• Particle and aerosol research
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• v.16 no.1
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• pp.9-17
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• 2020

The characteristics of particulate matter made from daily cooking at a Korean residential apartment house with three dwellers had been investigated for about 3 months. All data were recorded by an optical particle counter every minute at the kitchen. Types of cooking such as boiling, frying, and grilling that performed in the house were listed. Boiling only was used in 32% cases among total 234 meals. Frying and grilling were 14% and 11%, respectively. From an initial indoor particulate matter smaller than 10 ㎛ in diameter, the increases due to cooking are reported by size. In case of boiling, PM at 1-10 ㎛ size and under 1 ㎛ size little increased. Normally, particles from oil or combustion in a process of frying or grilling increased indoor PM. In a case of grilling, particle mass concentration in a region of 1-10 ㎛ in diameter increased as much as 295 ㎍/㎥. Mass concentration of particles smaller than 1 ㎛ increased as much as 33 ㎍/㎥.

• Proceedings of the Korean Society of Toxicology Conference
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• 2003.10b
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• pp.153-153
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• 2003

It has been reported that exposure to particulate matter is linked to increase lung cancer risk. Recently it is confirmed that diesel exhaust particulate matter (DEP), which derives from diesel powered vehicles, is contributed as a major pollutant in urban air-borne particulate matter.(omitted)

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.619-631
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• 2018

Functional data analysis continues to attract interest because advances in technology across many fields have increasingly permitted measurements to be made from continuous processes on a discretized scale. Particulate matter is among the most harmful air pollutants affecting public health and the environment, and levels of PM10 (particles less than 10 micrometers in diameter) for regions of California remain among the highest in the United States. The relatively high frequency of particulate matter sampling enables us to regard the data as functional data. In this work, we investigate the dominant modes of variation of PM10 using functional data analysis methodologies. Our analysis provides insight into the underlying data structure of PM10, and it captures the size and temporal variation of this underlying data structure. In addition, our study shows that certain aspects of size and temporal variation of the underlying PM10 structure are associated with changes in large-scale climate indices that quantify variations of sea surface temperature and atmospheric circulation patterns.

• Fisheries and Aquatic Sciences
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• v.27 no.2
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• pp.111-121
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• 2024

Particulate matter (PM) is a mixture of microscopic solid inhalable particles including airborne liquid droplets, and it is implicated with several diseases. The eye does not have a protective barrier among the human organs, consequently it get directly exposed to environmental substances such as PM. The scarcity of treatments for damage to the eyesight and the vision and eye structure being closely related to the structure and function of the central nervous system highlights the cruciality of novel therapeutics. In this study was conducted using in vivo zebrafish vertebrate model which is a useful model due to the conserved genes between human. We found that protective effect of Octopus minor extract against particulate matter-induced adverse effects on eye development in zebrafish (Danio rerio) embryos by regulating antioxidant and anti-inflammatory mRNA expression.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2006.10a
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• pp.169-170
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• 2006

• Elastomers and Composites
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• v.58 no.4
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• pp.161-172
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• 2023

Because particulate matter has emerged as a major contributor to air pollution, the tire industry has conducted studies to reduce particulate matters from tires by improving tire performance. In this study, we compared the conventional blending method, in which rubber, filler, and additives are mixed simultaneously, to the Y-blending method, in which masterbatches are blended. We manufactured carbon black (CB)-filled natural rubber (NR)/butadiene rubber (BR) blend and silica-filled epoxidized NR/BR blend compounds to compare the effects of the two blending methods on the physical properties of the compounds and the amount of particulate matter generated. The Y-blending method provided uniform filler distribution in the heterogeneous rubber matrix, improved processability, and exhibited low rolling resistance. This method also improved physical properties owing to the excellent filler-rubber interaction. The results obtained from measuring the generation of particulate matter indicated that, the Y-blending method reduced PM_2.5 particulate matter generation from the CB-filled and silica-filled compounds by 38% and 60%, and that of PM₁₀ by 29% and 67%, respectively. This confirmed the excellence of the Y-blending method regarding the physical properties of truck bus radial tire tread compounds and reduced particulate matter generated.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2003.05a
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• pp.195-195
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• 2003

It is well known that diesel exhaust particulate matter contains mutagenic PAHs, such as benzo[${\alpha}$]pyrene, benz[${\alpha}$]anthracene, chrysene, etc. Therefore it is suspected that these chemicals act on estrogen receptor and reveal endocrine-disrupting effects. Recent attention has focused on causative chemicals of endocrine-disrupting effects. We examined the estrogenic activity of respirable diesel exhaust particulate matter derived from diesel powered vehicle. PM2.5 diesel exhaust of vehicle was collected using a high volume sampler equipped with a cascade impactor. Diesel exhaust samples were fractionated according to EPA methods. The presence of estrogenic and antiestrogenic chemicals in PM 2.5 diesel exhaust was determined using E-screen assay. To quantitatively assess the estrogenic and antiestrogenic activities in diesel exhaust particulate matter, estradiol equivalent concentration (bio-EEQ) was calculated by comparing the concentration response curve of the sample with those of the estrogen calibration curve. Weak estrogenic activities and strong antiestrogenic activities were detected in the crude extract and moderately polar fractions. Higher antiestrogenic potency was observed with higher EROD activities in aliphatic and aromatic compounds fraction. In conclusion, estrogenic/antiestrogenic-like activities were present in diesel exhaust particulate matter. However, the health consequences of this observation was unknown, the presence of these activities may contribute to and exacerbate adverse health effect evoked by diesel exhaust particulate matter.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.620-622
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• 2018

The need for particulate matter prediction algorithms has increased as social interest in the effects of human on particulate matter increased. Many studies have proposed statistical modelling and machine learning techniques based prediction models using weather data, but it is difficult to accurately set the environment and detailed conditions of the models. In addition, there is a need to design a new prediction model for missing data in domestic weather monitoring station. In this paper, fine dust prediction is performed using multi-layer perceptron network as a previous study for particulate matter prediction. For this purpose, a prediction model is designed based on weather data of three monitoring station and the suitability of the algorithm for particulate matter prediction is evaluated through comparison with actual data.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.471-479
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• 2019

The purpose of this study was to investigate the determinants of preventive behavior intention to the particulate matter. The results based on the survey of 280 university students showed that the perceived susceptibility and barriers to the particulate matter do not have statistically significant effects on the preventive behavior intention. However, perceived severity and benefits, subjective norm, and self-efficacy to the particulate matter had statistically significant positive effects on the preventive behavior intention. The results of this study suggested that communication strategies to increase perceived severity and benefits, subjective norm and self-efficacy should be required to improve the degree of preventive behavior intention to the particulate matter of college students. It is expected to contribute explaining preventive actions against environmental hazards such as air pollution in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.20-26
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• 2019

Due to the human influence of particulate matter, various studies are being conducted to predict it using past data measured in the atmospheric environment monitoring network. However, it is difficult to precisely set the measurement environment and detailed conditions of the previously designed predictive model, and it is necessary to design a new predictive model based on the existing research results because of the problems such as the missing of the weather data. In this paper, as a previous study for particulate matter prediction, the conformity of the algorithm for particulate matter prediction was evaluated by designing the prediction model through the multiple linear regression and the artificial neural network, which are machine learning algorithms. As a result of the prediction performance comparison through RMSE, 18.13 for the MLR model and 14.31 for the MLP model, and the artificial neural network model was more conformable for predicting the particulate matter concentration.