• 한국대기환경학회지
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• 제25권2호
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• pp.154-166
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• 2009

In this study, 1-hour integrated $PM_{2.5}$ mass and chemical composition concentrations were monitored at the St. Louis-Midwest Supersite in Illinois. Time-resolved samples were collected one week in each of June 2001 (22 June to 28 June), November 2001 (7 November to 13 November), and March 2002 (19 March to 25 March). A total of 427 samples were collected by CAMM (continuous ambient mass monitor) and 15 compounds were analyzed by AAS, PILS (particle-into-liquid sampler), and TOT (thermal optical transmittance) method. PMF was applied to identify the sources and apportion the $PM_{2.5}$ mass to each source for highly time resolved data. In addition, the nonparametric regression (NPR) was applied to identify the predominant directions of local sources relative to wind direction. Also, this study performed compare the NPR analysis and location of actual local point sources at the St. Louis area. The PMF modeling identified nine sources and the average mass was apportioned to gasoline vehicle, road dust, zinc smelter, copper production, secondary sulfate, diesel emission, secondary nitrate, iron+steel, and lead smelter, respectively. These results suggested that this study results will be help for $PM_{2.5}$ source apportionment studies at similar metropolitan area, establish $PM_{2.5}$ standard, and establish effective emissions reduction strategies in Korea.

• 한국대기환경학회지
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• 제11권2호
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• pp.179-184
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• 1995

This study was carried out to characterize sources of suspended particulate matter(SPM) in Mt. Soback area from January to Novembver in 1993. The collection and major water soluble ion analysis of SPM were conducted by using a High Volumn Air Sampler(HVAS; W&A Inc., PM-10) and ion chromatograph(DIONEX 4000i), respectively. The variations of SPM and major ion concentrations were found to be 9. sim. 156.mu.g/ $m^{3}$, $F^{-10}$ 0.00 .sim. 0.15.mu.g/ $m^{3}$, C $l^{-10}$ 0.06 .sim. 3.79.mu.g/ $m^{3}$, N $O_{3}$$^{-10}$ 0.90 .sim. 6.85.mu.g/ $m^{3}$, S $O_{4}$$^{2-}$ 1.99 .sim. 9.36.mu.g/ $m^{3}$ N $a^{+}$0.00 .sim. 0.27.mu.g/ $m^{3}$, N $H_{4}$ $^{+}$0.72 .sim. 5.77.mu.g/ $m^{3}$, $k^{+}$0.03 .sim. 0.88.mu.g/ $m^{3}$ and $Ca^{2+}$0.12 .sim. 2.76.mu.g/ $m^{3}$. Tree sources were identified by Principal Component Amalysis(PCA) using a SPSS/P $C^{+}$. The explanation ability of forst, second and third Principal Component were 60.8%, 13.6%, 8.2%, of total variance. The sources classfied by PCA were found to be secondary aerosol/fuel combustion, soil dust related cement production/yellow sand and aerosol related waste burning.related waste burning.g.

• 한국환경과학회지
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• 제26권11호
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• pp.1297-1306
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• 2017

This study identified physical characteristics and aerosol particle sources of $PM_{10}$ and $PM_{2.5}$ in the industrial complex of Busan Metropolitan City, Korea. Samples of $PM_{10}$, $PM_{2.5}$ and also soil, were collected in several areas during the year of 2012 to investigate elemental composition. A URG cyclone sampler was used for collection. The samples were collected according to each experimental condition, and the analysis method of SEM-EDX was used to determine the concentration of each metallic element. The comparative analysis indicated that their mass concentration ranged from 1% to 3%. The elements in the industrial region that were above 10% were Si, Al, Fe, and Ca. Those below 5% were Na, Mg, and S. The remaining elements (1% of total mass) consisted of elements such as Ni, Co, Br and Pb. Finally, a statistical tool was applied to the elemental results to identify each source for the industrial region. From a principal components analysis (SPSS, Ver 20.0) performed to analyze the possible sources of $PM_{10}$ in the industrial region, five main factors were determined. Factor 1 (Si, Al), which accounted for 15.8% of the total variance, was mostly affected by soil and dust from manufacturing facilities nearby, Factors 2 (Cu, Ni), 3 (Zn, Pb), and 4 (Mn, Fe), which also accounted for some of variance, were mainly related to iron, non-ferrous metals, and other industrial manufacturing sources. Also, five factors determined to access possible sources of $PM_{2.5}$, Factor 1 (Na, S), accounted for 13.5% of the total variance and was affected by sea-salt particles and fuel incineration sources, and Factors 2 (Ti, Mn), 3 (Pb, Cl), 4 (K, Al) also explained significant proportions of the variance. Theses factors mean that the $PM_{2.5}$ emission sources may be considered as sources of incineration, and metals, and non-ferrous manufacturing industries.

