서울 PM2.5와 PM1.0의 오염원 추정과 산화 잠재력 평가

학위논문(석사) -- 서울대학교대학원 : 보건대학원 환경보건학과, 2022. 8. 이승묵.Since PM1.0 is mainly emitted from anthropogenic processes and contributes greatly to the health effects of PM2.5, the need for research into PM1.0 as well as PM2.5 is growing. In this study, the constituents of PM2.5 and PM1.0 in Seoul were analyzed and the oxidative potential was measured by dithiothreitol (DTT) assay. The sources were identified by positive matrix factorization (PMF) and their characteristics were compared by conditional bivariate probability function (CBPF), cluster analysis, and potential source contribution function (PSCF). In the average mass concentration of 123 samples collected in Seoul, PM1.0 (15.1 µg/m3) accounted for about 75% of PM2.5 (20.1 µg/m3). This indicates that secondary sources and combustion-related sources mainly contribute to PM2.5. The organic carbon (OC), SO42-, and NH4+ fractions were significantly higher in PM1.0 than in PM2.5. For the crustal elements, the fraction was significantly higher in PM2.5 than in PM1.0. In the result of the PMF model, ten sources contributed to PM2.5 and PM1.0, and each source and its contribution (µg/m3) were as follows (PM2.5, PM1.0). Secondary nitrate: 6.01 (29%), 5.23 (32%); Secondary sulfate: 3.64 (17%), 3.48 (22%); Mobile: 2.71 (13%), 1.81 (11%); Biomass burning: 2.69 (13%), 2.03 (13%); Incinerator: 0.81 (3.8%), 0.69 (4.3%); Soil: 0.61 (2.9%), 0.30 (1.9%); Industry: 1.65 (7.8%), 0.40 (2.5%); Coal combustion: 1.77 (8.4%), 1.22 (7.6%); Oil combustion 0.40 (1.9%), 0.35 (2.2%); Aged sea salt: 0.72 (3.4%), 0.64 (4.0%). The fractional contributions (%) of secondary sources (secondary nitrate and secondary sulfate) in PM1.0 were higher than in PM2.5. For industry and soil sources, the fractional contributions were higher in PM2.5 than in PM1.0. In mobile source, there was a difference in constituents by road dust. The CBPF plots showed the direction of sources around Seoul. These plots showed that many sources were influenced from industrial complexes located in the south and the west of Seoul. For the cluster analysis, the contribution of biomass burning increased when backward trajectories flowed through Manchuria and North Korea. In the cluster flowing from Shandong Province, the contribution of secondary sources increased. Also, in PSCF, North China Plain including Shandong Province was mainly indicated as a possible source area of secondary sources, and the contributions of these sources increased significantly when high concentration events (HCEs) occurred. In particular, secondary sulfate from North China Plain contributed greatly to PM1.0 when HCEs occurred during seasonal management period (SMP). The DTTv of PM2.5 and PM1.0 were 0.611 nmol/min/m3 and 0.588 nmol/min/m3, respectively. PM1.0 contributed mostly to the oxidative potential of PM2.5. In Pearson correlation analysis, OC showed the highest correlation with DTTv (PM2.5: r=0.873, PM1.0: r=0.786). By the multiple linear regression, secondary nitrate and biomass burning were selected as variables to represent DTTv in both PM2.5 