• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.374-385
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• 2022

Purpose: Even if a damage is applied to the dust of the construction site containing the first-class carcinogen, it is dismissed or 5~30% of the amount of noise damage compensation is paid., Because of such loopholes, some construction companies are neglecting the dust management of the construction site, and the damage of the workers and the residents in the construction site continues. Method: The purpose of this study is to examine the problems of the calculation criteria of damage compensation amount of construction site dust, the measurement of dust concentration, the analysis of measurement data (the data of electric signboard measuring device by the mining scattering method), the prediction and evaluation methods such as modeling, and to suggest improvement measures. Result: It is found that it is impossible to calculate the amount of damages from dust damage in the construction site by calculating the current dust damage compensation amount and dust concentration modeling and measurement. Conclusion: It will receive an application for compensation for damage within the site where damage is expected (about 100m in the straight line and the boundary line of the site), and present a method of calculating the amount of compensation that differentially evaluates dust damage to the degree of dust management and compliance with dust-related legal standards.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1881-1890
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• 2021

Particulate matter (PM) affects the human, ecosystems, and weather. Motorized vehicles and combustion generate fine particulate matter (PM_2.5), which can contain toxic substances and, therefore, requires systematic management. Consequently, it is important to monitor and predict PM_2.5 concentrations, especially in large cities with dense populations and infrastructures. This study aimed to predict PM_2.5 concentrations in large cities using meteorological and chemical variables as well as satellite-based aerosol optical depth. For PM_2.5 concentrations prediction, a random forest (RF) model showing excellent performance in PM concentrations prediction among machine learning models was selected. Based on the performance indicators R², RMSE, MAE, and MAPE with training accuracies of 0.97, 3.09, 2.18, and 13.31 and testing accuracies of 0.82, 6.03, 4.36, and 25.79 for R², RMSE, MAE, and MAPE, respectively. The variables used in this study showed high correlation to PM_2.5 concentrations. Therefore, we conclude that these variables can be used in a random forest model to generate reliable PM_2.5 concentrations predictions, which can then be used to assess the vulnerability of schools to PM_2.5.

• Journal of Environmental Impact Assessment
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• v.30 no.4
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• pp.237-246
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• 2021

The similarity of hourly PM₁₀ and PM_2.5 concentration profiles of the atmospheric monitoring stations in Gyeonggi-do was evaluated through the multilateral analysis between stations. The existing category for most stations in the regions shows relatively low Pearson correlation values of 0.68 and 0.7 for PM₁₀ and PM_2.5 on average respectively, and some monitoring stations revealed high relationships over 0.8 to other regions. Since the current regions are mainly categorized by cluster analysis based on the number of occurrence of high concentration events and geological factors, it is necessary to reclassify them by concentration characteristics for precise fine dust management. In accordance, multi-dimensional scaling being able to visualize could categorize the regions based on regional emission contribution rate and hourly fine dust concentration. As a result of the current analysis, PM₁₀ and PM_2.5 could be reclassified into five regions and fourregions, respectively.

• Particle and aerosol research
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• v.5 no.3
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• pp.139-148
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• 2009

In Seoul, the largest emission source for volatile organic compounds (VOCs) based on the emission inventory is solvent usage followed by vehicular exhaust. However, according to a CMB modeling result by Na and Kim (2007), vehicular exhaust was the largest emission source followed by solvent usage. Detailed analyses on the validity of the CMB model result were carried out and it was suggested that the existing emission inventory for VOCs might be underestimating vehicular emission. Scientific considerations that should be considered for the effective control strategy against VOCs are discussed.

• 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 the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.118-129
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• 2016

Many epidemiological studies, relying on administrative air pollution monitoring data, have reported the association between particulate matter ($PM_{10}$) air pollution and human health. These monitoring data were collected at a limited number of fixed sites, whereas government-generated health data are aggregated at the area level. To link these two data types for assessing health effects, it is necessary to estimate area-level concentrations of $PM_{10}$. In this study, we estimated district (Gu)-level $PM_{10}$ concentrations using a previously developed pointwise exposure prediction model for $PM_{10}$ and three types of point locations in Seoul, Korea. These points included 16,230 centroids of the largest census output residential areas, 422 community service centers, and 610 centroids on the 1km grid. After creating three types of points, we predicted $PM_{10}$ annual average concentrations at all locations and calculated Gu averages of predicted $PM_{10}$ concentrations as representative Gu-estimates. Then, we compared estimates to each other and to measurements. Prediction-based Gu-level estimates showed higher correlations with measurement-based estimates as prediction locations became more population representative ($R^2=0.06-0.59$). Among the three estimates, grid-based estimates gave lowest correlations compared to the other two(0.35-0.47). This study provides an approach for estimating area-level air pollution concentrations and assesses air pollution health effects using national-scale administrative health data.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.61-69
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• 2014

