• Particle and aerosol research
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• v.19 no.4
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• pp.111-128
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• 2023

Chungcheongnam-do has various emission sources, including large-scale facilities such as power plants, steel and petrochemical industry complexes, which can lead to the severe PM pollution. Here, we measured concentrations of PM₁₀, PM_2.5, and its metallic elements at a suburban site in Taean, Chungcheongnam-do from September 2017 to June 2022. During the measurement period, the average concentrations of PM₁₀ and PM_2.5 were 58.6 ㎍/m³ (9.6~379.0 ㎍/m³) and 35.0 ㎍/m³ (6.1~132.2 ㎍/m³), respectively. The concentration of PM₁₀ and PM_2.5 showed typical seasonal variation, with higher concentration in winter and lower concentration in summer. When high concentrations of PM_2.5 occurred, particulary in winter, the fraction of Zn and Pb components considerably increased, indicating a significant contribution of Zn and Pb to high-PM_2.5 concentration. In addition, Zn and Pb exhibited the highest correlation coefficient among all other metallic elements of PM_2.5. A backward trajectory cluster analysis and CPF model were performed to examine the origin of PM_2.5. The high concentration of PM_2.5 was primarily influenced by emissions from industrial complexes located in the northeast and northwest areas.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.1
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• pp.20-28
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• 2014

The three air quality monitoring sites, analysed simultaneously PM-10 and PM-2.5, ie. Ihyeondong in industrial area, Manchondong in residential area, Pyeongnidong in streetside, among 13 air quality monitoring sites in Daegu area, were investigated the concentration distribution characteristics of PM-2.5 and PM-10 in the last 2 years (2011~2012). PM-10 concentrations exceeded annual average reference value ($50{\mu}g/m^3$) in Ihyeondong ($52.5{\mu}g/m^3$) and Pyeongnidong ($60.9{\mu}g/m^3$) but satisfied in Manchondong ($44.9{\mu}g/m^3$). All PM-2.5 concentrations exceeded EPA annual standard value of the United States ($15{\mu}g/m^3$) in three points, but also exceeded new control annual standard value ($25{\mu}g/m^3$) coming into effect in 2015. Seasonal concentration of PM-10 appeared the order of spring > winter > fall > summer, and in the case of PM-2.5, the order was winter > spring > fall > summer. Monthly concentrations of PM-10 and PM-2.5 were highest in February and lowest in September. Diurnal concentrations of PM-10 and PM-2.5 increased from 7:00 AM, and recorded the highest concentration between 10:00 AM and 11:00 AM. And after 6:00 PM it lowered continuously and tended to show fixed concentrations from evening until early morning. In addition, the concentration of fine particles during the week was higher than the weekend. The fluctuation in industrial area was larger than the residential area. At the PM-2.5/PM-10 ratio, summer was generally high, spring was the lowest. And, when yellow sand occurred, it was 0.32 to 0.42. It was very low compared to 0.54 to 0.64 during non-yellow sand times. This paper for the state and the characteristics of Daegu' fine particles (PM-10, PM-2.5) will be valuable to future researches of fine particles and air pollution management.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.2
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• pp.1-17
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• 2022

This study analyzed the PM_2.5 pattern by using data measured for one year from June 2020 to May 2021 by 21 low-cost sensors installed near the Changwon National Industrial Complex in Changwon, Gyeongsangnam-do. For the PM_2.5 pattern, the land use types around the measuring points and meteorological factors such as air temperature and wind speed were considered. The PM_2.5 concentration was high from November to March in winter, and from 1 to 9 in the morning and early in the morning by time zone. The concentration of PM_2.5 was higher as it got closer to the industrial area, but the concentration was lower in the residential area and public facility area. In terms of meteorological factors, the higher the air temperature and wind speed, the lower the concentration of PM_2.5. As a result of this study, it was possible to identify the PM_2.5 patter near Changwon National Industrial Complex. This result will be useful data that can be used in urban and environmental planning to improve air quality including PM_2.5 in urban area in the future.

