• Journal of Life Science
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• v.30 no.8
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• pp.722-730
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• 2020

As one of the most serious health risk factors, air pollution can no longer be ignored. Particulate matter (PM) is an important and harmful component of air pollution that originates from a variety of sources. Numerous recent studies have linked PM to a range of conditions including cancer, cardiovascular, respiratory, and skin disease. The eye, despite being directly exposed to air pollution, has been investigated in very few of these studies. In this review, we describe the evidence from in vitro and in vivo studies, as well as epidemiological investigations, that supports the association between exposure to PM and the development of ocular conditions such as surface and retinal disease and glaucoma. Based on the results of previous studies, we suggest that PM exposure can lead to oxidative stress, inflammation, autophagy, and, ultimately, ocular surface disease. Nevertheless, almost no studies focus on ocular surface damage from PM while some epidemiological and clinical studies report on the posterior of the eye. However, the underlying pathological mechanisms in the posterior following PM exposure have yet to be identified, and further studies are therefore warranted of the ocular surface as well as the posterior part of the eye.

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.717-726
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• 2018

PM(Particulate Matter) cause serious diseases of air pollution. Most of the studies have analyzed local distribution trends using satellite images or modeling techniques. However,the method using the spatial interpolation method based on the meteorological value is insufficient in Korea. In this study, monthly spatial distribution of $PM_{10}$ and $PM_{2.5}$ in January, February, March, and April of 2018 Seoul Metropolitan City were analyzed based on 39 PM monitoring networks. In addition, a distribution map showing the difference between $PM_{10}$ and $PM_{2.5}$ was based on the distribution obtained through this study. The regions of high $PM_{10}$ and $PM_{2.5}$ emissions were selected. In addition, the correlation between $PM_{10}$ and $PM_{2.5}$ was confirmed through the distribution map. This study analyzed the spatial distribution variation results of analyzing $PM_{10}$ and $PM_{2.5}$ in Seoulthrough spatial analysis technique. As a result of this study, it was confirmed that $PM_{10}$ shows high measured value on the roadside measurement station.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1891-1900
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• 2021

Fine particulate matter (FPM; diameter ≤ 2.5 ㎛) is frequently found in metropolitan areas due to activities associated with rapid urbanization and population growth. Many adolescents spend a substantial amount of time at school where, for various reasons, FPM generated outdoors may flow into indoor areas. The aims of this study were to estimate FPM concentrations and categorize types of FPM in schools. Meteorological and chemical variables as well as satellite-based aerosol optical depth were analyzed as input data in a random forest model, which applied 10-fold cross validation and a grid-search method, to estimate school FPM concentrations, with four statistical indicators used to evaluate accuracy. Loose and strict standards were established to categorize types of FPM in schools. Under the former classification scheme, FPM in most schools was classified as type 2 or 3, whereas under strict standards, school FPM was mostly classified as type 3 or 4.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2003.11a
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• pp.79-80
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• 2003

대기환경 개선대책으로 인해 TSP와 PM$_{10}$의 농도는 안정된 경향을 나타내고 있으나, 미세먼지(PM$_{2.5}$)와 같은 2차 대기오염물질의 오염도는 오히려 증가하고 있는 추세에 있으며, 최근 대기오염물질에 대한 관심의 증가는 2차 오염물질(Secondary air pollutants)로 분류되는 오존과 미세먼지에 대한 연구가 활발히 진행되고 있다. 특히 이들 미세먼지는 대부분 가스상 물질로 배출된 대기오염물질이 대기중에서 광화학반응(Photochemical reaction)이나 각종 상변화를 거쳐 생성되기 때문에 그 생성기작이 복잡한 것으로 알려져 있다. (중략)략)략)

• Applied Chemistry for Engineering
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• v.31 no.1
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• pp.84-89
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• 2020

Particulate matters of PM_2.5, particularly focusing on 0.1~1 ㎛ decrease the efficiency of dust-collector due to the brownian-motion. This study is to verify the effect of coagulant on the particle size distributions of potassium and PM_2.5. The activated coagulant was spayed to the coal fired fluidized bed combustion boiler by the weight ratio of 1,200 : 1 = coal : coagulant, and the size distributions of captured particles at both the cyclone (FP) and electrostatic precipitator (EP) were measured. As the result of XRP analysis, the potassium content of FP increased to 13.33% (averagely from 1.65% to 1.87%) and, in EP at 17.68% (averagely from 1.65% to 2.03%). And it was confirmed by the particle size distribution analyzer and SEM image analysis that the distribution rates of PM_2.5 decreased at 89.53% on average in FP, and at 88.57% in EP. The total dust concentration (mg/㎥) confirmed by tele-monitering system (TMS) decreased during the primary test from 2.6 to 1.7~1.9 and also the secondary test from 2.9 to 1.7~1.9.

