• Journal of the Korean Medical Association
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• v.61 no.12
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• pp.749-755
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• 2018

Particulate matter is an air pollutant emitted from both natural and anthropogenic sources, and its adverse health effects have been well documented in time-series analyses and cohort studies. The effect size of particulate matter exposure-a roughly 0.5% increase in mortality for each $10{\mu}g/m^3$ increment of short-term exposure to particulate matter with aerodynamic diameter ${\leq}10{\mu}m$ and approximately a 10% increase for each $10{\mu}g/m^3$ increment of long-term exposure to particulate matter with aerodynamic diameter ${\leq}2.5{\mu}m$-is small compared to other risk factors, but the exposure is involuntary and affects the entire population, which makes particulate matter pollution an important public health issue. The World Health Organization and Korean government have both established guidelines for particulate matter concentrations, but the Korean guideline is less stringent than that of the World Health Organization. The annual mean concentration of particulate matter in Korea is decreasing, but the trend seems to be slowing. In addition to policy efforts to reduce particulate matter emission, personal approaches such as the use of face masks and air purifiers have been recommended. Personal approaches may not solve the fundamental problem, but can provide temporary mitigation until efforts to reduce emission make progress.

• Journal of Sensor Science and Technology
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• v.22 no.3
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• pp.191-196
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• 2013

A particulate matter sensor fabricated by MEMS process is proposed. It is developed to accommodate Euro6 on-board diagnostics regulation for diesel automobile. In the regulation, emission of diesel particulate matter is restricted to 9 mg/km. Particulate matter sensor is designed to use induced charges by charged particulate matter. To increase sensitivity of the sensor, groove is formed on sensor surface because wider surface area generates more induced charges. Sensitivity of the sensor is measured 10.6 mV/(mg/km) and the sensor shows good linearity up to 15.7 mg/km. Also its minimum detectable range is about 0.25 mg/km. It is suitable to detect failure of a diesel particulate filter which should filter particulate matter more than 9 mg/km. For removing accumulated particulate matter on the sensor which can disturb normal operation, platinum heater is designed on the backside of the sensor. The developed sensor can sense very low amount of particulate matter from exhaust gas in real-time with good linearity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.623-625
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• 2018

As the issue of particulate matter spreads, services for providing particulate matter information in real time are increasing. However, when a sensor node for collecting particulate matter is defective, a corresponding service may not be provided. To solve these problems, it is necessary to predict and deduce particulate matter. In this paper, a particulate matter prediction model is designed using artificial neural network algorithm based on past particulate matter and meteorological data to predict particulate matter. Also, the prediction results are compared by learning the input data of the model in the design stage.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1138-1148
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• 2021

In this study, we developed a 'shared-use mobility'-mounted local particulate matter monitoring terminal technology to measure the actual particulate matter concentration around me. As a mobile terminal device in the form of an IoT sensor platform, it is designed to be separated into a control module and a sensor module to minimize interference between sensors and to consider the optimal observation position of each sensor. As a result of the field test, it was confirmed that particulate matter was locally different depending on time and space even within the same area. In addition, it was confirmed that the concentration of particulate matter in the relevant section differed by up to 100 times compared to the surrounding area due to specific sources of particulate matter such as unpaved roads. In addition, we positively reviewed the applicability of the service in the real-time metaverse environment using this result. Through technological advancement and application of multiple shared-use mobilities, we expect to be able to provide new services for practical smart city air environment monitoring, such as localized particulate matter information, air pollution event information, and identification of causes of particulate matter.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.393-403
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• 2020

This study was aimed to investigate the factors influencing particulate matter behavior of particulate matter knowledge, particulate matter attitude among adolescents. A descriptive study design was used. Participants were 218 high school students in D city. The data were collected from May 13 to 24 2019. Collected data were analyzed by t-test, one-way ANOVA, Pearson's correlation coefficient, multiple regression using SPSS WIN 18.0 program. Results: The influential factor for particulate matter behavior was particulate matter attitude (β=0.52, p<.001). It was found that particulate matter education experience (β=0.08, p=.157), academic background of father (β=0.08, p=.288), academic background of mother (β=0.05, p=.463), particulate matter knowledge (β=-0.05, p=.415), residence with (β=-0.09, p=.126), school record (β=-0.02, p=.710) had no significant effect on teacher efficacy. In order to develop a program to increase the particulate matter behavior for youth, it is necessary to prepare a plan to improve the attitude of particulate matter.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.201-202
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• 2023