• 한국지구과학회지
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• 제24권4호
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• pp.315-324
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• 2003

서울시에서 설치하여 운영중인 대기질 측정소의 입자상물질을 대표하는 PM$_{2.5}$, PM$_{10}$, TSP와 황사기간 중 고용량고기포집기로 채취한 먼지성분을 분석, 평가하였다. 1990년도부터 2002년 11월까지 서울에서 관측된 황사일수는 2000년 이후 발생빈도가 증가하였으며 황사지속시간도 길어지는 경향을 보였다. PM$_{10}$/TSP 비율은 황사시 2000년, 2001년도에 각각 52.9%, 59.4%로 비황사시에 비해 PM$_{10}$의 비율이 약 10% 정도 낮은 것으로 미루어 황사시 10 ${\mu}$m이상의 입경이 큰 입자 영향이 컸던 반면에 2002년 황사시에는 PM$_{10}$의 영향이 오히려 크게 나타나 PM$_{10}$이 TSP 중의 71.4%에 달하였다. 황사가 전체 먼지농도에 미치는 기여율은 2002년도에 PM$_{2.5}$ 11.9%, PM$_{10}$ 23.1%, TSP 19%로 가장 높은 기여도를 보여 황사가 전체 면지농도에 미치는 영향이 매우 크다는 것을 알 수 있었다.

• 한국대기환경학회지
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• 제28권5호
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• pp.538-555
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• 2012

The purpose of this study was to understand $PM_{2.5}$ chemical characteristics on the Suwon/Yongin area and further to quantitatively estimate $PM_{2.5}$ source contributions. The $PM_{2.5}$ sampling was carried out by a high-volume air sampler at the Kyung Hee University-Global Campus from November, 2010 to October, 2011. The 40 chemical species were then analyzed by using ICP-AES(Ag, Ba, Cr, Cu, Fe, Mn, Ni, Pb, Si, Ti, V and Zn), IC ($Na^+$, $K^+$, $NH_4{^+}$, $Mg^{2+}$, $Ca^{2+}$, $NO_3{^-}$, ${SO_4}^{2-}$ and $Cl^-$), DRI/OGC (OC1, OC2, OC3, OC4, OP, EC1, EC2 and EC3) and GC-FID (acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benzo[a]anthracene, benzo[b]fluoranthene, benzo[a] pyrene, indeno[1,2,3-cd] pyrene, benzo[g,h,i]perylene and dibenzo[a,h,]anthracene). When applying PMF model after performing proper data treatment, a total of 10 sources was identified and their contributions were quantitatively estimated. The average contribution to $PM_{2.5}$ emitted from each source was determined as follows; 26.3% from secondary aerosol source, 15.5% from soil and road dust emission, 15.3% from vehicle emission, 15.3% from illegal biomass burning, 12.2% from incineration, 7.2% from oil combustion source, 4.9% from industrial related source, and finally 3.2% from coal combustion source. In this study we used the ratios of PAHs concentration as markers to double check whether the sources were reasonably classified or not. Finally we provided basic information on the major $PM_{2.5}$ sources in order to improve the air quality in the study area.