• 대한지역사회영양학회지
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• 제12권2호
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• pp.133-141
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• 2007

Minerals play important roles in biological processes. Hair mineral analysis has the advantages of conveniences in sampling and observing the profiles of multiple minerals simultaneously. The purpose of this study was to compare hair mineral contents between urban and local middle school students, and to find out the relation with nutrient intakes and clinical symptoms. Ten nutritional minerals and 7 environmental mineral contents were analyzed in the hair samples of 44 and 37 male students who resided in urban and local area, respectively. And their nutrient intake and clinical symptom score were estimated from questionnaire. The average age, height, and weight were 15.1 years, 168.4 cm, 56.8 kg in urban subjects and 15.4 years, 169.1 cm, 61.9 kg in local subjects, respectively. The residence types were apartment (63.6%) and small-sized apartment (22.7%) in urban subjects and apartment (51.4%) and house (37.8%) in local subjects. The primary water sources were purified water (38.3%), running water (18.2%) in urban subjects and purified water (32.4%), underground water (27.0%) in local subjects, respectively. Daily energy and nutrient intakes were not significantly different between two groups. Among the surveyed 17 clinical symptoms, the scores of constipation, cold, anxieties and total score of clinical symptoms in urban subjects were significantly love. than those in local subjects. Hair contents of Na, Mg, Ca, Cu, V Al, As were significantly higher, while U was lower in urban subjects compared to those in rural subjects. Hair contents of Ni and U were significantly correlated with food intake, and Cr, V, Al, Sb, As, U showed a correlation with energy intake. Hair Mg level showed significantly negative correlation with total clinical symptom score. In conclusion, hair mineral profile between urban and local students was significantly different. And some of these minerals were significantly correlated with food and nutrient intakes. Especially hair magnesium was significantly correlated with total clinical symptom score. Therefore, hair mineral levels would be needed for more systematic study elucidating potentiality as a useful clinical tool.

• 자원환경지질
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• 제39권6호
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• pp.675-687
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• 2006

이 연구에서는 청풍지역 하상퇴적물에 대한 지구화학적 특성 규명을 통해, 주성분원소 및 미량원소에 대한 청풍지역의 자연배경치를 제시하고, 지구화학적 재해에 대해 예견하고자 한다. 이를 위해 물이 흐르고 있는 1차 수계를 대상으로 하상퇴적물시료를 채취하였고, 실험실에서 자연건조 시켰으며, 화학적 분석을 위해 알루미나 몰타르를 이용하여 200메쉬 이하로 분쇄하였다. 주성분원소 및 미량성분원소는 XRD, XRF, ICP-AES, NAA를 이용하여 분석하였다. 청풍지역 하상퇴적물의 기반암에 따른 지질집단별 지구화학적 특성 비교를 위해, 화강암질편마암지역, 메타텍틱편마암지역, 다도응회암지역, 유치역암 지역, 능주용암지역으로 분류하였다. 청풍지역 하상퇴적물 전체에 대한 주성분원소 함량은 $SiO_2\;47.31{\sim}72.81\;wt.%,\;Al_2O_3 \;11.26{\sim}21.88\;wt.%,\;Fe_2O_3\;2.83{\sim}8.39\;wt.%,\;CaO\;0.34{\sim}7.54\;wt.%,\;MgO\; 0.55{\sim}3.59\;wt.%,\;K_2O\;1.71{\sim}4.31\;wt.%,\;Na_2O\;0.56{\sim}2.28\;wt.%,\;TiO_2\;0.46{\sim}1.24\;wt.%,\;MnO\;0.04{\sim}0.27\;wt.%,\;P_2O_5\;0.02{\sim}0.45\;wt.%$이다. 청풍지역 하상퇴적물 전체에 대한 미량성분원소 및 희토류원소 함량은 $Ba\;700ppm{\sim}8990ppm,\;Be\;1.0{\sim}3.50ppm,\;Cu\;6.20{\sim}60ppm,\;Nb\;12{\sim}28ppm,\;Ni\;4.4{\sim}61ppm,\;Pb\;13{\sim}34ppm,\;Sr\;65{\sim}787ppm,\;V\;4{\sim}98ppm,\;Zr\;32{\sim}164ppm,\;Li\;21{\sim}827ppm,\;Co\;3.68{\sim}65ppm,\;Cr\;16.7{\sim}409ppm,\;Cs\;72{\sim}37.1ppm,\;Hf\;4.99{\sim}49.2ppm,\;Rb\;71.9{\sim}649ppm,\;Sb\;0.16{\sim}5.03ppm,\;Sc\;4.97{\sim}5ppm,\;Zn\;26.3{\sim}375ppm,\;Ce\;60.6{\sim}373ppm,\;Eu\;0.82{\sim}6ppm,\;Yb\;0.71{\sim}10ppm$의 범위를 보였다.

