• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.417-426
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• 2019

Conventional visualization of PM (Particulate Matter)10 flows applies superimposition of concentration distribution maps and wind field maps. This method is efficient for small scale maps where only macro flow trends are of interest. However, in the case of urban areas, local flows are difficult to model at micro level using wind fields, and therefore different methods of flow extraction is deemed necessary. In this study, flow information is extracted and visualized directly from the PM10 density data by using the gravity model. This method has the advantage that additional information such as wind field is not necessary for estimating the intensity and direction of PM10 flow. The extracted spatio-temporal flow patterns of PM10 are analyzed with relation to traffic information.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.123-138
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• 2018

To sustainably monitor air pollution in Seoul, the number of Air Pollution Monitoring Station has been gradually increased by Korea's Ministry of Environment. Although particulate matters(PM), one of the pollutants measured at the stations, have an significant influence on human body, the concentration of PM in Korea came in second among 35 OECD member countries. In this study, using the data of PM concentration from the stations, distribution maps of PM10 and PM2.5 concentrations over Seoul were generated, and spatial factors potentially related to PM distribution were investigated. Based on a circumscribed hexagon about a circle in radius of 500 meters created as a basic unit, Seoul was sectionalized and PM concentration map was generated using the interpolation technique of 'inverse distance weighting'. The distributions of PM concentrations were investigated with commuting time by administrative district and the outcome was related with land-use type and volume of traffic. Results from this analysis indicated distribution pattern of PM10 concentration was different from that of PM2.5 by administrative district and time. The distribution of PM concentration was strongly related to not only the size of business and trafficked areas among the land-use type, but also the existence of urban green. Further analysis of the relationship between the PM concentration and detailed land-use and urban green maps can be helpful to identify spatial factors which have an impact on the PM concentration on the regional scale.

• Environmental and Resource Economics Review
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• v.30 no.4
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• pp.627-648
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• 2021

The fine-dust season management system refers to the policy of implementing enhanced reduction measures in transportation, power, business and living sectors in winter, when fine dust levels are high. The fine dust season management system is a regulatory policy that causes social costs and transfers to various economic players. Equity is an important issue for the cost burden. Therefore, in this study, the cost of each power generator was analyzed using the coal power generation reduction amount of each power generator to verify that the cost of the power sector is evenly distributed. In particular, the effect of the fine dust season management system on coal power generation of power generators was analyzed by applying a synthetic control method that can identify the time-variable effect of the policy. It was confirmed that the fine dust season management system reduced volume of fuel and power generation in coal power plants, resulting in an increase in the cost of the power generation sector, even considering the effect of some power demand due to the COVID-19 crisis. However, it could be seen that these costs were not distributed equally among the generators, and that they were more costly to the specific generators.Social costs incurred by fine dust season management need to be improved so that stakeholders are equally burdened.

• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.355-383
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• 2019

This study is to investigate whether averting costs for wearing $anti-PM_{10}$ masks and using air purifiers at home to reduce exposure from $PM_{10}$ are influenced by subjective risk perceptions and/or objective $PM_{10}$ concentration levels, whose estimates will be used to measure the willingness to pay for $PM_{10}$ risk reduction. An empirical analysis was conducted on a sample of 1,224 respondents who participated in the web-based survey in the late October of 2017. As we reflect the potential endogeniety bias in the estimation of averting cost functions of using air purifiers, the coefficients of risk perception were differed by 6~7 times. Respondents. subjective risk perceptions were influenced by individuals' knowledge, attitudes and demographic variables, as well as the levels of $PM_{10}$ concentrations in their residential region. The marginal willingness to pay for risk reductions at the mean levels of their risk perceptions were measured at 1,000 won per month from wearing $anti-PM_{10}$ masks and 6,000 won for using air purifiers respectively.

• Journal of Environmental Impact Assessment
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• v.27 no.5
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• pp.405-416
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• 2018

In this research, the distribution of Platinum Group Elements (PGEs) at roadside dust in Daejeon, Korea was examined using an ICP-MS (Inductively Coupled Plasma-Mass Spectrometry) technique. For the quality assurance of the determination, method validation based on its accuracy and precision was conducted using SRM (Standard Reference Material). It was found that the relative errors of Pt, Pd, and Rh against each SRM value were -0.7%, -10.0%, and -20.4%, respectively, while relative standard deviations for three elements were less than 10%. The concentrations of Pt, Pd and Rh in roadside dust averaged as $17.4{\pm}9.2{\mu}g/kg$, $283.6{\pm}20.5{\mu}g/kg$, and $7.3{\pm}2.8{\mu}g/kg$, respectively. The concentrations of Pt and Rh have significantly higher distribution patterns in the dust at roadside and underground parking lot than those in soil of the background or other urban area. The correlation analysis between concentrations of PGEs in roadside dust indicates that the distribution of Pt and Rh concentration were strongly affected by automobile sources.

• Proceedings of the Korean Environmental Health Society Conference
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• 2001.04a
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• pp.81-84
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• 2001

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2002.04a
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• pp.269-270
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• 2002

PM$_{10}$ 대기먼지는 자연적이고 인위적인 발생원에 의해 다양한 미량원소들을 함유하고 있기 때문에 흔히 대기질 관리나 환경영향 평가를 위한 대기관측시료로 이용되고 있으며, 특히 PM$_{2.5}$ 먼지의 질량농도와 특정원소의 농도가 높을 때, 장ㆍ단기적으로 인체 보건에 큰 영향을 미치는 것으로 알려져 있다. 중성자방사화분석법은 핵반응을 통해 생성시킨 방사성동위원소로부터 방출되는 방사선을 검색하여 절대적으로 성분원소를 정량하는 핵분석기술(Nuclear Analytical Techniques)중의 하나로서 소량의 시료로부터 미량원소의 비파괴, 동시 다원소 분석이 가능하고 분석감도($10^{-3}$ - $10^{-7}$$\mu\textrm{g}$)가 탁월한 분석기술이다. (중략)략)

• 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.

• Proceedings of the Korean Environmental Health Society Conference
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• 2001.04a
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• pp.86-88
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• 2001

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.124-132
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• 2022

This study was aimed to visualize the spatial distribution of PM₁₀ data measured at non-uniformly distributed observation sites in South Korea. Different spatial interpolation methods were applied to irregularly distributed PM₁₀ observation data from January, 2019, when the concentration was the highest and in July, 2019, when the concentration was the lowest. Four interpolation methods with different parameters were used: Inverse Distance Weighted (IDW), Ordinary Kriging (OK), radial base function, and scattered interpolation. Six cases were cross-validated and the normalized root-mean-square error for each case was compared. The results showed that IDW using smoothing-related factors was the most appropriate method, while the OK method was least appropriate. Our results are expected to help users select the proper spatial interpolation method for PM₁₀ data analysis with comparative reliability and effectiveness.