• Journal of Environmental Health Sciences
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• v.45 no.2
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• pp.126-133
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• 2019

Objectives: In this study, a nine-stage cascade impactor was used to collect dust, and the concentration of $PM_{2.1}$ & $PM_{10}$ and alveolar deposition ratio were investigated. Methods: This study was conducted at Kunsan National University from May to June 2016. A nine-stage Cascade Impactor was used to analyze the concentrations of fine and ultrafine dust and to estimate the alveolar deposition rate by particle size of atmospheric dust particles. The pore size of each stage of the collector used in this study gradually increased from F to 0, with the F-stage as the last stage. Results: The mass fraction of PM showed a bimodal distribution divided into $PM_{2.1}$ & $PM_{10}$ based on $2.1-3.1{\mu}m$. The average mass fraction of particulate matter in the range of $2.1-3.1{\mu}m$ was 44%, and the area occupied by $PM_{2.1}$ was similar. Therefore, the Gunsan area is considered to be a region where there are similar effects from anthropogenic and natural sources. Conclusion: Dust collecting efficiency increased with the stage of collecting fine dust, and the efficiency of collection was very low at the stage of collecting ultra-fine dust. The seasonal overall efficiency of the Cascade Impactor was 44% in spring and 37.4% in summer, and the average overall efficiency was 40.7%. The alveolar deposition rate of $PM_{2.1}/PM_{10}$ during the sampling period was estimated to be about 75% deposited in the alveoli.

• Particle and aerosol research
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• v.15 no.2
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• pp.45-56
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• 2019

The public's concern on ambient $PM_{2.5}$ has been increasing in Korea. We have estimated (1) the annual and monthly mean $PM_{2.5}$ concentrations, (2) the frequency by the $PM_{2.5}$ concentration interval, and (3) the high concentration occurrence duration time between 2015 and 2018 at 16 administration regions. We found that there have been differences in all three above parameters' trends among the studied 16 regions in Korea. Still, Jeonbuk showed the highest rank in all three parameters' trends. In Jeonbuk, the average $PM_{2.5}$ concentration and the sum of the frequency fraction when the $PM_{2.5}$ concentration being over $75{\mu}g/m^3$ between 2016 and 2018 was $28.4{\mu}g/m^3$ and 9.0%, respectively. And the days when the $PM_{2.5}$ concentration is over $50{\mu}g/m^3$ between 2015 and 2018 were 149. Chungbuk was the only region with the increasing trend of $PM_{2.5}$ concentration between 2016 and 2018. And in Seoul and Gyeonggi, the average $PM_{2.5}$ concentrations decreased whereas the high concentration frequency fraction increased between 2016 and 2018. Also, it is found that there have been differences in the trends of the frequency by the $PM_{2.5}$ concentration interval and the high concentration occurrence duration time between $PM_{10}$ and $PM_{2.5}$.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1267-1276
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• 2020

To quantitatively predict the impacts of large-scale volcanic eruptions of Mt. Baekdu on air quality and damage around the Korean Peninsula, a three-dimensional chemistry-transport modeling system (Weather Research & Forecasting - Sparse Matrix Operation Kernel Emission - Comunity Multi-scale Air Quality) was adopted. A worst-case meteorology scenario was selected to estimate the direct impact on Korea. This study applied the typical worst-case scenarios that are likely to cause significant damage to Korea among worst-case volcanic eruptions of Mt. Baekdu in the past decade (2005~2014) and assumed a massive VEI 4 volcanic eruption on May 16, 2012, to analyze the concentration of PM_2.5 caused by the volcanic eruption. The effects of air quality in each region-cities, counties, boroughs-were estimated, and vulnerable areas were derived by conducting an exposure assessment reflecting vulnerable groups. Moreover, the effects of cities, counties, and boroughs were analyzed with a high-resolution scale (9 km × 9 km) to derive vulnerable areas within the regions. As a result of analyzing the typical worst-case volcanic eruptions of Mt. Baekdu, a discrepancy was shown in areas between high PM_2.5 concentration, high population density, and where vulnerable groups are concentrated. From the result, PM_2.5 peak concentration was about 24,547 ㎍/㎥, which is estimated to be a more serious situation than the eruption of Mt. St. Helensin 1980, which is known for 540 million tons of volcanic ash. Paju, Gimpo, Goyang, Ganghwa, Sancheong, Hadong showed to have a high PM_2.5 concentration. Paju appeared to be the most vulnerable area from the exposure assessment. While areas estimated with a high concentration of air pollutants are important, it is also necessary to develop plans and measures considering densely populated areas or areas with high concentrations of susceptible population or vulnerable groups. Also, establishing measures for each vulnerable area by selecting high concentration areas within cities, counties, and boroughs rather than establishing uniform measures for all regions is needed. This study will provide the foundation for developing the standards for disaster declaration and preemptive response systems for volcanic eruptions.