• Journal of Environmental Impact Assessment
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• v.19 no.4
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• pp.431-441
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• 2010

In this study, inter-comparison for PM-2.5 was undertaken. The PM-2.5 mass concentrations using the gravimetric and beta-attenuation methods were compared during the winter in 2007. Two different types of conventional filter-based measurements (Cyclone type and Impactor type) were also collocated and the measurement data was compared with each other. As a result, continuous PM-2.5 data using beta attenuation method show a comparable mass concentration with gravimetric measurement when the inlet of beta-gauge sampler is heated. The results also showed that the cyclone type shows a little high PM-2.5 concentration than Impactor type. In all the sampling cases, the correlations between measurement methods are high. Subsequently, this study suggests that highly correlated relationship between PM-2.5 measurement instruments can be obtained through the inter-comparison results based on filterb-ased gravimetric method and more intensive measurement and theoretical studies are needed in order to clarify the measurement errors for different sampler types.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

• The Journal of Korean Physical Therapy
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• v.14 no.2
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• pp.172-180
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• 2002

The purpose of this paper was to make an analysis of anaerobic power in professional female basketball players using the Wingate Test Method with bicycle ergometer. Twenty-three subjects(age $21.6\pm2.8years$, body height $178.0\pm7.4cm$, body weight $70.3\pm7.4kg$) were selected from professional female basketball team whose careers were over 10years and participated in this investigation. Each subject peformed a Wingate anaerobic power test to determine total work, peak power, mean power, fatigue index and blood lactate concentration. The following were obtained from result data analysis; 1. The Total Work of athletes was a $1128.7\pm120.6watt$ 2. The Peak Power of athletes was a $449.5\pm53.1watt$ 3. The Mean Power of athletes was a $369.1\pm39.4watt$ 4. The Fatigue Index of athletes was a $33.5\pm6.9\%$ 5. The blood lactate concentration was $1.85\pm0.85mM/L$ at the normal state and $3.16\pm1.53mM/L$ at the after Wingate test. The blood lactate concentration was $6.96\pm0.81mM/L$ after 3 minute and $6.95\pm1.05mM/L$ after 5 minutes.

• Asian Journal of Atmospheric Environment
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• v.12 no.2
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• pp.139-152
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• 2018

The urban model inter-comparison study (UMICS) was conducted in order to improve the performance of air quality models (AQMs) for simulating fine particulate matter ($PM_{2.5}$) in the Greater Tokyo Area of Japan. UMICS consists of three phases: the first phase focusing on elemental carbon (UMICS1), the second phase focusing on sulfate, nitrate and ammonium (UMICS2), and the third phase focusing on organic aerosol (OA) (UMICS 3). In UMICS2/3, all the participating AQMs were the Community Multiscale Air Quality modeling system (CMAQ) with different configurations, and they similarly overestimated $PM_{2.5}$ nitrate concentration and underestimated $PM_{2.5}$ OA concentration. Various sensitivity analyses on CMAQ configurations, emissions and boundary concentrations, and meteorological fields were conducted in order to seek pathways for improvement of $PM_{2.5}$ simulation. The sensitivity analyses revealed that $PM_{2.5}$ nitrate concentration was highly sensitive to emissions of ammonia ($NH_3$) and dry deposition of nitric acid ($HNO_3$) and $NH_3$, and $PM_{2.5}$ OA concentration was highly sensitive to emissions of condensable organic compounds (COC). It was found that $PM_{2.5}$ simulation was substantially improved by using modified monthly profile of $NH_3$ emissions, larger dry deposition velocities of $HNO_3$ and $NH_3$, and additionally estimated COC emissions. Moreover, variability in $PM_{2.5}$ simulation was estimated from the results of all the sensitivity analyses. The variabilities on CMAQ configurations, chemical inputs (emissions and boundary concentrations), and meteorological fields were 6.1-6.5, 9.7-10.9, and 10.3-12.3%, respectively.

