-Pyo (Air Quality Forecasting Center, Climate and Air Quality Research Department, NIER) ;
  • Lee, Dae-Gyun (Air Quality Forecasting Center, Climate and Air Quality Research Department, NIER) ;
  • Jang, Lim-Seok (Air Quality Forecasting Center, Climate and Air Quality Research Department, NIER)
  • 남기표 (국립환경과학원 기후대기연구부 대기질통합예보센터) ;
  • 이대균 (국립환경과학원 기후대기연구부 대기질통합예보센터) ;
  • 장임석 (국립환경과학원 기후대기연구부 대기질통합예보센터)
  • Received : 2018.12.26
  • Accepted : 2019.05.14
  • Published : 2019.06.30
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Abstract

In this study, the characteristics of seasonal $PM_{2.5}$ behavior in South Korea and other Northeast Asian regions were analyzed by using the $PM_{2.5}$ ground measurement data, weather data, WRF and CMAQ models. Analysis of seasonal $PM_{2.5}$ behavior in Northeast Asia showed that $PM_{2.5}$ concentration at 6 IMS sites in South Korea was increased by long-distance transport and atmospheric congestion, or decreased by clean air inflow due to seasonal weather characteristics. As a result of analysis by applying BFM to air quality model, the contribution from foreign countries dominantly influenced the $PM_{2.5}$ concentrations of Baengnyeongdo due to the low self-emission and geographical location. In the case of urban areas with high self-emissions such as Seoul and Ulsan, the $PM_{2.5}$ contribution from overseas was relatively low compared to other regions, but the standard deviation of the season was relatively high. This study is expected to improve the understanding of the air pollutant phenomenon by analyzing the characteristics of $PM_{2.5}$ behavior in Northeast Asia according to the seasonal weather condition change. At the same time, this study can be used to establish the air quality policy in the future, knowing that the contribution of $PM_{2.5}$ concentration to the domestic and overseas can be different depending on the regional emission characteristics.

본 연구에서는 지상 $PM_{2.5}$ 측정 자료와 일기도 자료, WRF 및 CMAQ 모델을 활용하여 동북아시아 지역의 계절별 $PM_{2.5}$ 거동특성을 분석하였으며, 대기질 모델에 BFM을 적용하여 우리나라 $PM_{2.5}$ 농도에 대한 계절별 국내외 기여도를 평가하였다. 일기도 자료를 기반으로 국내 $PM_{2.5}$ 측정 자료 및 대기질 모사결과를 통해 $PM_{2.5}$의 거동특성을 분석한 결과, 동북아 지역에서의 $PM_{2.5}$는 장거리 수송된 대기오염 물질의 유입 및 대기정체 현상에 기인한 농도의 증가 또는 깨끗한 공기의 유입에 따른 농도의 감소 등의 특징이 계절별 종관기상 특성에 따라 상이하게 나타났다. 대기질 모델에 BFM (Brute-Force Method)을 적용하여 우리나라 6개 집중측정소 지점의 $PM_{2.5}$ 농도에 대한 국내외 기여도 평가를 수행한 결과, 백령도 지역은 낮은 자체 배출량과 동시에 중국으로부터 인접한 지리적 특성으로 인해 국외로부터의 기여가 지배적인 영향을 미치는 것으로 나타났다. 반면, 서울, 울산과 같이 높은 자체 배출량 특성을 나타내는 지역의 경우, $PM_{2.5}$에 대한 국외 기여도는 타 지역에 비해 상대적으로 낮게 나타남과 동시에 계절에 따른 기여도의 표준편차는 상대적으로 높게 나타나는 특징을 보였다. 본 연구는 우리나라를 중심으로 계절별 기상조건 변화에 따른 동북아 지역의 $PM_{2.5}$ 거동특성을 분석하여 국내 대기오염물질 현상에 대한 이해를 증진함과 동시에, 지역 배출특성에 따라 $PM_{2.5}$ 농도에 대한 국내외 기여도는 상이할 수 있음을 알려 향후 대기질 개선 대책 수립시 기초자료로 활용될 수 있을 것으로 기대된다.

Keywords

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Figure 1. Domain settings for air quality modeling and IMS locations in South Korea.

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Figure 2. Time series of simulated (line) and measured (dot) daily mean PM2.5 concentrations for Case 1 period (a: Seoul,b: Daejeon).

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Figure 3. Time series of daily mean PM2.5 concentrations at 6 IMS sites during Case1.

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Figure 4. Spatial distributions of hourly PM2.5 representing the Case1 period.

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Figure 5. Time series of daily mean PM2.5 concentrations at 6 IMS sites during Case2.

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Figure 6. Spatial distributions of hourly PM2.5 representing the Case2 period.

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Figure 7. Time series of daily mean PM2.5 concentrations at 6 IMS sites during Case3.

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Figure 8. Spatial distributions of hourly PM2.5 representing the Case3 period.

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Figure 9. Time series of daily mean PM2.5 concentrations at 6 IMS sites during Case4.

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Figure 10. Spatial distributions of hourly PM2.5 representing the Case4 period.

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Figure 11. Box plot of foreign contributions to PM2.5 by region.

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Figure 12. Time series (left) and box-plot (right) of measured PM2.5 concentrations and model bias for meteorological variables (upper: wind speed, lower: middle-low cloud fraction).

Table 1. Seasonal analysis period in this study

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Table 2. WRF and CMAQ configurations

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Table 3. Results of statistical verification of simulated daily mean PM2.5 for 6 IMSs.

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Table 4. Simulated mean PM2.5 concentration with standard deviation (μg/m3) and domestic and foreign contribution (%) by case

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