• Asian Journal of Atmospheric Environment
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• 제8권3호
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• pp.126-139
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• 2014

In order to estimate the influence of sources on $PM_{2.5}$ in the industrial area of Japan, we carried out a source analysis using chemical component data of $PM_{2.5}$. $PM_{2.5}$ samples were collected intermittently at an industrial area in Japan from July 2010 to November 2012. Water soluble ions ($Cl^-$, $NO_3{^-}$, $SO{_4}^{2-}$, $Na^+$,$NH_4{^+}$, $K^+$, $Mg^{2+}$, $Ca^{2+}$), elements (Al, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, Sb, Pb), and carbonaceous species (OC, EC) of the $PM_{2.5}$ (a total of 198 samples) were analyzed. Positive Matrix Factorization (PMF) model was applied to the data of those chemical components to identify the source of $PM_{2.5}$. At this observation site, nine factors were extracted. The major contributors of $PM_{2.5}$ were secondary sulfate 1, in which loading factors of $SO{_4}^{2-}$ and $NH_4{^+}$ were large (percentage source contribution: 20.9%), traffic, in which loading factors of OC (organic carbon) and EC (elemental carbon) were large (20.8%), secondary sulfate 2, in which loading factors of K and $SO{_4}^{2-}$ were large (8.0%), steel mills (7.8%), secondary chloride and nitrate (7.0%), soil (5.0%), heavy oil combustion (3.8%), sea salt (3.8%), and coal combustion (2.3%). The conditional probability function (CPF) and the potential source contribution function (PSCF) were carried out to examine the influence of a regional source and a broad-based source, respectively. CPF results supported local source influences such as steel mills, sea salt, traffic, coal combustion, and heavy oil combustion. PSCF results suggested that ships in the East China Sea, an industrial area of the east coastal region of China, and an active volcano in the Kyushu region of Japan were potential regional sources of secondary sulfate 1. Secondary sulfate 2 was affected by the burning of biomass fields and by coal combustion in Chinese urban areas such as Beijing, Hebei, and western Inner Mongolia. Source characterization using continuous data from one site showed a potential source representing fossil fuel combustion is affected both by regional and broad-based sources.

• 한국환경과학회지
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• 제23권3호
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• pp.447-457
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• 2014