• Journal of Environmental Health Sciences
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• v.50 no.4
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• pp.257-266
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• 2024

Background: Exposure to fine particulate matter (PM_2.5) and ozone (O₃) poses potential health risks. The Indoor-to-Outdoor ratio (I/O ratio) is a valuable tool for understanding indoor air quality and identifying potential indoor sources. Objectives: The objective of this study was to determine I/O ratios of PM_2.5 and O₃ by different microenvironments and seasons in Korea. Methods: From December 2021 to November 2023, indoor concentrations of PM_2.5 and O₃ were monitored every hour in 13 microenvironments (residential indoor, office, school, restaurant, pub, café, study café, private educational institute, PC room, billiard room, screen golf center, supermarket, and shopping mall) in Korea. Hourly outdoor concentrations of PM_2.5 and O₃ were obtained from local air quality monitoring stations, provided by airkorea.or.kr. The hourly I/O ratio was calculated by the indoor and outdoor concentrations. Results: At the pub, billiard room, and PC room, the median PM_2.5 I/O ratio exceeded 1 in all seasons, except in spring at the PC room (0.9), suggesting indoor smoking as a potential cause. The median PM_2.5 I/O ratio at the restaurant exceeded 1 in winter, autumn, and summer, except for spring (0.9), indicating significant PM_2.5 emission sources in the restaurant. The median O₃ I/O ratio was below 0.5 in all seasons and microenvironments. Conclusions: This study provided useful data on relationships between indoor and outdoor pollution in various microenvironments by seasons. These I/O ratios could be applied for more accurate exposure assessment to protect health of human.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.851-861
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• 2022

In this study, we analyzed the effects of trees planted in urban areas on PM_2.5 reduction using a computational fluid dynamics (CFD) model. For realistic numerical simulations, the meteorological components(e.g., wind velocity components and air temperatures) predicted by the local data assimilation and prediction system (LDAPS), an operational model of the Korea Meteorological Administration, were used as the initial and boundary conditions of the CFD model. The CFD model was validated against, the PM_2.5 concentrations measured by the sensor networks. To investigate the effects of trees on the PM_2.5 reduction, we conducted the numerical simulations for three configurations of the buildings and trees: i) no tree (NT), ii) trees with only drag effect (TD), and iii) trees with the drag and dry-deposition effects (DD). The results showed that the trees in the target area significantly reduced the PM_2.5 concentrations via the dry-deposition process. The PM_2.5 concentration averaged over the domain in DD was reduced by 5.7 ㎍ m^-3 compared to that in TD.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.

