• Proceedings of the Korean Environmental Health Society Conference
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• 2005.11a
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• pp.130-133
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• 2005

황사는 대기오염과 관련하여 심혈관계 및 호흡기계 질환으로 인한 사망 의 증가 등의 건강 영향으로 인해 관심의 대상이 되어왔다. 그러나 최근의 황사성분을 분석한 연구결과에 따르면 실제로 황사기간 동안에 전체먼지농도 중 조대먼지농도의 비율은 증가한 반면 미세먼지농도의 비율은 감소하였고 유해한 중금속의 농도도 별다른 변화를 보이지 않거나 오히려 감소하였다. 이에 본 연구에서는 서울시의 2000년부터 2002년, 2월부터 5월까지의 황사시기를 포함한 경우와 포함하지 않은 경우를 비교하여 황사현상으로 인한 대기먼지가 총사망에 미치는 영향을 비교하였다. 분석결과에 따르면 총사망에 미치는 대기먼지의 영향은, 황사시기를 포함하여 분석한 경우보다 황사시기를 제외하고 분석한 경우에서 더 큰 건강영향을 보였다. 즉, 황사시기를 제외하고 분석한 경우에서 대기먼지가 총사망에 미치는 위험의 크기가 더 큰 것으로 나타났다. 이는 황사의 화학성분 및 황사시 사람들의 행동양식의 변화와 같은 노출의 감소로 설명될 수 있으며 이는 도시 대기오염의 건강위해성을 평가함에 있어서 황사시기를 포함하여 분석하는 경우 도시 대기오염, 특히 대기먼지의 위해도를 과소평가할 가능성이 있으며, 지금까지 제안되는 기존의 연구결과보다 실제 도시 미세먼지의 건강영향이 훨씬 더 클 수 있음을 본 연구결과가 제시하고 있다.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• Korean Journal of Construction Engineering and Management
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• v.18 no.5
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• pp.68-76
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• 2017

A recent issue of fine dust is particulate matter with a diameter of less than $10{\mu}g/m^3$. It's classified as a pollutant that has a fatal effect on the human body when inhaled. The fugitive dust must be well controlled, since the adverse effects of dust on the surroundings are increased when the dust is blown away by the wind. Since the construction site is the place where the most fugitive dust is discharged in Korea, managing the fugitive dust discharged from the construction site can be an important issue to solve the problem of domestic fine dust. However, since the construction industry in Korea is the largest in Korea, it is difficult to control the emission of fugitive dust in the domestic construction site. In this paper, we compare and analyze the fugitive dust regulations applied to construction sites in major cities and propose the improvement plans to help control the fugitive dust generated in domestic construction sites.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.3
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• pp.293-299
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• 2022

Recently, various stakeholder are interested in microplastic to cause pollution of the marine's ecosystem and effort to conduct study of product's life cycle to reduce pollution of marine's ecosystem. The micorplastic refer to materials of the nano- to micro- sized units and it can be classified into primary and secondary. The primary microplastic mean the manufactured for use in the specific field such as the microbead of the cosmetic or cleanser. also, secondary mean the unintentionally generated during use of the product such as the textile crumb by the doing the laundry. Tire and Road Wear Particles(TRWPs) are also defined as secondary microplastic. Typically, TRWPs are created by friction between the tread compound's rubber of the tire and the surface of the road du ring the driving cars. Most of the generated TRWPs exist on the roadside and some of them were carried to marine by the rainwater. In this study, we perform the quantitative analysis of the TRWPs existed in fine dust at the roadside. So, we collected the dust from the roadside in Chungcheongnam-do's C site with a movement of 1,300 cars per the hour. The collected samples were separated according to size and density. And shape analysis was performed using the Scanning Electron Microscope(SEM). We were possible to discover a lot of TRWPs at the fine dust of the 100 ± 20 ㎛. And we analysis it u sing the Thermo Gravimetric Analysis(TGA) and Gas Chromatography/Mass Spectrometer(GC/MS) for the quantitative components from the tire. As a result, it was confirmed that TRWPs generated from the roadside fine dust were included the 0.21 %, and the tire and road components in the generated TRWPs consisted of the 3:7 ratio.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.873-885
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• 2022

The particulate matter (PM) has emerged as a hot topic around the world as it has been reported that PM is related to an increase in mortality and prevalence rates. In South Korea, the importance of PM has been recognized since the late 1990s, and various studies on PM have been conducted. This study investigated the PM research topics and trends for papers (D=2,764) published in Research Information Sharing Service (RISS) using topic modeling based on Latent Dirichlet Allocation (LDA). As a result, a total of 10 topics were identified in the whole papers, and the PM research topics were classified as 'PM reduction (Topic 1)', 'Government policy and management (Topic 2)', 'Characteristics of PM (Topic 3)', 'PM model (Topic 4)', 'Environmental education (Topic 5)', 'Bio (Topic 6)', 'Traffic (Topic 7)', 'Asian dust (Topic 8)', 'Indoor PM (Topic 9)', 'Human risk (Topic 10)'. In particular, the proportion of papers on topics 'Government policy and management (Topic 2)', 'PM model (Topic 4)', 'Environmental education (Topic 5)', and 'Bio (Topic 6)' to the toal number of papers increased over time (linear slope > 0). The results of this study provide the new literature review methodology related to particulate matter and the history and insight.