and PM1.0. In this result, biomass burning was an important source related to oxidative potential and secondary nitrate showed the influence of secondary formation process. This study showed that the continuous studies of PM1.0 were necessary to understand the characteristics of sources and oxidative potential, and showed that management of secondary sources and biomass burning source in Seoul was necessary.PM1.0은 인위적 과정에서 주로 배출되고 PM2.5의 건강 영향에 대부분을 차지하기 때문에 PM2.5뿐만 아니라 PM1.0에 대한 연구의 필요성은 커지고 있다. 본 연구에서는 서울의 PM2.5와 PM1.0의 성분을 분석하고 dithiothreitol (DTT) 분석을 통해 산화 잠재력을 평가하였다. 또한, positive matrix factorization (PMF)을 통해 오염원을 추정하였고 conditional bivariate probability function (CBPF), cluster analysis, potential source contribution function (PSCF)를 통해 오염원들의 특징을 비교하였다. 서울에서 채취한 123개 시료의 평균 질량농도에서 PM1.0 (15.1 µg/m3)이 PM2.5 (20.1 µg/m3)의 약 75%를 차지하였다. 이는 이차 생성과 연소관련 오염원이 PM2.5에 크게 기여하는 것을 나타낸다. Organic carbon (OC), SO42-, NH4+는 PM1.0에서 유의하게 큰 비율을 차지하고 있었고 지각 성분의 비율은 PM2.5에서 유의하게 컸다. PMF 결과 10개의 오염원이 기여했으며, 각각의 오염원과 기여도(µg/m3)는 다음과 같다(PM2.5, PM1.0). 이차 질산염: 6.01 (29%), 5.23 (32%); 이차 황산염: 3.64 (17%), 3.48 (22%); 자동차: 2.71 (13%), 1.81 (11%); 생물성연소: 2.69 (13%), 2.03 (13%); 소각: 0.81 (3.8%), 0.69 (4.3%); 토양: 0.61 (2.9%), 0.30 (1.9%); 산업: 1.65 (7.8%), 0.40 (2.5%); 석탄연소: 1.77 (8.4%), 1.22 (7.6%); 기름연소: 0.40 (1.9%), 0.35 (2.2%); 노후 해염: 0.72 (3.4%), 0.64 (4.0%). 이차 생성 오염원(이차 질산염과 이차 황산염)은 PM1.0에서 더 큰 기여도 비율을 차지했으며, 산업과 토양 오염원의 기여도 비율은 PM2.5에서 더 높았다. 자동차 오염원에서는 도로 먼지로 인한 성분의 차이가 나타났다. CBPF는 서울 주변의 오염원 방향을 잘 나타내고 있었으며 많은 오염원들이 남쪽과 서쪽에 위치한 산업단지의 영향을 받는 것으로 나타났다. 클러스터 분석에서는 역궤적이 만주와 북한을 통해 유입될 때 생물성연소의 기여도가 높아졌고, 산둥성에서 유입되는 경우 이차 생성 오염원의 기여도가 증가했다. PSCF 결과에서도 주로 산둥성을 포함한 North China Plain이 이차 생성 오염원의 오염원 가능지역으로 나타났고 이 오염원들은 고농도 사례 시 기여도가 유의하게 증가하였다. 특히, North China Plain으로부터의 이차 황산염은 계절관리제기간 동안 고농도 사례 시 PM1.0에 크게 기여했다. PM2.5와 PM1.0의 DTTv (nmol/min/m3)는 각각 0.611, 0.588로 PM2.5의 산화 잠재력의 대부분에 PM1.0이 기여했다. Pearson 상관 분석에서 OC가 DTTv와 가장 높은 상관성을 보였다(PM2.5: r=0.873, PM1.0: r=0.786). 다중 회귀분석에서 이차 질산염과 생물성연소는 PM2.5와 PM1.0에서 모두 DTTv를 설명하는 변수로 선택되었다. 이 결과에서 생물성연소는 산화 잠재력과 관련된 중요한 오염원이었고 이차 질산염은 이차 생성 과정의 영향을 나타냈다. 본 연구는 오염원과 산화 잠재력의 특성을 파악하기 위한 지속적인 PM1.0 연구의 필요성을 보여주었고, 서울에서 이차 생성과 생물성연소 오염원 관리의 필요성을 나타냈다.1. Introduction 1 2. Method 3 3. Results and Discussion 10 4. Summary and Conclusion 45 References 49 Supplementary 60 Abstract in Korean 67석

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We search for a five-plet singly charged Higgs $H_5^\pm$ in the Georgi-Machacek model at the ep colliders. The charged Higgs bosons are produced via the $ZW^{\pm}$ fusion process $p\, e^- \to j\, e^-\, H_5^\pm$, and decay as $H_5^\pm \to Z\, W^{\pm} \to (l^+ l^-)\, (jj)$. With a detector-level simulation at the FCC-eh and LHeC, a multi-variate analysis is performed to yield limits on the production cross section times branching ratio $\sigma (p\, e^- \to j e^- H_5^\pm) \times {\rm BR}(H_5^\pm \to Z\, W^\pm)$ and on the model parameter $\sin\theta_H$ for charged Higgs masses between 200 and 1000 GeV. The effects of electron beam polarization are also investigated.Comment: 7 pages, 6 figure