• Journal of Community Nutrition
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• 제8권1호
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• pp.3-8
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• 2006

This retrospective study was designed to evaluate the nutrient contributions of the five meal components of school lunch menus planned for elementary students in two school districts (District A and B) in the Midwestern state of the United States. The 4-week cycle menu was planned for two time periods (Period 1 and Period 2) following guidelines for NuMenus and general menu planning principles. Menu components of planned and served menus for two time periods were analyzed using $Nutri-Kids^{TM}$. No significant differences in the nutrient content of between Periods 1 and 2 were found for District A. District B served significantly more vitamin A and total fat in Period 1 and significantly more calories, iron, vitamin A, protein, and total fat in Period 2 than was planned. The major nutrients provided by the entree component included protein, calories, cholesterol, total fat, saturated fat, and sodium. Milk was an important source of calcium and provided approximately one-third of the total protein and vitamin A in the meal. The vegetable/fruit component was the major source of vitamins A and C. The grain/bread component provided approximately 20% of the carbohydrates among five meal components. The miscellaneous component affected the sodium and fat content of the menus. Menu planners can use the results of this study to enhance their knowledge of the nutrient contributions of each meal component and as inputs for planning menus that meet children's nutritional requirements.

• 한국대기환경학회지
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• 제11권3호
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• pp.279-290
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• 1995

Recently in Korea, due to the significant drop of lead and bromine levels as a marker of autoemission source in the urban areas, the conventional application of receptor methods has many difficulties to properly apportion mass contribution of some sources. It is then needed to urgently develop alternative source profiles and identify new emission markers. Thus, the study has extensively examined the results obtained from using PAHs and elemental data for receptor modeling and has provided an opportunity to identify alternative source compositions and to determine a proper number of the ambient emission sources in Seoul area. The purpose of the study is to identify the sources of PM-10 and to estimate their mass contributions in Seoul area. Thus, a receptor model, target transformation factor analysis(TTFA) has been massively applied. The TTFA offers the possibility of determining the number of sources and their mass contributions. The input data used in this study are composed of two separate sets: fine (d$_{p}$ < 2.5.mu.m) and coarse (2.5.mu.m < d$_{p}$ < 10.mu.m) mode aerosol samples. Each sample was simultaneously collected by a PM-10 dichotomous sampler during the daytime(8 AM to 8 PM) and the nighttime(8 PM to 8 AM) from February to October 1993 on the Sungdong-Gu, Seoul. All the samples were analyzed to determine the levels of 10 inorganic elements by an XRF system as well as 14 PAHs by a HPLC. However, only 8 inorganic elements and 7 PAHs were used for the various statistical analysis.sis.

• 한국대기환경학회지
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• 제33권5호
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• pp.445-457
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• 2017

In this study, we developed an approach to better account for uncertainties in estimated contributions from fine particulate matter ($PM_{2.5}$) modeling. Our approach computes a Concentration Correction Factor (CCF) which is a ratio of observed concentrations to baseline model concentrations. We multiply modeled direct contribution estimates with CCF to obtain revised contributions. Overall, the modeling system showed reasonably good performance, correlation coefficient R of 0.82 and normalized mean bias of 2%, although the model underestimated some PM species concentrations. We also noticed that model biases vary seasonally. We compared contribution estimates of major source sectors before and after applying CCFs. We observed that different source sectors showed variable magnitudes of sensitivities to the CCF application. For example, the total primary $PM_{2.5}$ contribution was increased $2.4{\mu}g/m^3$ or 63% after the CCF application. Out of a $2.4{\mu}g/m^3$ increment, line sources and area source made up $1.3{\mu}g/m^3$ and $0.9{\mu}g/m^3$ which is 92% of the total contribution changes. We postulated two major reasons for variations in estimated contributions after the CCF application: (1) monthly variability of unadjusted contributions due to emission source characteristics and (2) physico-chemical differences in environmental conditions that emitted precursors undergo. Since emissions-to-$PM_{2.5}$ concentration conversion rate is an important piece of information to prioritize control strategy, we examined the effects of CCF application on the estimated conversion rates. We found that the application of CCFs can alter the rank of conversion efficiencies of source sectors. Finally, we discussed caveats of our current approach such as no consideration of ion neutralization which warrants further studies.

• 한국환경보건학회지
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• 제26권4호
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• pp.1-8
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• 2000

The objective of this study was to identify the sources and to estimate the source contributions to the atmospheric TSP(total suspended particulate matter) and PM-10(particulate matter with aerodynamic diameters less than 10$\mu\textrm{m}$) concentration in Taegu area. A total of 84 samples was collected during the January to December 1999. TSP and PM-10 were collected on filters by portable air sampler, and heavy metals in TSP and PM-배 were analyzed by ICO(Inductively Coupled Plasma Spectrometery) after preliminary treatment. The results were follow as : First, annual average of TSP and PM-10 concentration was 123 and 69$\mu\textrm{g}$/㎥ respectively. The concentration of TSP and PM-10 were highest in winter season compared to other seasons. Second, the concentration of Al, Fe, Mn were higher in TSP than in PM-10, indicating that these heavy metals are generally associate with natural contributions. Third, metal combinations showed that a high correlation among concentrations of heavy metals were follows: As Al, Fe and Mn in TSP ; Ni, Cr, Cd and Pb in PM-10. Finally, Statistical analysis was performed using Principal Components Analysis(PCA) in order to find possible sources of the pollutants. The factor analysis was permitted to identify four major sources(soil/road dust resuspension, waste incineration, furl combustion, vehicular emission) in each fraction. These source accounted for at least 83, 85% of variance of TSP and PM-10 concentration in Taegu area.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.123-123
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• 2020

