학술논문

지리정보시스템과 통계기법을 이용한 고농도 미세먼지 오염특성 파악: 동남권과 남부권 우심지역 사례 연구
Identification of Pollution Characteristics of PM2.5 Using a Geographic Information System and Statistical Tools: A Case Study of the Southeastern and Southern Regions of South Korea
Document Type
Article
Source
한국대기환경학회지, 39(4), pp.478-491 Aug, 2023
Subject
대기과학
Language
한국어
ISSN
2383-5346
1598-7132
Abstract
In recent years, fine particles (PM2.5) have become a major environmental issue in South Korea, making it necessary to investigate the regional pollution characteristics of PM2.5 to establish effective PM2.5 management policies. This study employed a geographic information system (GIS) and statistical methods, such as space-time cube analysis and principal component analysis (PCA), to identify the characteristics of high PM2.5 events and the factors influencing PM2.5 levels in the southeastern and southern regions of South Korea. The periods in which PM2.5 levels exceeded the very unhealthy state, as defined by the air quality standard of South Korea, were designated as Pollution Episodes 1 and 2 in the southeastern and southern regions, respectively. The findings revealed that both Pollution Episode 1 and Pollution Episode 2 were influenced by long-range atmospheric transport (LRAT) from Asian continental outflow as well as atmospheric stagnation within South Korea. In addition, local industrial activities and the secondary formation of SO4 2- and NO3 - were identified as major sources of PM2.5 in both episodes. However, the impact of industrial emissions was more pronounced in Pollution Episode 1, whereas the secondary formation of NH4 + and the influence of natural sources were predominant only in Pollution Episode 2. Therefore, reducing both industrial emissions in the southeastern region and the formation of secondary inorganic aerosols in the southern region would be practical approaches to improve air quality. The results of this study, which utilized GIS and statistical techniques to analyze regional pollution characteristics, can be further employed to visually and quantitatively identify PM2.5 pollution sources.