• 한국응용과학기술학회지
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• 제33권4호
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• pp.824-835
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• 2016

2015년 기준 자동차 등록대수는 약 2,100만대를 넘어 1가구당 1.07대를 보유하고 있는 실정이나[1], 국내 자동차용 표준연료에 대한 기준은 부재한 상황이다. 자동차용 표준연료(reference fuel)는 차량의 연비와 배출가스를 인증하거나 새로운 자동차를 개발할 때 차량의 성능 등을 평가하기 위해 사용하는 연료를 의미한다. 현재 국내에는 차량의 배출가스, 성능, 연비시험 등을 위해 유통연료를 사용하고 있으며, 유통연료는 석유 및 석유대체연료사업법과 대기환경보전법 상의 품질기준을 만족하지만 각 제조사의 원료와 공정 등에 따라 연료의 물성차이가 있어 차량 시험 시 편차가 발생할 수 있다. 본 연구에서는 국내 유통되는 휘발유 품질모니터링 분석결과를 바탕으로 표준연료 기준(안)을 설정하고, GDI와 MPI 연료 분사 방식의 차량에 적용하여 비교 평가한 결과, 실제 유통연료를 사용했을 때 최대 3.8%까지 발생하는 연비차이가 1.1%까지 감소함을 확인할 수 있었다.

• 한국입자에어로졸학회지
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• 제7권1호
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• pp.31-44
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• 2011

The hourly volatile organic compounds(VOCs) concentrations between 2005 and 2008 at Bulgwang photochemical assessment monitoring station were investigated to establish a method for quality assurance and quality control(QA/QC) procedure. Systematic error, erratic error, and random error, which was manifested by outlier and highly fluctuated data, were checked and removed. About 17.3% of the raw data were excluded according to the proposed QA/QC procedure. After QA/QC, relative standard deviation for representing 15 species concentrations decreased from 94.7-548.0% to 63.4-125.8%, implying the QA/QC procedure is proper. For further evaluation about the adequacy of QA/QC procedure, principal components analysis(PCA) was carried out. When the data after QA/QC procedure was used for PCA, the extracted principal components were different from the result from the raw data and could logically explain the major emission sources(gasoline vapor, vehicle exhaust, and solvent usage). The QA/QC procedure based on the concept of errors is inferred to proper to be applied on VOCs. However, an additional QA/QC step considering the relationship between species in the atmosphere needs to be further considered.

• 한국대기환경학회지
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• 제19권4호
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• pp.387-396
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• 2003

A field study was conducted during the summer time of 2002 to determine compositions of volatile organic compounds (VOCs) emitted from vehicles and to develop source emission profiles that is applied to CMB model to estimate the source contribution of certain area. Source emission profile is widely used for the estimation of source contribution by the chemical mass balance model and have to be developed applicable for the target area of estimation. This study was aimed to develop source emission profile and estimation of source contribution of VOCs after application of the chemical mass balance (CMB) receptor model. After considering the emission inventory and other research results for the VOCs in Seoul, Korea, the sources like vehicle emission (tunnel), gas station (gasoline, diesel), solvent usage (painting operation, dry cleaning, graphic art), and gas fuels were selected for the major VOCs sources. Furthermore, ambient air samples were simultaneously collected from 09:00 to 11:00 for four days at eight different official air quality monitoring sites as receptors in Seoul during summer of 2001. Source samples were collected by canisters, and then about seventy volatile organic compounds were analyzed by gas chromatography with flame ionization detector (GC/FID). Based on both the developed source profiles and the database of the receptors, CMB model was intensively applied to estimate mass contribution of VOCs sources. Examining the source profile from the vehicle, the portion of alkanes of VOCs was highest, and then the portion of aromatics such toluene, m/p-xylene were followed. In case of gas fuel. they have their own components; the content of butane, propane, ethane was higher than any other component according to the fuel usage. The average of the source apportionment on VOCs for 8 sites showed that the major sources were vehicle emission and gas fuels. The vehicle emission source was revealed as having the highest contribution with an average of 49.6%, and followed by solvent with 21.3%, gas fuel with 16.1%, gasoline with 13.1%.

