Source apportionment of PM2.5 using various receptor models in Daebu Island, Korea
수용모델을 통한 대부도 지역 대기 중 PM2.5의 오염원 추정

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보건대학원 환경보건학과
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서울대학교 보건대학원
Fine particulate matter (PM2.5)Source apportionmentPositive Matrix Factorization (PMF)Conditional Probability Function (CPF)Weighted Potential Source Contribution Function (PSCF)
학위논문 (석사)-- 서울대학교 보건대학원 환경보건학과, 2017. 8. 이승묵.

Source apportionment of PM2.5 using various receptor models

Sun-Hye Kim
Department of Environmental Health Sciences
Graduate School of Public Health
Seoul National University

Chemical characteristics of PM2.5 play important roles in determining its effect on climate change and human health. Because of its chemical complexity which reflects properties of various environments, characterization of chemical species in PM2.5 help trace back where it has been originated. Receptor models have been used as one of source apportioning methods with a chemically speciated data set. As several kinds of literature recommended combining various receptor models to make sure robust source identification results, several receptor model results were discussed with filter-based PM2.5 data in this study.
For the source apportionment of PM2.5 using Positive Matrix Factorization (PMF) were used. A total of 83 samples were collected from May 21 to November 1 in 2016. The average PM2.5 mass concentration was 26.2 ± 14.5 µg m-3 with the highest concentration in May (46.5 ± 14.7 µg m-3) and the lowest concentration in August (18.6 ± 8.1 µg m-3). During the sampling period, potassium (K) and sulfate (SO42-) showed the highest concentration of trace metals and ion species, respectively. The average OC/EC ratio observed in this study indicated the high possibility of formation in Secondary Organic Aerosols (SOAs) around the sampling site. Carbonaceous compounds in PM2.5 was characterized with high water soluble organic carbon to organic carbon ratio (WSOC/OC) in a summer, indicating frequent photo-chemical reactions. For the speciation of individual organic compounds, a total number of 38 samples were gathered through a high-volume air sampler from May 27 to October 30 in 2016. The average sum of individual organic compounds was 116.05 ± 66.19 ng m-3, accounting for 1.97% of the average organic carbon (OC) concentration. Dicarboxylic acids (DCAs) concentration was highest (78.75 ± 57.12 ng m-3) of the average sums followed by n-Alkanoic acids (26.26 ± 9.28 ng m-3), n-Alkanes (10.02 ± 7.46 ng m-3), Sugars (0.54 ± 0.01 ng m-3), and PAHs (0.48 ± 0.42 ng m-3).
In total, nine sources were identified using PMF, which were Secondary Sulfate (29.0%), Mobile (22.0%), Secondary Nitrate (13.2%), Oil combustion (10.1%), Coal combustion (9.4%), Aged Sea Salt (7.9%), Soil (5.6%), Non-ferrous Smelter (1.7%) and Industrial Activities (1.1%). From hybrid receptor models results, high contributions of secondary aerosols from east coastal regions of China was suggested while other expected sources were originated from the industrial complex in inland areas of South Korea or Shandong peninsula in China. The analysis results of organic compounds were added with 38 samples to perform Principal Component Analysis (PCA). Six factors from PCA were Secondary Organic Aerosols 1, SOAs 1 (38.568%), Combustion related sources (20.170%), Secondary Organic Aerosols 2, SOAs 2 (10.191%), Secondary inorganic factor (7.434%), Biomass burning (5.833%), and Industrial sources (4.455%).
Both of two receptor model results indicated that elevated PM2.5 concentrations observed in Daebu Island were mainly attributable to secondary aerosols and combustion sources. Secondary aerosol compounds were mostly from long-range transport from China, whereas combustion sources were from various regions in North Korea, China, and highly contributed in Industrial regions of South Korea.
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Graduate School of Public Health (보건대학원)Dept. of Environmental Health (환경보건학과)Theses (Master's Degree_환경보건학과)
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