The number concentrations, the mass concentrations and the elemental concentrations of $PM_{10}$ have measured at Gosan site in Jeju, Korea, from March 2010 to December 2010. And the correlation and the factor analysis for the number, the mass and the elemental concentrations of $PM_{10}$ are performed to identify their relationships and sources. The average $PM_{10}$ number concentration is observed $246\;particles/cm^3$($35.7{\sim}1,017\;particles/cm^3$) and the average $PM_{10}$ mass concentration is shown $50.1{\mu}g/m^3$($16.7{\sim}441.4{\mu}g/m^3$) during this experimental period. The number concentrations are significantly decreased with increasing particle size, hence the concentrations for the smaller particles less than $2.5{\mu}m$($PM_{2.5}$) are contributed 99.6% to the total $PM_{10}$ number concentrations. The highest concentration of the 20 elements in $PM_{10}$ determined in this study is shown by S with a mean value of $1,497ng/m^3$ and the lowest concentration of them is found by Cd with a mean value of $0.57ng/m^3$. The elements in $PM_{10}$ are evidently classified into two group based on their concentrations: In group 1, including S>Na>Al>Fe>Ca>Mg>K, the elemental mean concentrations are higher than several hundred $ng/m^3$, on the other hand, the concentrations are lower than several ten $ng/m^3$ in group 2, including Zn>Mn>Ni>Ti>Cr>Co>Cu>Mo>Sr>Ba>V>Cd. The size-separated number concentrations are shown positively correlated with the mass concentrations in overall size ranges, although their correlation coefficients, which are monotonously increased or decreased with size range, are not high. The concentrations of the elements in group 1 are shown highly correlated with the mass concentrations, but the concentrations in group 2 are shown hardly correlated with the mass concentrations. The elements originated from natural sources have been predominantly related to the mass concentrations while the elements from anthropogenic sources have mainly affected on the number concentrations of $PM_{10}$.

• 한국대기환경학회지
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• 제26권5호
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• pp.543-553
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• 2010

Almost five million citizens a day are using subways as a means of traffic communication in the Seoul metropolitan. As the subway system is typically a closed environment, indoor air pollution problems frequently occurs and passengers complain of mal-health impact. Especially $PM_{10}$ is well known as one of the major pollutants in subway indoor environments. The purpose of this study was to compare the indoor air quality in terms of $PM_{10}$ and to quantitatively compare its source contributions in a Seoul subway platform before and after installing platform screen doors (PSD). $PM_{10}$ samples were collected on the J station platform of Subway Line 7 in Seoul metropolitan area from Jun. 12, 2008 to Jan. 12, 2009. The samples collected on membrane filters using $PM_{10}$ mini-volume portable samplers were then analyzed for trace metals and soluble ions. A total of 18 chemical species (Ba, Mn, Cr, Cd, Si, Fe, Ni, Al, Cu, Pb, Ti, $Na^+$, $NH_4^+$, $K^+$, $Mg^{2+}$, $Ca^{2+}$, $Cl^-$, and ${SO_4}^{2-}$) were analyzed by using an ICP-AES and an IC after performing proper pre-treatments of each sample filter. Based on the chemical information, positive matrix factorization (PMF) model was applied to identify the source of particulate matters. $PM_{10}$ for the station was characterized by three sources such as ferrous related source, soil and road dust related source, and fine secondary aerosol source. After installing PSD, the average $PM_{10}$ concentration was decreased by 20.5% during the study periods. Especially the contribution of the ferrous related source emitted during train service in a tunnel route was decreased from 59.1% to 43.8% since both platform and tunnel areas were completely blocked by screen doors. However, the contribution of the fine secondary aerosol source emitted from various outside combustion activities was increased from 14.8% to 29.9% presumably due to ill-managed ventilation system and confined platform space.

• 한국대기환경학회지
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• 제24권4호
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• pp.439-454
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• 2008