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.635-646
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• 2018

This study sought to identify the characteristics of seasonal concentration differences of particulate matter influenced by land cover types associated with particulate matter emission and reductions, namely forest and urbanized regions. PM10 and PM2.5 was measured with quantitative concentration in 2016 on 23 urban air monitoring stations in Seoul, classified the stations into 3 groups based on the ratio of urbanized and forest land covers within a range of 3km around station, and analysed the differences in particulate matter concentration by season. The center values for the urbanized and forest land covers by group were 53.4% and 34.6% in Group A, 61.8% and 16.5% in Group B, and 76.3% and 6.7% in Group C. The group-specific concentration of PM10 and PM2.5 by season indicated that the concentration of Group A, with high ratio of forests, was the lowest in all seasons, and the concentration of Group C, with high ratio of urbanized regions, had the highest concentration from spring to autumn. These inter-group differences were statistically significant. The concentration of Group C was lower than Group B in the winter; however, the differences between Groups B to C in the winter were not statistically significant. Group A concentration compared to the high-concentration groups by season was lower by 8.5%, 11.2%, 8.0%, 6.8% for PM10 in the order of spring, summer, autumn and winter, and 3.5%, 10.0%, 4.1% and 3.3% for PM2.5. The inter-group concentration differences for both PM10 and PM2.5 were the highest in the summer and grew smaller in the winter, this was thought to be because the forests' ability to reduce particulate matter emissions was the most pronounced during the summer and the least pronounced during the winter. The influence of urbanized areas on particulate matter concentration was lower compared to the influence of forests. This study provided evidence that the particulate matter concentration was lower for regions with higher ratios of forests, and subsequent studies are required to identify the role of green space to manage particulate matter concentration in cities.

• Journal of Korean Society of Environmental Engineers
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• v.37 no.4
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• pp.197-203
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• 2015

Particulate matter (PM) has emitted in many regions of the world and is causing many health-related problems. Thus reasonable politics and solutions are needed to reduce PM in Seoul. Further it is required to clearly explain the major portions of chemical components contained in $PM_{10}$ to figure out the characteristics of $PM_{10}$, and to develop effective reduction measures in order to decrease the adverse effects of $PM_{10}$. $PM_{10}$ samples were collected in Seoul and analyzed their ions to examine the physical and chemical characteristics of ionic species. Since hydrogen ion ($H^+$) and carbonate ion (${CO_3}^{2-}$)) cannot be analyzed by Ion chromatography (IC), concentrations of $H^+$ and ${CO_3}^{2-}$ were initially estimated by pH and equivalent differences between anions and cations in this study. Starting from the study findings, good combination results for compositional patterns between anions and cations were obtained by applying a mathematical modelling technique that was based on the mass balance principle. The ions in $PM_{10}$ were combined with $H^+$, ${CO_3}^{2-}$, and supplement for $NO_3{^-}$, $Cl^-$ formed such compounds $NH_4Cl$, $NH_4NO_3$, $CaSO_4$, $(NH_4)_2SO_4$, $NaNO_3$, NaCl, $Na_2CO_3$, and $(NH_4)_2CO_3$ in the study area.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.506-512
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• 2018

The fine dust generated in the home and restaurant business occupies a low ratio of about 4% of the total fine dust emissions. However, at the foodservice business, the rate of change of the pollutant concentration is very high, so that the temporary fine dust concentration can be measured up to 60 times. The pollutants generated from non-industrial combustion plants consist of particulate fine dust and gaseous organic compounds. To remove these pollutants, cleaning dust collection system, which is an effective system for simultaneous removal of gaseous and particulate matter, is applied. This is a method of increasing the probability of diffusion capture of the Brownian motion by pressurized liquid injection method using the atomizing nozzle. The dust removal efficiency of the fine dust collecting system was analyzed by nozzle spraying air pressure condition and angle using the manufactured fine dust removing system. As a result, it was confirmed that the efficiency of removal of fine dust and gaseous organic compounds was more than 90%. The developed system is expected to be highly usable in the future because it can remove particulate dust from the existing plant hood system without any installation cost.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.56-62
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• 2021

High forecast accuracy is required as social issues on particulate matter increase. Therefore, many attempts are being made using machine learning to increase the accuracy of particulate matter prediction. However, due to problems with the distribution of imbalance in the concentration and various characteristics of particulate matter, the learning of prediction models is not well done. In this paper, to solve these problems, a binary classification model was proposed to predict the concentration of particulate matter needed for prediction by dividing it into two classes based on the value of 80㎍/㎥. Four classification algorithms were utilized for the binary classification of PM10. Classification algorithms used logistic regression, decision tree, SVM, and MLP. As a result of performance evaluation through confusion matrix, the MLP model showed the highest binary classification performance with 89.98% accuracy among the four models.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.218-236
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• 2023

This study attempted to analyze the environmental equity of fine dust(PM₁₀) in Daegu using MGWR(Multi-scale Geographically Weighted Regression) and KT(Korea Telecom Corporation) sensor data. Existing national monitoring network data for measuring fine dust are collected at a small number of ground-based stations that are sparsely distributed in a large area. To complement these drawbacks, KT sensor data with a large number of IoT(Internet of Things) stations densely distributed were used in this study. The MGWR model was used to deal with spatial heterogeneity and multi-scale contextual effects in the spatial relationships between fine dust concentration and socioeconomic variables. Results indicate that there existed an environmental inequity by land value and foreigner ratio in the spatial distribution of fine dust in Daegu metropolitan city. Also, the MGWR model showed better the explanatory power than Ordinary Least Square(OLS) and Geographically Weighted Regression(GWR) models in explaining the spatial relationships between the concentration of fine dust and socioeconomic variables. This study demonstrated the potential of KT sensor data as a supplement to the existing national monitoring network data for measuring fine dust.