• Journal of Environmental Health Sciences
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• v.50 no.1
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• pp.6-15
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• 2024

Background: People's activities have been restricted due to the COVID-19 pandemic. These changes in activity patterns may lead to a decrease in fine particulate matter (PM_2.5) concentrations. Additionally, the level of population exposure to PM_2.5 may be changed. Objectives: This study aimed to analyze the impact of population movement and meteorological factors on the distribution of PM_2.5 concentrations before and after the outbreak of COVID-19. Methods: The study area was Guro-gu in Seoul. The research period was selected as January to March 2020, a period of significant population movement changes caused by COVID-19. The evaluation of the dynamic population was conducted by calculating the absolute difference in population numbers between consecutive hours and comparing them to determine the daily average. Ambient PM_2.5 concentrations were estimated for each grid using ordinary kriging in Python. For the population exposure assessment, the population-weighted average concentration was calculated by determining the indoor to outdoor population for each grid and applying the indoor to outdoor ratio to the ambient PM_2.5 concentration. To assess the factors influencing changes in the ambient PM_2.5 concentration, a statistical analysis was conducted, incorporating population mobility and meteorological factors. Results: Through statistical analysis, the correlation between ambient PM_2.5 concentration and population movement was positive on both weekends and weekdays (r=0.71, r=0.266). The results confirmed that most of the relationships were positive, suggesting that a decrease in human activity can lead to a decrease in PM_2.5 concentrations. In addition, when population-weighted concentration averages were calculated and the exposure level of the population group was compared before and after the COVID-19 outbreak, the proportion of people exceeding the air quality standard decreased by approximately 15.5%. Conclusions: Human activities can impact ambient concentrations of PM_2.5, potentially altering the levels of PM_2.5 exposure in the population.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.919-932
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• 2019

Fine particulate matter (PM2.5) is not only affected by anthropogenic emissions, but also intensifies, migrates, decreases by hydrometeorological factors. Therefore, it is essential to understand relationships between the hydrometeorological factors and PM2.5 concentration. In Korea, PM2.5 concentration is measured at the ground observatories and estimated data are given to locations where observatories are not present. In this way, the data is not suitable to represent an area, hence it is impossible to know accurate concentration at such locations. In addition, it is hard to trace migration, intensification, reduction of PM2.5. In this study, we analyzed the relationships between hydrometeorological factors, acquired from Global Land Data Assimilation System (GLDAS), and PM2.5 by means of Bayesian Model Averaging (BMA). By BMA, we also selected factors that have meaningful relationship with the variation of PM2.5 concentration. 4 PM2.5 concentration models for different seasons were developed using those selected factors, with Aerosol Optical Depth (AOD) from MODerate resolution Imaging Spectroradiometer (MODIS). Finally, we mapped the result of the model, to show spatial distribution of PM2.5. The model correlated well with the observed PM2.5 concentration (R ~0.7; IOA ~0.78; RMSE ~7.66 ㎍/㎥). When the models were compared with the observed PM2.5 concentrations at different locations, the correlation coefficients differed (R: 0.32-0.82), although there were similarities in data distribution. The developed concentration map using the models showed its capability in representing temporal, spatial variation of PM2.5 concentration. The result of this study is expected to be able to facilitate researches that aim to analyze sources and movements of PM2.5, if the study area is extended to East Asia.

• Journal of Environmental Science International
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• v.31 no.12
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• pp.1069-1078
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• 2022

This study investigated the weather conditions, fine particle concentration, and ion components in PM_2.5 when two cold fronts passed through Busan in succession on February 1 and 2, 2021. A analysis of the surface weather chart, AWS, and backward trajectory revealed that the first cold front passed through the Busan at 0900 LST on February 1, 2021, with the second cold front arriving at 0100 LST on February 2, 2021. According to the PM₁₀ concentration of the KMA, the timing of the cold front passage had a close relationship with the occurrence of the highest concentration of fine particles. The transport time of the cold front from Baengnyeongdo to Mt. Gudeok was approximately 11 hours . The PM₁₀ and PM_2.5 concentrations in Busan started to increase after the first cold front had passed, and the maximum concentration occurred two hours after the second cold front passed. The SO₄^2-, NO₃^-, and NH₄⁺ concentration in PM_2.5 started to increase from 1100 to 1200 LST on February 1, after the first cold front passed, and peaked at 0100 LST to 0300 LST on February 2. However, the highest Ca²⁺ concentration was recorded 2-3 hours after the second cold front had passed.