• 한국환경독성학회:학술대회논문집
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• 한국환경독성학회 2003년도 추계국제학술대회
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• pp.27-27
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• 2003

Lake Texoma is located on the border of southern Oklahoma and northern Texas. It has 93,000 surface acres, and is a focus of the recreation, and farming industries in the region. There are potential stressors around the Lake Texoma watershed that may cause adverse ecological effects in the lake. System assimilative capacity (SAC) is the ability of abiotic and biotic processes to atteuniate the stressors. SAC Exceeded indicates potential of occuring adverse eco-effects. A number of representative chemical release sites and stressor sources in the surrounding watershed were characterized, and several impact sites having stressors sources, such as being near agriculture, landfills, housing areas, oil production fields and heavy use recreational activity, were selected for surface water, sediment, and groundwater monitoring. A paired reference site, having similar physical characteristics as its impact site, was also chosen based on its proximity to the impact site. Lake water samples were collected at locations identified as marina entrance, gasoline filling station, and boat dock at five marinas selected on Lake Texoma from September 1999 to December 2001. Paired water and sediment samples were also collected. Groundwater samples were collected at about 70 producing monitoring wells. Water quality parameters measured were inorganics (nitrate, nitrite, orthophosphate, ammonia, sulfate, and chloride), dissolved methane, total organic carbon (TOC) (or DOC), volatile organic compounds (VOCs) such as methyl tert-butyl ether (MTBE) and BTEX, and a suite of metals. Biotic communities were evaluated at impact and reference sites. Five basic components were measured; two terrestirial components (plants and bird comminitires) and three aquatic components (benthic inverbrates, litteral-zone fishes, ecosystem attribures). Potential impacts to these comminites were evaluated.

• 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.

• Asian Journal of Atmospheric Environment
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• 제5권4호
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• pp.237-246
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• 2011

To explore the sources of carbonaceous material in the airborne particulate matter (PM), comprehensive PM sampling was performed (3 to 14 January 2010) at a traffic hot spot site (HS), Farm Gate, Dhaka using several samplers: AirMetrics MiniVol (for $PM_{10}$ and $PM_{2.5}$) and MOUDI (for size fractionated submicron PM). Long-term PM data (April 2000 to March 2006 and April 2000 to March 2010 in two size fractions ($PM_{2.2}$ and $PM_{2.2-10}$) obtained from two air quality-monitoring stations, one at Farm Gate (HS) and another at a semi-residential (SR) area (Atomic Energy Centre, Dhaka Campus, (AECD)), respectively were also analyzed. The long-term PM trend shows that fine particulate matter concentrations have decreased over time as a result of government policy interventions even with increasing vehicles on the road. The ratio of $PM_{2.5}/PM_{10}$ showed that the average $PM_{2.5}$ mass was about 78% of the $PM_{10}$ mass. It was also found that about 63% of $PM_{2.5}$ mass is $PM_1$. The total contribution of BC to $PM_{2.5}$ is about 16% and showed a decreasing trend over the years. It was observed that $PM_1$ fractions contained the major amount of carbonaceous materials, which mainly originated from high temperature combustion process in the $PM_{2.5}$. From the IMPROVE TOR protocol carbon fraction analysis, it was observed that emissions from gasoline vehicles contributed to $PM_1$ given the high abundance of EC1 and OC2 and the contribution of diesel to $PM_1$ is minimal as indicated by the low abundance of OC1 and EC2. Source apportionment results also show that vehicular exhaust is the largest contributors to PM in Dhaka. There is also transported $PM_{2.2}$from regional sources. With the increasing economic activities and recent GDP growth, the number of vehicles and brick kilns has significantly increased in and around Dhaka. Further action will be required to further reduce PM-related air pollution in Dhaka.