• The Journal of Bigdata
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• v.6 no.2
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• pp.39-49
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• 2021

Recently, large and diverse weather data are being collected by sensors from various sources. Efforts to predict the concentration of fine dust through machine learning are being made everywhere, and this study intends to compare PM10 and PM2.5 prediction models using data from 840 outdoor air meters installed throughout the city. Information can be provided in real time by predicting the concentration of fine dust after 5 minutes, and can be the basis for model development after 10 minutes, 30 minutes, and 1 hour. Data preprocessing was performed, such as noise removal and missing value replacement, and a derived variable that considers temporal and spatial variables was created. The parameters of the model were selected through the response surface method. XGBoost, Random Forest, and Deep Learning (Multilayer Perceptron) are used as predictive models to check the difference between fine dust concentration and predicted values, and to compare the performance between models.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.807-817
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• 2014

This paper considers multivariate time series modelling of PM10 data in Korea collected from 2008 to 2011. We consider both temporal and spatial dependencies of PM10 by applying the sparse vector autoregressive (sVAR) modelling proposed by Davis et al. (2013). It utilizes the partial spectral coherence to measure cross correlation between different regions, in turn provides the sparsity in the model while balancing the parsimony of model and the goodness of fit. It is also shown that sVAR performs better than usual vector autoregressive model (VAR) in forecasting.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2022.10a
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• pp.239-240
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• 2022

공사장에서 발생하고 있는 먼지는 현장 근로자뿐만 아니라 인근 주민들의 건강에 치명적인 영향을 미치고 있다. 공사장 인근 주민들이 환경분쟁조정위원회에 먼지피해에 대한 피해배상을 청구하고 있지만 공사장 먼지피해 수인한도 초과여부를 확인하기 위한 측정, 예측, 평가가 어려워 먼지피해에 대한 보상이 제대로 이루어지지 않고 있다. 공사장 먼지관리의 법적기준이며 먼지 저감에 대한 구체적인 방법을 제시하고 있는"비산먼지 억제조치기준"의 준수 등을 점수로 평가하여 일정 점수 이하인 경우 피해배상액을 차등 적용하는 방안을 제안한다. 본 안이 제도화된다면 건설사는 먼지피해 배상액 지출을 줄이기 위해 현재보다 한층 더 먼지 저감 노력을 강화할 것이기에 먼지 발생을 획기적으로 줄일 수 있어서 현장 근로자 및 인근 주민들의 먼지피해를 최소화할 수 있으며, 먼지로 인한 환경, 보건 법규위반예방과 쾌적한 작업환경으로 노동 생산성 확보와 먼지로 인한 피해 배상액 지급 등 손실을 줄일 수 있을 것이라고 생각한다.

• Korean Journal of Environment and Ecology
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• v.38 no.2
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• pp.217-226
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• 2024

This study aimed to investigate the influence of anions in the air on the purification of fine dust (PM10 and PM2.5) and to evaluate the effects of plants on the generation of anions in the air and the purification of fine dust. Subsequently, the fine dust reduction models were compared according to each factor and plant volume. The characteristics of anion generation by each factor were observed to be in the order of Type N.I (negative ion generator; 204,133.33 ea/cm³) > Type P₃₀ (plant vol. 30%; 362.55 ea/cm³) > Type C (control; 46.22 ea/cm³), indicating that the amount of anion generation in the anion generator treatment group and the plant arrangement group were approximately 4,417 times and 7 times higher, respectively, than that in the untreated group. Consequently, the fine dust reduction characteristics by anion generation source showed that for PM10, Type NI had a purification efficiency 2.52 times higher than Type C, and Type P₃₀ was 1.46 times higher, while for PM2.5, Type NI had a purification efficiency 2.26 times higher than Type C, and Type P₃₀ was 1.31 times higher. The efficiency of fine dust purification by plant volume was in the order of Type P₂₀ (84.60 minutes) > Type P₃₀ (106.50 minutes) = Type P₂₅ (115.50 minutes) = Type P₁₅ (117.60 minutes) > Type P₅ (125.25 minutes) = Type P₁₀ (129.75 minutes), and for ultrafine dust, Type P₂₀ (104.00 minutes) > Type P₃₀ (133.20 minutes) = Type P₂₅ (144.00 minutes) = Type P₁₅ (147.60 minutes) > Type P₅ (161.25 minutes) = Type P₁₀ (168.00 minutes). Thus, a quantitative analysis of the anions and plants for purifying fine dust and suggested matters to be considered for future green space planning and plant planting considering fine dust purification.

• Journal of the Korean earth science society
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• v.43 no.2
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• pp.265-282
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• 2022

This study analyzes the impact of long-range transport of biomass burning emissions from northeastern China on the concentration of particulate matter of diameter less than 10 ㎛ (PM₁₀) in Korea using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Korea was impacted by anthropogenic emissions from eastern China, dust storms from northern China and Mongolia, and biomass burning emissions from northeast China between April 4-and 7, 2020. The contributions of long-range PM₁₀ transport were calculated by separating biomass burning emissions from mixed air pollutants with anthropogenic emissions and dust storms using the zeroing-out method. Further, the radiation energy budget over land and sea around the Korean Peninsula was analyzed according to the distribution of biomass burning emissions. Based on the WRF-Chem simulation during April 5-6, 2020, the contribution of long-range transport of biomass burning emissions was calculated as 60% of the daily PM₁₀ average in Korea. The net heat flux around the Korean Peninsula was in a negative phase due to the influence of the large-scale biomass burning emissions. However, the contribution of biomass burning emissions was analyzed to be <45% during April 7-8, 2020, when the anthropogenic emissions from eastern China were added to biomass burning emissions, and PM₁₀ concentration increased compared with the concentration recorded during April 5-6, 2020 in Korea. Furthermore, the net heat flux around the Korean Peninsula increased to a positive phase with the decreasing influence of biomass burning emissions.