• Annals of Occupational and Environmental Medicine
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• v.34
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• pp.21.1-21.13
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• 2022

Background: Because particulate matter (PM) and asthma are closely related, the prevalence of school absence among adolescents with asthma can be affected by the concentration of PM. We aimed to investigate the relationship between school absences due to asthma and the total number of days that the PM concentration exceeded the standard. Methods: We used the data from the 16th Korea Youth Risk Behavior Survey and the PM levels of 17 metropolitan cities and provinces gathered from the AirKorea. Information on the characteristics of asthmatic adolescents and the prevalence of school absence was obtained using a questionnaire, while the PM levels based on the total number of days with poor and very poor PM grades were collected from the AirKorea website. Both χ² test and logistic regression analysis were performed using the weights presented in the original dataset. Results: In the case of particulate matter of 10 microns in diameter or smaller (PM₁₀), the odds ratio (OR) after adjusting for confounders (sex, school year, body mass index, smoking history, diagnosis of allergic rhinitis, diagnosis of atopic dermatitis and city size) was 1.07 (95% confidence interval [CI]: 1.01-1.13) for absents due to asthma when the total days of poor and very poor grades of PM₁₀ (81 ㎍/m³ or higher) increased by 1 day. In the analysis of particulate matter of 2.5 microns in diameter or smaller (PM_2.5), the OR after adjusting for confounders was 1.01 (95% CI: 1.00-1.03) for absents due to asthma when the total number of days with poor and very poor PM_2.5 grades (36 ㎍/m³ or higher) increased by 1 day. Conclusions: A significant association was observed between the total number of days of poor and very poor PM₁₀ and PM_2.5 grades and school absence due to asthma; PM can cause asthma exacerbation and affect the academic life.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.259-272
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• 2023

In this paper, we suggest a new method for the prediction of sharp changes in particulate matter (PM₁₀) using quantile mapping. To predict the current PM₁₀ density in Seoul, we consider PM₁₀ and precipitation in Baengnyeong and Ganghwa monitoring stations observed a few hours before. For the PM₁₀ distribution estimation, we use the extreme value mixture model, which is a combination of conventional probability distributions and the generalized Pareto distribution. Furthermore, we also consider a quantile generalized additive model (QGAM) for the relationship modeling between precipitation and PM₁₀. To prove the validity of our proposed model, we conducted a simulation study and showed that the proposed method gives lower mean absolute differences. Real data analysis shows that the proposed method could give a more accurate prediction when there are sharp changes in PM₁₀ in Seoul.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.51.1-51.15
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• 2023

Background: Recently, there has been increasing worldwide concern about outdoor air pollution, especially particulate matter (PM), which has been extensively researched for its harmful effects on the respiratory system. However, sufficient research on its effects on cardiovascular diseases, such as hypertension, remains lacking. In this study, we examine the associations between PM levels and hypertension and hypothesize that higher PM concentrations are associated with elevated blood pressure. Methods: A total of 133,935 adults aged ≥ 40 years who participated in the Korean Genome and Epidemiology Study were analyzed. Multiple linear regression analyses were conducted to investigate the short- (1-14 days), medium- (1 and 3 months), and long-term (1 and 2 years) impacts of PM on blood pressure. Logistic regression analyses were conducted to evaluate the medium- and long-term effects of PM on blood pressure elevation after adjusting for sex, age, body mass index, health-related lifestyle behaviors, and geographic areas. Results: Using multiple linear regression analyses, both crude and adjusted models generated positive estimates, indicating an association with increased blood pressure, with all results being statistically significant, with the exception of PM levels over the long-term period (1 and 2 years) in non-hypertensive participants. In the logistic regression analyses on non-hypertensive participants, moderate PM₁₀ (particulate matter with diameters < 10 ㎛) and PM_2.5 (particulate matter with diameters < 2.5 ㎛) levels over the long-term period and all high PM₁₀ and PM_2.5 levels were statistically significant after adjusting for various covariates. Notably, high PM_2.5 levels of the 1 year exhibited the highest odds ratio of 1.23 (95% confidence interval: 1.19-1.28) after adjustment. Conclusions: These findings suggest that both short- and long-term exposure to PM is associated with blood pressure elevation.

• Journal of odor and indoor environment
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• v.17 no.4
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• pp.303-314
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• 2018

The urban railway system is a convenient public transportation system, as it carries many people without increasing traffic congestion. However, air quality in urban railway environments is worse than ambient air quality due to the internal location of the source of air pollutants and the isolated space. In this paper, characteristics of particulate matter (PM) pollution in urban railway environments are described from the perspective of diurnal variation, chemical composition and source apportionment of PM. PM concentrations in concourse, platform, passenger cabin, and tunnel are summarized through an analysis of 34 journal articles published in Korea and overseas. This information will be helpful in developing effective policies to reduce PM pollution in urban railway environments.