In this study spatial analysis of PM10 was performed to Particulate Materials(PM) less than $10{\mu}m$ in diameter in Seoul city. Because PM10 are responsible for the increasing mortality rate of lung cancer and cardiovascular diseases, spatial distribution of PM10 are special interest in air pollution of Seoul. In this study, spatial analysis of Particulate Materials were monitored by monthly averaged PM10 concentration of 2010, 2011. The monthly spatial patterns of PM10 showed the west area of Seoul(Youngdungpo) higher PM10 concentration than northern part of Seoul in early spring and winter seasons. In the comparison of PM10 concentration distribution patterns in 2010 and 2011, the PM10 concentration of 2011 at Gangnam and Songpa-gu were more increased than yearly averaged patterns of 2010. The distribution patterns of PM10 in Seoul city showed the high concentration PM10 of several areas with Youngdungpo-gu, Gangnam-gu and Cheongnyangni. Therefore we need to establish PM10 management strategy for these area.

• Journal of Science Education
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• v.45 no.2
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• pp.201-218
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• 2021

The purpose of this study is to investigate qualitatively two cases of general citizens' scientific participation and activities responding to fine dust problem. The processes of their scientific actions were investigated and categorized inductively based on three stages: problem recognition stage, information collection and analysis stage, and sharing and spreading stage. As a result, in the 'problem recognition' stage, two participants recognized the seriousness of the fine dust problem as they felt a threat to their health and began to act practically by questioning the accuracy of public data. In the 'information collection and analysis' stage, a participant collected as much information as possible and compared them in order to obtain more accurate information for her situation. On the other hand, another participant conducted various experiments in person to get the information which is appropriate to his situation. Finally, in the 'Sharing and Spreading' stage, both participants created and shared various materials based on online environment, and continued their activities with a sense of contribution through others assistance. Educational implications are discussed in terms of civic science education and scientific literacy.

• Journal of Environmental Impact Assessment
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• v.24 no.1
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• pp.66-77
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• 2015

The association between daily total/cardiovascular mortality and air pollution in Yeosu was investigated over 11-year period (January 2001 to December 2011). The purpose of this study was to evaluate th relative importance of the major air pollutants [particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$)] as predictors of daily total/cardiovascular mortality. People aged 65 and older showed total mortality increase by 5.0% with $SO_2$ concentration increase by 11.67ppb(IQR) was found to raise mortality caused by circulatory diseases by 8.6%, exhibiting a statistically significant result.

• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.42-47
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• 2010

본 연구에서는 2005~2007년 여름철(JJAS; 6~9월) 기간에 한반도 부근의 위성관측(TRMM) 및 지상관측(AWS) 강수량 그리고 대기오염 관측 자료를 이용하여 이들의 공간적 분포와 일주기 특성을 분석하였다. 이 기간에 AWS 평균 강수량은 전국적으로 약 5 mm/day 이었고, 제주도, 중부 내륙, 그리고 영 호남 경계 지역에서 9 mm/day 이상으로 많았다. PM10 (${\mu}g/m^3$) 미세먼지의 농도는 $27{\sim}57\;{\mu}g/m^3$ 이었고, 특히 수도권과 경남의 산업지역에서 $45\;{\mu}g/m^3$ 이상으로 높았다. 위성관측과 지상관측 강수량간의 상관(~0.8)은 매우 유의적이었다. AWS는 지점 관측이고 TRMM 관측은 면적평균임을 고려할 때, 위의 상관값은 상당히 높은 것으로 판단된다. 일주기 분석에서 미세먼지는 수도권 지역에서 오전에, 그리고 영남 지역에서는 오후 늦게 많이 발생하였다. AWS 강수량은 영동 및 경북 지역에서 이른 오전(2~8시)에, 이 지역 외에서는 오후 늦게(16~22시) 주로 발생하였다. 중부 지방의 강수 주기는 전선이 오후 3시부터 다음날 오전 4시까지 서해안에서 동해안으로 동진하는 형태를 잘 반영하였다.