The purpose of this study was to extensively identify $PM_{10}$ sources and to estimate their contributions to the study area, based on the analysis of the $PM_{10}$ mass concentration and the associated inorganic elements, ions, and total carbon. The contribution of $PM_{10}$ sources was estimated by applying a receptor method because identifying air emission sources were effective way to control the ambient air quality. $PM_{10}$ particles were collected from May to November 2007 in the Yongin-Suwon bordering area. $PM_{10}$ samples were collected on quartz filters by a $PM_{10}$ high-volume air sampler. The inorganic elements (Al, Mn, V, Cr, Fe, Ni, Cu, Zn, Cd, Pb, Si, Ba, Ti and Ag) were analyzed by an ICP-AES after proper pre-treatments of each sample. The ionic components of these $PM_{10}$ samples ($Cl^_$, $NO_3^-$, $SO_4^{2-}$, $Na^+$, $NH_4^+$, $K^+$, $Ca^{2+}$, and $Mg^{2+}$) were analyzed by an IC. The carbon components (OC1, OC2, OC3, OC4, OP, EC1, EC2 and EC3) were also analyzed by DRI/OGC analyzer. Source apportionment of $PM_{10}$ was performed using a positive matrix factorization (PMF) model. After performing PMF modeling, a total of 8 sources were identified and their contribution were estimated. Contributions from each emission source were as follows: 13.8% from oil combustion and industrial related source, 25.4% from soil source, 22.1% from secondary sulfate, 12.3% from secondary nitrate, 17.7% from auto emission including diesel (12.1%) and gasoline (5.6%), 3.1% from waste incineration and 5.6% from Na-rich source. This study provides information on the major sources affecting air quality in the receptor site, and therefore it will help us maintain and manage the ambient air quality in the Yongin-Suwon bordering area by establishing reliable control strategies for the related sources.

• 한국대기환경학회지
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• 제29권1호
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• pp.74-85
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• 2013

Since the underground transportation system is a closed environment, indoor air quality problems may seriously affect many passengers' health. The purpose of this study was to understand $PM_{10}$ characteristics in the underground air environment and further to quantitatively estimate $PM_{10}$ source contributions in a Seoul Metropolitan subway station. The $PM_{10}$ was intensively collected on various filters with $PM_{10}$ aerosol samplers to obtain sufficient samples for its chemical analysis. Sampling was carried out in the M station on the Line-4 from April 21 to 28, July 13 to 21, and October 11 to 19 in the year of 2010 and January 11 to 17 in the year of 2011. The aerosol filter samples were then analyzed for metals, water soluble ions, and carbon components. The 29 chemical species (OC1, OC2, OC3, OC4, CC, PC, EC, Ag, Al, Ba, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Si, Ti, V, Zn, $Cl^-$, $NO_3{^-}$, $SO_4{^{2-}}$, $Na^+$, $NH_4{^+}$, $K^+$, $Mg^{2+}$, $Ca^{2+}$) were analyzed by using ICP-AES, IC, and TOR after proper pretreatments of each sample filter. Based on the chemical information, positive matrix factorization (PMF) model was applied to identify the $PM_{10}$ sources and then six sources such as biomass burning, outdoor, vehicle, soil and road dust, secondary aerosol, ferrous, and brakewear related source were classified. The contributions rate of their sources in tunnel are 4.0%, 5.8%, 1.6%, 17.9%, 13.8% and 56.9% in order.

• 한국대기환경학회지
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• 제22권5호
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• pp.590-603
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• 2006

Size-and time-resolved aerosol samples were collected using an eight-stage Davis rotating unit for monitoring (DRUM) sampler from 23 March to 29 April 2001 at Gosan, Jeju Island, Korea, which is one of the super sites of Asia-Pacific Regional Aerosol Characterization Experiment(ACE-Asia). These samples were analyzed using synchrotron X-ray fluorescence for 3-hr average concentrations of 19 elements including Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Br, Rb, and Pb. The size-resolved data sets were then analyzed using the positive matrix factorization(PMF) technique to identify possible sources and estimate their contributions to particulate matter mass. PMF analysis uses the uncertainty of the measured data to provide an optimal weighting. Twelve sources were resolved in eight size ranges($0.09{\sim}12{\mu}m$) and included continental soil, local soil, sea salt, biomass/biofuel burning, coal combustion, oil combustion, municipal incineration, nonferrous metal source, ferrous metal source, gasoline vehicle, diesel vehicle, and volcanic emission. The PMF result of size-resolved source contributions showed that natural sources represented by local soil, sea salt, continental soil, and volcanic emission contributed about 79% to the predicted primary particulate matter(PM) mass in the coarse size range ($1.15{\sim}12{\mu}m$) while anthropogenic sources such as coal combustion and biomass/biofuel burning contributed about 58% in the fine size range($0.56{\sim}2.5{\mu}m$). The diesel vehicle source contributed mostly in ultra-fine size range($0.09{\sim}0.56{\mu}m$) and was responsible for about 56% of the primary PM mass.

