• Journal of Korean Society for Atmospheric Environment
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• v.22 no.2
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• pp.157-166
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• 2006

Ambient TSP at four sites in Korea and soil samples from the source regions of Asian Dust in northern China were collected and analyzed for 15 metal components and 6 water-soluble ions to conduct a chemical mass balance (CMB). CMB receptor model was used to estimate the source contribution of TSP during the Asian Dust period, and the model results showed that China soil was the largest source contributor, accounting for 81% of TSP ($458.2{\mu}g/m^3$). Vehicle emission and geological sources contributed to about 8.8% and 4.4% of aerosol mass, followed by sea salt (1.5%) and secondary aerosol (2.9%). Fuel combustion and industrial process sources were found to be relatively minor contributors to TSP (${\leq}1%$). In addition to source contribution estimates, this study tried to identify the origin of Asian Dust observed in Korea. Among all 13 China soil profiles presented in this study, the most adoptable profile which can project the case well was selected and considered as the origin of the applied case.

• Journal of Korean Society for Atmospheric Environment
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• v.6 no.1
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• pp.11-21
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• 1990

To quantitatively estimate the effect of yellow sand(loess) fromt he Northern China, various soil sources having similar chemical compositions to yellow sands should be separated and identified. After that, mass contribution for yellow sand can be calculated. The study showed that it was impossible to solve this problem by the traditional bulk analyses. However, particle-by-particle analysis by a CCSEM (computer controlled scanning electron microscope) gave enormous potentials to solve it. To perform this study, seven soil source data analyzed by CCSEM were obtained from Texas, U.S.A. Initially, each soil date was classified into two groups, coarse and fine particle groups since the particle number distribution showed a minimum occurring at 5.2$\mu$m of aerodynamic diameter. Particles in each group were then classified into one of the 283 homogeneous particle classes by the universal classification rule which had been built by an expert system in the early study. Further, mass fractions and their uncertainties for each class in each source were calculated by the Jackknife method, and then source profile matrix for the 7 soil sources was created. To use the profile matrix in the study of source contribution, it is necessary to test the degree of collinearity among sources. The profiles were tested by the singular value decomposition method. As a result, each soil source characterized by artificially created variables was totally independent each other and is ready to use in source contribution studies as a receptor model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.75-81
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• 2006

Recently, customers interest about noise of household electric appliance is growing. so, designer of product must study to reduce noise of product. Specially, in case of household electric appliance such as washing machine, there was consumers' complaint about noise that is radiated under dehydrating condition. But, in the case of washing machine, identification of noise source is not easy when washing machine is under dehydrating condition. Because various noise source influence each other, it is difficult to find out pure contribution degree about output noise. Multi-Dimensional Spectral Analysis(MDSA) is method that can remove correlation between inputs each other and express pure contribution degree about output of single input. So in this study, we analyzed contribution of each noise source on transfer pass of noise that is radiated at dehydration of washing machine using MDSA.

• Journal of Environmental Science International
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• v.19 no.8
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• pp.971-981
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• 2010

The objectives of this study were to measure ambient total gaseous mercury (TGM) concentrations in Seoul, to analyze the characteristics of TGM concentration, and to identify of possible source areas for TGM using back-trajectory based hybrid receptor models like PSCF (Potential Source Contribution Function) and RTWC (Residence Time Weighted Concentration). Ambient TGM concentrations were measured at the roof of Graduate School of Public Health building in Seoul for a period of January to October 2004. Average TGM concentration was $3.43{\pm}1.17\;ng/m^3$. TGM had no notable pattern according to season and meteorological phenomena such as rainfall, Asian dust, relative humidity and so on. Hybrid receptor models incorporating backward trajectories including potential source contribution function (PSCF) and residence time weighted concentration (RTWC) were performed to identify source areas of TGM. Before hybrid receptor models were applied for TGM, we analysed sensitivities of starting height for HYSPLIT model and critical value for PSCF. According to result of sensitivity analysis, trajectories were calculated an arrival height of 1000 m was used at the receptor location and PSCF was applied using average concentration as criterion value for TGM. Using PSCF and RTWC, central and eastern Chinese industrial areas and the west coast of Korea were determined as important source areas. Statistical analysis between TGM and GEIA grided emission bolsters the evidence that these models could be effective tools to identify possible source area and source contribution.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.6
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• pp.662-673
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• 2008

