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Source Apportionment of Organic Carbons in PM2.5 using the Positive Matrix Factorization model

Title
Source Apportionment of Organic Carbons in PM2.5 using the Positive Matrix Factorization model
Authors
강민경
Issue Date
2021
Department/Major
대학원 환경공학과
Publisher
이화여자대학교 대학원
Degree
Master
Advisors
이지이
Abstract
Particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5), which is known to have adverse health effects, hinders visibility and affects climate change. In chemical composition of PM2.5, organic carbon (OC) is one of the most dominant components of PM2.5 mass concentration. OC is composed of primary organic carbon (POC) and secondary organic carbon (SOC), which is generated by chemical reactions of volatile organic compounds (VOCs) in the ambient air. Previous studies have introduced methods to characterize secondary organic aerosol (SOA) by estimating SOA quantity with SOA tracers specific to each precursor VOCs. Recently, several studies have adopted receptor model, positive matrix factorization (PMF) model in specific, to separate source profiles and calculate contributions of each factor. In this study, organic species were extracted from PM2.5 samples collected on a quartz fiber filter 2018 Seoul. Among the organic species, Polycyclic Aromatic Hydrocarbon (PAHs), n-alkane, Dicarboxylic Acid (DCA), Fatty Acid(FA) and Sugar compounds were quantified through authentic standards then put in the PMF model as an input data. Of the factors and contributions resulted from PMF modeling, ones that were associated with secondary formation processes were separately characterized with identified and semi-quantified SOA tracers of each precursor VOCs, to clarify the seasonal characteristics of SOA during one year from 2018 to 2019 at Seoul, Korea. 7 factor profiles and their contributions were separated from the original input data. The factors were named by observing the marker organic species and atmospheric conditions—Cooking emission, biomass burning, solid fuel burning, liquid fuel burning, biogenic emission, mixture of primary combustion and SOA formation. Mixture of primary combustion and secondary formation and SOA formation are both related to secondary formation in PMF results. By comparing them to response ratio of identified SOA tracers, we found that the factor of “mixture of primary combustion” and “secondary formation”, primary and secondary sources were inseparable and only observable in the winter while the factor for SOA formation was purely secondary formation and remarkable in the winter, spring and summer with different precursor VOCs contributing to its secondary formation. From PMF modeling with SOA tracers with correlation and time series analysis of these factors and SOA tracers, we concluded that contribution of each precursor for VOCs might be different for ‘mixture of primary combustion and secondary formation’ and ‘SOA formation’. The factor of mixture of primary combustion and secondary formation is related to the isoprene and β-caryophyllene SOA tracers, while, the SOA formation was highly connected with the toluene, α-pinene and β-caryophyllene SOA tracers. ;건강, 시정 거리에 악영향을 미치며, 기후변화에 영향을 주는 것으로 알려진 대기 중 미세먼지, PM2.5 는 한국을 포함한 동북아시아 지역에서 심각한 대기오염을 보이며, 지정된 대기환경 기준치를 빈번히 초과해오고 있다. PM2.5 는 다양한 화학물질의 집합으로 이루어져 있는데 그 중 유기탄소 (Organic Carbon, OC) 가 높은 비율을 차지하고 있다. 유기탄소는 다시 일차유기탄소 (Primary Organic Carbon, POC) 와 이차유기탄소 (Secondary Organic Carbon, SOC) 로 나눌 수 있으며, 이차유기탄소의 경우 휘발성유기화합물질 (Volatile Organic Compounds, VOCs)의 대기 중 화학 반응으로 생성되는 유기탄소를 일컫는다. 기존에 연구에서는 이차유기에어로졸 (Secondary Organic Aerosol, SOA) 의 특징을 분석하고자 각 전구체 VOCs 으로부터 특징적으로 생성되는 SOA tracer 를 사용하여 SOA 양을 추정하는 방식을 사용해 왔다. 그러나 최근의 연구에서는 수용모델, 특히 EPA 에서 개발한 Positive Matrix Factorization (PMF) 모델이 이용되고 있으며, 이를 통하여 OC 오염원 프로파일과 기여도를 결과로 제시하곤 한다. 본 연구에서는 2018년 서울에서 계절별로 필터에 채취한 PM2.5 시료에서 유기성분을 추출하였다. 그 중 Polycyclic Aromatic Hydrocarbon (PAHs), n-alkane, Dicarboxylic Acid (DCA), Fatty Acid(FA) 및 Sugar 성분은 정량화를 진행하여 PMF 모델의 입력자료로 사용하였고, 이를 통하여 OC 오염원을 분리하고 각 오염원 별 기여도를 계산하였다. 오염원 프로파일 중, 이차생성과 관련된 것은 기여도를 개별 전구물질 VOCs 의 SOA tracer 들과 시계열 및 상관성 분석을 진행하여 2018년 계절별 SOA 특징을 밝혔다.
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