Now, there are many cases where materials are maintained at construction sites or standard construction prices are not in accordance with the applicable regulations, resulting in a lot of fine dust. Therefore, a particulate matter measurement system is applied not only to manage particulate matter at construction sites but also to reduce particulate matter .This study aims to evaluate the applicability of this particulate matter measurement system to the construction site through long-term measurement experiments using a light scattering method particulate matter measurement instrument at the construction site.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.540-542
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• 2019

With many announcements on air pollution and human effects from particulate matters, particulate matter forecasts are attracting a lot of public attention. As a result, various efforts have been made to increase the accuracy of particulate matter forecasting by using statistical modeling and machine learning technique. In this paper, the particulate matter AQI index prediction is performed using the multilayer perceptron neural network for particulate matter prediction. For this purpose, a prediction model is designed by using the meteorological factors and particulate matter concentration values commonly used in a number of studies, and the accuracy of the particulate matter AQI prediction is compared.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.5
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• pp.86-93
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• 2021

In order to remove particulate matter, functional construction materials are developed. However, there is no evaluation method and infrastructure for particulate matter removal rate. Therefore, the purpose of this study was to build a particulate matter removal rate test chamber and to present a method for particulate matter removal rate. As a result, since construction materials have effectiveness in an environment where particulate matter is generated, the particulate matter injection step was proposed as a comparison target. The evaluation of the particulate removal rate was proposed by relative comparison of the slope values obtained by linear regression analysis for all concentration values measured in the particulate matter injection step. In linear regression method, all measured values can be evaluated, and the variability can be evaluated with the coefficient of determination (R-square), so that the reliability of the particulate matter removal rate can be secured.

• Journal of Environmental Health Sciences
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• v.46 no.6
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• pp.719-725
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• 2020

Objectives: With the growth of national interest in fine particulate matter, many complaints about pollutants emitted from air pollution emitting facilities have arisen in recent years. In particular, it is thought that a large volume of particulate pollutants are discharged from workplaces that use Solid Refuse Fuel (SRF). Therefore, particulate contaminants generated from SRF were measured and analyzed in this study in terms of respective particle sizes. Methods: In this study, particulate matter in exhaust gas was measured by applying US EPA method 201a using a cyclone. This method measures Filterable Particulate Matter (FPM), and does not consider the Condensable Particulate Matter (CPM) that forms particles in the atmosphere after being discharged as a gas in the exhaust gas. Results: The mass concentration of Total Suspended Particles (TSP) in the four SRF-using facilities was 1.16 to 11.21 mg/Sm3, indicating a very large concentration deviation of about 10 times. When the fuel input method was the continuous injection type, particulate matter larger than 10 ㎛ diameter showed the highest particle size fraction, followed by particulate matter smaller than 10 ㎛ and larger than 2.5 ㎛, and particulate matter of 2.5 ㎛ or less. Contrary to the continuous injection type, the batch injection type had the smallest particle size fraction of particulate matter larger than 10 ㎛. The overall particulate matter decreased as the operating load factor decreased from 100% to 60% at the batch input type D plant. In addition, as incomplete combustion significantly decreased, the particle size fraction also changed significantly. Both TSP and heavy metals (six items) satisfied the emissions standards. The measured value of the emission factor was 38-99% smaller than the existing emissions factor. Conclusions: In the batch injection facility, the particulate matter decreased as the operating load factor decreased, as did the particle size fraction of the particulate matter. These results will help the selection of effective methods such as reducing the operating load factor instead of adjusting the operating time during emergency reduction measures.

• Journal of Korean Biological Nursing Science
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• v.20 no.1
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• pp.20-29
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• 2018

Purpose: This study aimed to determine the relationships between particulate matter risk perception, knowledge, and perceived barriers and health-promoting behaviors among college students. Methods: Data for this cross-sectional study were collected from September 1 to 30, 2017. The study sample consisted of 85 students from a university, Seoul. Students not living in the Seoul metropolitan area during the spring 2017 semester were excluded from participation. Pearson's correlation coefficient was used to identify relationships among study variables. Results: A significant positive correlation existed between particulate matter risk perception and health-promoting behaviors related to particulate matter (r= .51, p< .001). Among the risk perception subdomains, attention (r= .47, p< .001) and health effect (r= .55, p< .001) showed strong positive relationships with health-promoting behaviors. No significant relationships were found between knowledge (r= .12, p= .288) or perceived barriers (r= -.12, p= .264) and health-promoting behaviors related to particulate matter. Conclusion: Based on the study results, strategies for enhancing particulate matter risk perception are needed to increase the level of health-promoting behaviors related to particulate matter among college students.