대기오염측정망 측정자료를 이용한 한반도 미세먼지 습식 세정 특성 연구 및 3차원 대기질 모델 적용
Wet scavenging is one of the main mechanisms for the removal of particulate matter in the atmosphere. In this respect, precipitation is an important component and plays a key role in the removal of air pollutants. When precipitation occurs, raindrops fall, inducing below cloud scavenging, absorbing...
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Published in | 한국대기환경학회지(국문) Vol. 39; no. 4; pp. 437 - 447 |
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Main Authors | , , , |
Format | Journal Article |
Language | Korean |
Published |
한국대기환경학회
01.08.2023
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Subjects | |
Online Access | Get full text |
ISSN | 1598-7132 2383-5346 |
DOI | 10.5572/KOSAE.2023.39.4.437 |
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Summary: | Wet scavenging is one of the main mechanisms for the removal of particulate matter in the atmosphere. In this respect, precipitation is an important component and plays a key role in the removal of air pollutants. When precipitation occurs, raindrops fall, inducing below cloud scavenging, absorbing pollutants and removing them from the atmosphere. The wet scavenging process is affected by the strength, duration, concentration, and distribution of air pollutants. The aerosol wet scavenging coefficient (Λm) by precipitation is used to formulate a change in aerosol concentration (C) during the precipitation time (t). According to the equation, the wet scavenging coefficient was calculated using multi-year PM2.5 hourly concentration data from the NAMIS (National Ambient air quality Monitoring Information System). The main purpose of this study is to gain a better understanding of scavenging coefficients including characteristics unique to Korea by using measurement data for five years for 12 cities. By applying a developed scavenging coefficient to the three-dimensional air quality model, these results provide wider support for improving the accuracy of simulating particle matters in East Asia. An implication of the new scavenging coefficient is a value that better reflects precipitation characteristics in Korea and one that can also help research scavenging characteristics in East Asia in the future and contribute to improving modeling accuracy. KCI Citation Count: 0 |
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Bibliography: | https://doi.org/10.5572/KOSAE.2023.39.4.437 |
ISSN: | 1598-7132 2383-5346 |
DOI: | 10.5572/KOSAE.2023.39.4.437 |