• Korean Journal of Environment and Ecology
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• v.35 no.5
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• pp.569-575
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• 2021

This study analyzed the effect of forests on reducing particulate matter by investigating the particulate matter concentration and influencing factors between urban forest and traffic forest. The concentrations of particulate matter in Hongreung Experimental Forest (urban forest) and a forest (traffic forest) formed at the intersection of Cheongryangri Station in Dongdaemun-gu, Seoul were measured with the light scattering method instrument from January to November 2018. During the study period, the average PM₁₀ concentrations in the urban forest and the traffic forest were 12.5㎍/m³ and 15.7 ㎍/m³, respectively, and the average PM_2.5 concentrations were 16.6㎍/m³and 6.9 ㎍/m³, respectively. Comparing the concentration by the urban atmospheric measurement network of the Ministry of Environment and the concentration in urban forests showed that the reduction rate of PM₁₀ was 66.9±28.6% in urbanforest and 58.6±44.1% in traffic forest and that of PM_2.5 was 71.3±23.0% and 64.9±31.3%. The difference in the reduction rate of particulate matter is likely related to the size and structure of the urban forest, and the wind velocity is considered the reduction factor.

• 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 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.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2002.04a
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• pp.227-228
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• 2002

대기오염물질 중 입자상물질(particulate matter, PM)의 크기분포 측정은 주로 다단임팩터(cascade impactor)에 의해 이루어지고 있다. 임팩터 각 단에 포집된 입자의 무게를 측정하여 입자상물질의 크기 분포로 환산하는 다단임팩터는 입자의 샘플링에 많은 시간이 소모되며 필터의 전처리 및 무게측정과정이 번거롭고, 시간에 따른 농도분포의 추이를 파악할 수 없는 단점이 있다. (중략)

• Journal of Life Science
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• v.30 no.2
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• pp.191-201
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• 2020

As society develops rapidly, environmental pollution is becoming a greater risk factor threatening human health. One of the major causes of air pollution that affects human health is particulate matter (PM), which contains a heterogeneous mixture of different particle sizes and chemical compositions. PM is classified by size into general PM (PM₁₀; diameter below 10 ㎛) and fine PM (PM_2.5; diameter below 2.5 ㎛). PM_2.5 can pass through the respiratory tract into the circulatory system and thence throughout the body. PM_2.5 is known to stimulate oxidative stress and inflammatory responses to cells, promoting diseases such as asthma, chronic respiratory disease, cardiovascular disease, diabetes mellitus, and immunological disorders. Although detailed molecular mechanisms for how PM stimulates disease progression still need to be elucidated, together with national efforts to reduce PM production, significant research has been conducted that demonstrates the harmfulness of PM in disease progression through in vitro and in vivo experiments. This review focuses on the harmfulness of PM in disease progression; we also introduce a biological verification method for determining the hazards of PM.

• 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.

• Proceedings of the PSK Conference
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• 2003.04a
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• pp.165.1-165.1
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• 2003

This research was designed to examine the presence of mutagenic/carcinogenic compounds in airborne pollutants in diesel particulate matter using an integrated biological approach. Respirable air borne particulate matter (PM2.5: <2.5mm) was collected from diesel engine exhaust using a high-volume sampler equipped with a cascade impactor. (omitted)

• Journal of Environmental Science International
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• v.29 no.2
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• pp.177-189
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• 2020

We analyzed the recent characteristics of Particulate Matter (PM) including PM₁₀ (PM with diameter of less than 10 ㎛) and PM_2.5 (PM with diameter of less than 2.5 ㎛) observed in Busan metropolitan area, and compared them with those measured in Seoul metropolitan area. This analysis includes the monthly, seasonal, and annual variations and differences, in emissions and chemical compositions observed in both Busan and Seoul areas. Synoptic meteorological conditions were investigated at the time when high PM concentrations occurred in each of the two areas. The results showed clearly decreasing trends of annual mean concentrations with strong seasonal variations: lower in summer and higher in winter in both areas. In comparison with Seoul, the seasonal variation in Busan demonstrated relatively lower, but showed greater summer fluctuations than in Seoul metropolitan area. This is implying the importance of secondary generation of PM in summer via active photochemical reaction in Busan area. In high concentration days, Busan's chemical composition of sulfate was higher than that of nitrate in summer, whereas nitrate was higher than sulfate in Seoul. The ratios of NO₃^- to SO₄^2-(N/S ratio) showed lower in Busan approximately by a factor of 1/2(half of N/S ratio) in Busan compared with that in Seoul. Others such as synoptic characteristics and emission differences were also discussed in this study.

• Progress in Medical Physics
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• v.29 no.3
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• pp.81-91
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• 2018

Particulate matter (PM) in dust causes serious pathological conditions, and it has been considered a critical health issue for many years. Respiratory disorders such as bronchitis, asthma, and chronic inflammation, are the most common illnesses due to PM that appears as dust. There is evidence that cardiovascular and neurological abnormalities are caused by PM. Although an extensive amount of work has been conducted on this topic, including studies on the nature of the particles, particle size measurements, particle distribution upon inhalation, the health effects of fine particles, disease prevention, diagnosis, and treatment, to this date, there is still a considerable lack of knowledge in these areas. Therefore, the identification of the key components that cause diseases owing to PM, and the specific diagnoses of the diseases, is important. This review will explore the current literature on the origin and nature of PM and their effects on human health. In addition, it will also highlight the approaches that have been adopted in order to diagnose the effects of PM using positron emission tomography (PET) or single-photon emission computerized tomography (SPECT).

• Journal of the Korean Society of Visualization
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• v.22 no.1
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• pp.34-39
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• 2024

With the fine particulate matter (PM) poses a serious threat to public health and the environment. The ultrafine PM in particular can cause serious problems. This study investigates the effectiveness of a submicron dry fog system in removing fine PM. Two methods are used to create fine dust particles: burning incense and utilizing an aerosol generator. Results indicate that the dry fog system effectively removes fine dust particles, with a removal efficiency of up to 81.9% for PM₁₀ and 61.9% for PM_2.5 after 30 minutes of operation. The dry fog, characterized by a mean size of approximately 1.5 ㎛, exhibits superior performance in comparison to traditional water spraying methods, attributed to reduced water consumption and increased contact probability between water droplets and dust particles. Furthermore, experiments with uniform-sized particles which sizes are 1 ㎛ and 2 ㎛ demonstrate the system's capability in removing ultrafine PM. The proposed submicron dry fog system shows promise for mitigating fine dust pollution in various industrial settings, offering advantages such as energy consumption and enhanced safety for workers and equipment.