• Microbiology and Biotechnology Letters
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• v.49 no.3
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• pp.425-431
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• 2021

The use of big data may facilitate the recognition and interpretation of causal relationships between disease occurrence and climatic variables. Considering the immense contribution of rhinoviruses in causing respiratory infections, in this study, we examined the effects of various climatic variables on the seasonal epidemiology of rhinovirus infections in the temperate climate of Cheonan, Korea. Trends in rhinovirus detection were analyzed based on 9,010 tests performed between January 1, 2012, and December 31, 2018, at Dankook University Hospital, Cheonan, Korea. Seasonal patterns of rhinovirus detection frequency were compared with the local climatic variables for the same period. Rhinovirus infection was the highest in children under 10 years of age, and climatic variables influenced the infection rate. Temperature, wind chill temperature, humidity, and particulate matter significantly affected rhinovirus detection. Temperature and wind chill temperature were higher on days on which rhinovirus infection was detected than on which it was not. Conversely, particulate matter was lower on days on which rhinovirus was detected. Atmospheric pressure and particulate matter showed a negative relationship with rhinovirus detection, whereas temperature, wind chill temperature, and humidity showed a positive relationship. Rhinovirus infection was significantly related to climatic factors such as temperature, wind chill temperature, atmospheric pressure, humidity, and particulate matter. To the best of our knowledge, this is the first study to find a relationship between daily temperatures/wind chill temperatures and rhinovirus infection over an extended period.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.341-349
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• 2020

The present study aims to investigate the sequential mediation model in the relationships between optimistic bias, subjective perception, risk perception, and the future time perspective in terms of a particulate matter and depression, using the sequential mediation model. An online self-reported survey was conducted on 545 participants who agreed to participate in the current study. We considered depression as a dependent variable, optimistic bias as an independent variable, and subjective perception of particulate matter, the risk perception of particulate matter, future-time-perspective as mediators. The sequential mediation analysis was conducted using the SPSS Macro. The results show that optimistic bias was not directly related to depression, but was related to indirect paths through the subjective perception of particulate matter, the risk perception of particulate matter, and future time perspective. More specifically, the lack of optimistic bias was related to a tendency to subjectively perceive the quality of air pollution more seriously and a limited future time perspective, which subsequently related to depression. Future studies should pay more attention to the effects of particulate matter on the quality of life and mental health.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.98-103
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• 2013

Conventional method to estimate mass of particulate matter accumulated in diesel particulate filter is to use pressure difference between upstream and downstream of the filter. Then measured pressure difference should be compared that of clean condition which is no particulate matter accumulated in DPF. During regeneration soot oxidation is also estimated by same method. This methodology, however, has demerit on accuracy because of pressure difference deviation of clean DPFs and pressure difference caused by non-carbon based PM which is different from that of caused by carbon based PM. This study suggests new methodology to estimate accumulated soot oxidation rate through exhaust gas characteristics during regeneration. Results, more high accuracy of soot oxidation was obtained by analysis of relationship between fuel mass and concentration of carbon dioxide and oxygen.

• Land and Housing Review
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• v.10 no.3
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• pp.1-7
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• 2019

Indoor particulate matter(PM) is a carcinogen and needs to be removed and managed. It is generally reduced and removed through ventilation and filtration. Owing to the recent occurrence of high-concentration fine dust and yellow dust in the atmosphere, however, it is difficult to expect the purification of indoor air through the simple introduction of the outside air. For residential buildings, in particular, they are highly dependent on natural ventilation but the lack of natural ventilation is worsening because concerns over the inflow of external pollutants are increasing. Therefore, this study designed and manufactured a window ventilation system that does not require a duct to improve the maintenance and management problems of general ventilation system, and constructed indoor PM concentration change data through performance evaluation.

• Journal of Korean Society for Atmospheric Environment
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• v.31 no.5
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• pp.461-471
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• 2015

Base on emission factors derived from National Institute of Environmental Research, Particulate matter from combined cycle power plants (CCPPs) has been estimated to be a important source of $PM_{10}$. Generally there is no serious emission of particulate matter in CCPPs. because the fuel of them is natural gas. But emission gas after long shut down season has very high dust content. Therefore $PM_{10}$ emission rate is dependent on its operation mode. In this study, particulate dispersion study for new city near CCPPs complex has performed using CALPUFF model for three case. $PM_{10}$ concentration has big difference between normal operation and 2 case start-up condition after long shutdown. In normal operating conditions, daily $0.32{\sim}0.50{\mu}g/m^3$ influence on of the surrounding area. But when 1~2 aerobic high concentration discharged conditions, average concentration is higher about $9.2{\sim}34.1{\mu}g/m^3$ than normal operating conditions.

• Journal of Energy Engineering
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• v.26 no.4
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• pp.45-56
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• 2017

Coal power plants produce electricity for the nation's power grid, but they also produce more hazardous air emissions than any other industrial pollution sources. The quantity is staggering, over 386,000 tons of 84 separate hazardous air pollutants spew from over 400 plants in 46 states. In South Korea also, annual coal ash generation from coal-fired power plants were about 6 million tons in 2015. Pollutants containing particulate matter 10, 2.5 (PM10, PM2.5), heavy metals and dioxins from coal-fired power plant. The emissions threaten the health of people who live near these power plants, as well as those who live hundreds of miles away. These pollutants that have long-term impacts on the environment because they accumulate in soil, water and animals. The present study is to investigate the physical and chemical characteristics of coal-fired power plant fly ash and bottom ash contains particulate matter, whose particulate sizes are lower than $PM_{10}$ and $PM_{2.5}$ and heavy metals. There are wide commercial technologies were available for monitoring the PM 2.5 and ultra-fine particles, among those carbonation technology is a good tool for stabilizing the alkaline waste materials. We collected the coal ash samples from different coal power plants and the chemical composition of coal fly ash was characterized by XRF. In the present laboratory research approach reveals that potential application of carbonation technology for particulate matter $PM_{10}$, $PM_{2.5}$ and stabilization of heavy metals. The significance of this emerging carbonation technology was improving the chemical and physical properties of fly ash and bottom ash samples can facilitate wide re use in construction applications.