Two hybrid receptor models, potential source contribution function (PSCF) and concentration weighted tracjectory (CWT), were compared for locating $PM_{2.5}$ sources contributing to the atmospheric $PM_{2.5}$ concentrations in Seoul. The source contribution estimates by chemical receptor model (CMB) receptor model were used to identify better source areas, Among the sources, soil, agricultural burning, marine aerosol, coal-fired power plant and Chinese aerosol were only considered for the study because these sources were more likely to be associated with the long-range transport of air pollutant. Both methods are based on combining chemical data with calculated air parcel backward trajectories. However, the PSCF analyses were performed with trajectories above the $75^{th}$ percentile criterion values, while the CWT analyses used all trajectories. This difference resulted in locating of different sources, which might be helpful to interpret locating of $PM_{2.5}$ sources, High possible source areas in source contribution of soil and agricultural burning contributing to the Seoul $PM_{2.5}$ were inland areas of Heibei and Shandong provinces (highest density areas of agricultural production and population) in China. The "Chinese aerosol" was used as a representative source for the $PM_{2.5}$ originated from urban area in China. High possible source areas for the aerosol were the cities in China where are relatively close to the receptor. This result suggests that Chinese aerosol is likely to be a useful tool in studies on source apportionment and identification in Korea.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.4
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• pp.517-533
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• 2018

We utilize the CAMx (Comprehensive Air Quality Model with eXtensions) system and the PSAT (Particulate Source Apportionment Technology) diagnostic tool to determine the $PM_{2.5}$ concentration and to perform its source apportionment in the southeastern region of South Korea. For a year-long simulation, eight local authorities in the region such as Pohang, Daegu, Gyeongju, Ulsan, Busan-Gimhae, Gosung-Changwon, Hadong, and all remaining areas in Gyeongsangnam-do, are selected as source areas based on the emission rates of $NO_x$, $SO_x$, VOC, and primary PM in CAPSS (Clean Air Policy Support System) 2013 emissions inventory. The CAMx-PSAT simulation shows that Pohang has the highest $PM_{2.5}$ self-contribution rate (25%), followed by Hadong (15%) and Busan-Gimhae (14%). With the exception of Pohang, which has intense fugitive dust emissions, other authorities are strongly affected by emissions from their neighboring areas. This may be measured as much as 1 to 2 times higher than that of the self-contribution rate. Based on these estimations, we conclude that the efficiency of emission reduction measures to mitigate $PM_{2.5}$ concentrations in the southeastern region of South Korea can be maximized when the efforts of local or regional emission controls are combined with those from neighboring regions. A comprehensive control policy planning based on the collaboration between neighboring jurisdictional boundaries is required.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.4 no.2
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• pp.111-116
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• 2000

Transboundary air pollution has recently become an area of increasing scientific interest and political concern as countries are receiving air pollutants from their neighbors. In order to gain a better understanding of the long-range transport processes of air pollutants and the source-receptor relationships among neighboring countries, an atmospheric transport model coupled with a RAMS(Regional Atmospheric Modeling System) model was applied to the East Asia region during the entire month of January 1993. The scalar transport option of the RAMS model was used to calculate special atmospheric constituents such as trace gases or aerosols. The sulfate production in clouds and rainwater and its removal processes by dry and wet deposition were considered. The sulfate budget from source regions to receptor regions was estimated by analysing the source-receptor relationships. When a specific receptor site revealed a sulfate value higher than the sulfate concentration based on its own source origin, this was taken to indicate long-range transport from another source region. The contribution ratio from various source region was calculated. The contribution ratio of dry and wet deposition was higher on the main continent of the East region. Furthermore, the high deposition amounts were identified on the west coast of Korea and the East China Sea.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.4
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• pp.380-384
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• 2014

This study focused on identifying the major noise source in a tractor cabin using experimental methods. The noise levels in a tractor cabin for different engine revolution speeds were analyzed to identify the noise source. The results showed that the power steering unit (PSU) was the major noise source in a tractor cabin. The PSU was moved to the outside from the inside of the cabin in order to reduce the noise in the tractor cabin. As a result, the noise levels on the left and right sides of the operator in the tractor cabin were reduced by 6.8 and 3.9 dB, respectively. Finally, the window method was introduced to evaluate the contribution of the transmission noise. The orders of significance in the tractor noise were the front, bottom, and left area, successively.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11b
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• pp.156-160
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• 2005

The radiated noise of the excavator is composed of the various noise sources such as the diesel engine, cooling fan and hydraulic system, so the noise reduction for each noise source is required. In this study, the source contribution analysis for these principal noise sources is performed by using the noise source removal method. And to reduce the noise due to each one, the various experiments and analyses are studied. On the basis of these results, the proper reduction countermeasures are derived to develop the excavator satisfied the $2^{nd}$ noise regulation of EU.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.5
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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.