The present study is aimed to correcting radar-based mean areal precipitation forecasts to improve urban flood predictions and uncertainty analysis of water levels contributed at each stage in the process. For this reason, a long short-term memory (LSTM) network is used to reproduce three-hour mean areal precipitation (MAP) forecasts from the quantitative precipitation forecasts (QPFs) of the McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE). The Gangnam urban catchment located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 24 heavy rainfall events, 22 grid points from the MAPLE system and the observed MAP values estimated from five ground rain gauges of KMA Automatic Weather System. The corrected MAP forecasts were input into the developed coupled 1D/2D model to predict water levels and relevant inundation areas. The results indicate the viability of the proposed framework for generating three-hour MAP forecasts and urban flooding predictions. For the analysis uncertainty contributions of the source related to the process, the Bayesian Markov Chain Monte Carlo (MCMC) using delayed rejection and adaptive metropolis algorithm is applied. For this purpose, the uncertainty contributions of the stages such as QPE input, QPF MAP source LSTM-corrected source, and MAP input and the coupled model is discussed.

• Asian Journal of Atmospheric Environment
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• 제5권2호
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• pp.86-96
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• 2011

The positive matrix factorization (PMF2) and multilinear engine (ME2) models have been shown to be powerful environmental analysis techniques and have been successfully applied to the assessment of ambient particulate matter (PM) source contributions. Because these models are difficult to apply practically, the US EPA developed a more user-friendly version of the PMF. The initial version of the EPA PMF model does not provide any rotational capabilities; for this reason, the model was upgraded to include rotational functions in the EPA PMF ver. 2.0. In this study, PMF and EPA PMF modeling identified ten particulate matter sources including secondary sulfate I, vehicle gasoline, secondary sulfate II, secondary nitrate, secondary sulfate III, incinerators, aged sea salt, airborne soil particles, oil combustion, and diesel emissions. All of the source profiles determined by the two models showed excellent agreement. The calculated average concentrations of $PM_{2.5}$ were consistent between the PMF2 and EPA PMF ($17.94{\pm}0.30{\mu}g/m^3$ and $17.94{\pm}0.30\;{\mu}g/m^3$, respectively). Also, each set of estimated source contributions of the PMF2 and EPA PMF showed good agreement. The results from the new EPA PMF version applying rotational functions were consistent with those of PMF2. Therefore, the updated version of EPA PMF with rotational capabilities will provide more reasonable solutions compared with those of PMF2 and can be more widely applied to air quality management.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2009년도 춘계학술대회 논문집
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• pp.1069-1077
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• 2009

For external noise prediction of railway vehicles, sophisticated individual source modelling as well as appropriate noise propagation model from the sources is necessary to ensure the accuracy of the predicted results and contributions of each equipment to the overall noise levels. Accurate and reasonable identification procedures of sound sources of equipment including source strength, directivity and positions installed in the train play an important role in a prediction model, since it is not easy to establish a simple model for the sources with a single rule due to the complexity of source characteristics of equipment in size and directivity pattern. This paper guidelines practical considerations for identification of noise sources in railway vehicles including typical source characteristics of several sub-systems that emits noise to the environment, particularly for electric multiple unit(EMU), and verify effectiveness of assumptions used in the modelling of equipment by measurement with a simple part. The predicted external noise level of a complete train using Exnoise, which was developed by Hyundai-Rotem and has been verified in the a lot of field-tests, incorporating source modelling considered in this paper shows close correlation with the measured ones.

• 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.

• 한국대기환경학회:학술대회논문집
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• 한국대기환경학회 2007년도 추계학술대회 논문집
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• pp.234-235
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• 2007

• 한국음향학회지
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• 제33권1호
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• pp.40-47
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• 2014

원심팬 날개 깃에서 발생한 와류와 원심팬 볼루트 사이의 상호작용은 원심팬의 주요한 소음원으로 알려져 있다. 본 연구에서는 저소음 설계의 기초 자료로 활용하기 위하여 원심팬의 주요한 소음원 영역으로 고려되는 원심팬 볼루트 영역을 세분화하여 볼루트 영역내의 상대적 기여도를 분석한다. 주요한 소음원으로부터 방사되는 소음을 예측하기 위해 내부 음장용 복합 전산공력음향학(CAA, Computational Aero-Acoustics) 방법을 사용한다. 이 방법은 전산유체역학(CFD, Computational Fluid Dynamics)과 음향상사법(Acoustic Analogy), 그리고 경계요소법(BEM, Boundary Element Method)을 사용하여 원심팬 내부 유동장으로부터 방사한 소음을 원심팬 외부 음향장에서 예측하는 방법이다. 복합 CAA 방법을 이용한 원심팬 볼루트 영역내의 소음원의 상대적 기여도 분석은 컷-오프영역으로부터 출구영역보다 컷-오프영역으로부터 원심팬 스크롤영역이 전체 소음에 대한 기여도가 높고, 날개 깃의 쉬라우드 영역보다 허브 영역이 전체 소음에 대한 기여도가 높다는 것을 보여준다. 이러한 결과는 향후 저소음 원심팬 개발을 위한 유용한 자료로 활용될 것이다.