• Journal of Environmental Health Sciences
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• v.46 no.5
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• pp.504-512
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• 2020

Objective: The purpose of this study was to evaluate the indoor to outdoor ratio (I/O ratio) of time activity patterns affecting PM_2.5 concentrations in homes in Korea through a simulation. Methods: The time activity patterns of homemakers were analyzed based on the 'Time-Use Survey' data of the National Statistical Office in 2014. From September 30 to October 2, 2019, the experimenter lived in multifamily housing located in Guro-gu, Seoul. The I/O ratio of PM_2.5 concentration was measured by installing sensor-based instruments. Results: The average indoor and outdoor PM_2.5 concentrations during the three days were 33.1±48.9 and 45.9±25.3 ㎍/㎥, respectively. The average I/O ratio was 0.75±0.60. The indoor concentration tended to increase when PM_2.5 source activity such cooking and cleaning was present and outdoor PM_2.5 was supplied through ventilation. Conclusions: This study could be used as basic data for estimating indoor PM_2.5 concentrations with personal activity pattern and weather conditions using outdoor concentrations.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.2
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• pp.294-305
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• 2018

Quantitative assessment on the impact from North Korean emissions to surface particulate matter(PM) concentration in the Seoul Metropolitan Area (SMA), South Korea is conducted using a 3-dimensional chemistry transport model. Transboundary transport of air pollutants and their precursors are important to understand regional air quality in East Asian countries. As North Korea locates in the middle of main transport pathways of Chinese pollutants, quantifiable estimation of its impact is essential for policy making in South Korean air quality management. In this study, the Community Multiscale Air Quality Modeling System is utilized to simulate regional air quality and its sensitivity, using the Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment 2015 and the Clean Air Policy Support System 2013 emissions inventories for North and South Korea, respectively. Contributions were estimated by a brute force method, perturbing 50% of North and South Korean emissions. Simulations demonstrate that North Korean emissions contribute $3.89{\mu}g/m^3$ of annual surface PM concentrations in the SMA, which accounts 14.7% of the region's average. Impacts are dominant in nitrate and organic carbon (OC) concentrations, attributing almost 40% of SMA OC concentration during January and February. Clear seasonal variations are also found in North Korean emissions contribution to South Korea (and vice versa) due to seasonal characteristics of synoptic weather, especially by the change of seasonal flow patterns.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.2
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• pp.139-158
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• 2017

CMAQ (Community Multiscale Air Quality Model) simulations were carried out to estimate the potential range of contributions on surface $PM_{2.5}$ concentrations over the Seoul Metropolitan Area (SMA) with the gaseous precursors and Primary Particulate Matters(PPM) available from a recent national emissions inventory. In detail, on top of a base simulation utilizing the 2013 Clean Air Policy Supporting System (CAPSS) emission inventory, a set of Brute Force Method (BFM) simulations after reducing anthropogenic $NO_x$, $SO_2$, $NH_3$, VOCs, and PPM emissions released from area, mobile, and point sources in SMA by 50% were performed in turn. Modeling results show that zero-out contributions(ZOC) of $NH_3$ and PPM emissions from SMA are as high as $4{\sim}5{\mu}g/m^3$ over the region during the modeling period. On the contrary, ZOC of local $NO_x$ and $SO_2$ emissions to SMA $PM_{2.5}$ are less than $1{\mu}g/m^3$. Moreover, model analyses indicate that a wintertime $NO_x$ reduction at least up to 50% increases SMA $PM_{2.5}$ concentrations, probably due to increased HNO3 formation and conversion to aerosols under more abundant ozone and radical conditions after the $NO_x$ reduction. However, a nation-wide $NO_x$ reduction decreased SMA $PM_{2.5}$ concentrations even during winter, which implies that nation-wide reductions would be more effective to curtail SMA $PM_{2.5}$ concentrations than localized efforts.

• Journal of the Korean Applied Science and Technology
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• v.36 no.3
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• pp.905-914
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• 2019

In this study, The PMF model was used to identify pollutant sources and their contribution to pollution sources of $PM_{2.5}$. The contribution of A city to each source was 19.8% for Secondary Sulfate, followed by Mobile 19.5%, Industry 16.0%, Biomass Buring 14.1%, Secondary Nitrate 14.1%, Oil Combustion 11.6%, Aged Sea Salt 2.6%, Soil 2.5% and so on. Sulfate and Ammonium concentrations were the highest contributing sources in the source profile, which was analyzed to be Secondary Aerosols produced by Photochemical Reactions of gaseous precursors (SOx and ammonia gas) in the atmosphere.