• 청정기술
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• 제23권2호
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• pp.158-162
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• 2017

철, 구리, 알루미늄, 니켈 등의 금속을 에칭하기 위한 에칭액으로 $FeCl_3$ 용액이 사용되며, 에칭 과정에서 $Fe^{3+}$가 $Fe^{2+}$로 환원되면서 에칭속도를 저하시키고, 에칭효율이 감소하게 된다. 또한 에칭 후 발생하는 염화철 에칭폐액은 환경적, 경제적으로 문제를 지니기 때문에 에칭액을 재생하여 재사용 할 필요가 있다. 본 연구에서는 $FeCl_2$ 용액에 HCl을 첨가한 후, 산화제로 $H_2O_2$, $NaClO_3$를 첨가하여 용액 내 $Fe^{2+}$를 산화시켰으며, 산화과정에서 산화-환원전위(ORP)와 산화율간의 관계를 조사하였다. ORP는 $H_2O_2$와 $NaClO_3$의 농도가 증가함에 따라 증가하였으며, 산화가 진행되면서 점차 감소하여 산화가 완료된 후에 일정한 ORP를 유지하였다. Nernst 식과 일치하는 결과를 보였다. 또한 충분한 양의 HCl 및 $H_2O_2$, $NaClO_3$를 첨가하였을 경우, 약 99% 이상 산화가 이루어짐을 알 수 있었다.

• 한국식품과학회지
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• 제37권4호
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• pp.542-548
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• 2005

대나루(왕대)를 여러 가지 방법으로 추출하고 그 추출액의 화학 성분을 분석하였다. 대나무 추출액의 pH는 5.0-5.5였고, 가용성고형분과 총페놀 함량은 알코올 추출액이 각각 $13.7^{\circ}Brix$와 0.42%로 가장 높았으며, 증발잔사는 직접가열 추출액이 3.8%로 가장 높았다. 무기성분은 K이 가장 높은 함량을 보였으며, 추출방법에 따라서는 간접가열이 가장 높은 무기물 함량을 나타내었다. 중금속은 Al, Cr, Cu, Mn, Ni, Pb, Zn, 등이 검출되었으며, 유해중금속 중 As, Cd, Hg는 검출되지 않았고 Cr, Pb는 극미량 검출되었다. 폴리페놀은 catechin, chlorogenic acid, caffeic acid, 3-hydroxy benzoic acid, ferulic acid등 5종이 확인되었으며 이중 3-hydroxy benzoic acid와 catechin이 주종을 이루는 것으로 나타났다. 유리당은 galactose, glucose, fructose 및 sucrose가 검출되었으며 glucose와 sucrose의 함량이 가장 높았다. 유기산은 citric acid, tartaric acid, malic acid, succinic acid, acetic acid 등이 검출되었으나 직접가열 추출액은 tartaric acid가, 물 추출액과 알코올 추출액은 citric acid, tartaric acid 및 succinic acid가 검출되지 않았다. 항산화 활성은 간접가열 추출액과 알코올 추출액이 상업용 항산화제인 BHA와 ${\delta}-tocopherol$ 보다 우수하였다. 아질산염소거능은 pH 1.2와 3.0에서 효과가 우수하였으나 pH 6.0에서는 급격히 감소하였다. Tyrosinase 저해활성은 물 추출액이 가장 높았으며, SOD 유사활성과 ACE 저해활성은 간접가열 추출액이 가장 높았다. 식품 부패균에 대한 항균활성은 Bacillus subtilis, E. coli O157, Staphylococcus aureus에 대하여 높은 것으로 나타났다.