• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.103-110
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• 2022

Ultrafine Particle number and size distributions were simultaneously measured at rural area around the power plant in Dangjin, South Korea. New Particle formation and growth events were frequently observed during January, 2021 and classified based on their strength and persistence as well as the variation in geometric mean diameter(GMD) on January 12, 21 and 17. In this study, we investigated mechanisms of new particle growth based on measurements using a high resolution time of flight aerosol mass spectrometer(HR-ToF-AMS) and a scanning mobility particle sizer(SMPS). On Event days(Jan 12 and 21), the total average growth rate was found to be 8.46 nm/h~24.76 nm/hr. These growth rate are comparable to those reported for other urban and rural sites in South Korea using different method. Comparing to the Non-Event day(Jan 17), New Particle Growth mostly occurred when solar radiation is peaked and relative humidity is low in daytime, moreover enhanced under the condition of higher precusors, NO₂ (39.9 vs 6.2ppb), VOCs(129.5 vs 84.6ppb), NH₃(11 vs 4.7ppb). The HR-ToF-AMS PM_1.0 composition shows Organic and Ammoniated nitrate were dominant species effected by emission source in domestic. On the other hand, The Fraction of Ammoniated sulfate was calculated to be approximately 16% and 31% when air quality is inflow from China. Longer term studies are needed to help resolve the relative contributions of each precusor species on new particle growth characteristics.

• Journal of Environmental Science International
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• v.31 no.6
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• pp.503-513
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• 2022

This research investigated the effect of the eruption of Japan Sakurajima volcano on the concentration of ultrafine particle when the north Pacific high pressure exists in the Busan in summer. As a result of analyzing the forward trajectory using the HYSPLIT model, the air parcel from Sakurajima volcano passed through the sea in front of Busan at 1500 LST on July 17, 24 hours after the volcanic eruption. As a result of analyzing the PM₁₀ and PM_2.5 concentrations in the Busan for two days from July 16 to 17, 2018, the Sakurajima eruption in Japan, it can be seen that there was a high increase in PM₁₀ and PM_2.5 concentrations compared to the previous day. As a result of analyzing the backward trajectory, the air mass that reached Busan at 1300 LST on July 17, 2018 has moved near the Sakurajima volcano at 1,500 m, 2,000 m, and 3,000 m. The concentration of SO₄^2- in PM_2.5, the concentration of all three stations in Busan showed a sharp increase from 1000 LST on July 17th. Looking at the NH₄⁺ concentration in PM_2.5, it shows a very similar variation trend to SO₄^2-, and the correlation coefficient between the two components is 0.96 for Jangrimdong and Yeonsandong, and 0.85 for Busan New Port. Looking at the NO₃^- concentration in PM_2.5, the same high concentrations as SO₄² and NH₄⁺ were not observed in the afternoon of July 17th.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1653-1667
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• 2020

In this study, we coupled a computation fluid dynamics (CFD) model to the local data assimilation and prediction system (LDAPS), a current operational numerical weather prediction model of the Korea Meteorological Administration. We investigated the characteristics of fine particulate matter (PM2.5) distributions in a building-congested district. To analyze the effects of road emission on the PM2.5 concentrations, we calculated road emissions based on the monthly, daily, and hourly emission factors and the total amount of PM2.5 emissions established from the Clean Air Policy Support System (CAPSS) of the Ministry of Environment. We validated the simulated PM2.5 concentrations against those measured at the PKNU-AQ Sensor stations. In the cases of no road emission, the LDAPS-CFD model underestimated the PM2.5 concentrations measured at the PKNU-AQ Sensor stations. The LDAPS-CFD model improved the PM2.5 concentration predictions by considering road emission. At 07 and 19 LST on 22 June 2020, the southerly wind was dominant at the target area. The PM2.5 distribution at 07 LST were similar to that at 19 LST. The simulated PM2.5 concentrations were significantly affected by the road emissions at the roadside but not significantly at the building roof. In the road-emission case, the PM2.5 concentration was high at the north (wind speeds were weak) and west roads (a long street canyon). The PM2.5 concentration was low in the east road where the building density was relatively low.

• 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 The Korean Society of Agricultural Engineers
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• v.63 no.5
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• pp.13-25
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• 2021

The particulate matters (PM₁₀ and PM_2.5) and ammonia emitted from livestock farms as dispersed to urban and residential areas can increase the public's concern over the health problem, social conflicts, and air quality. Understanding the atmospheric dispersion of such matters is important to prevent the problems for the regulatory purposes. In this study, AERMOD modeling was performed to predict the dispersion of livestock particulate matters and ammonia in Gwangju metropolitan city and five surrounding cities. The five cities were divided into 40 sub-zones to model the area-based emissions which varied with the number of livestock farms, species and growth stages of the animals. As a result, the concentrations of PM₁₀, PM_2.5 and ammonia resulted from livestock farms located in the surrounding cities were 2.00 ㎍ m^-3, 0.30 ㎍ m^-3 and 0.04 ppm in the southwestern part of Gwangju based on the average concentration of 1 hour. These values accounted for 0.7% of PM₁₀ concentration, 0.5% of PM_2.5 concentration, and 0.4% of the ammonia concentration in Gwangju, contributing to a small amount of air pollution compared to other sources. As preventive measures, the plantation was applied to high emission source areas to reduce particulate matters and ammonia emissions by 35% and 31%, respectively, and resulted in decrease of the area of influence by 57% for particulate matters and 59% for ammonia.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.567-575
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• 2021

Residents in Dangjin, South Chungcheong Province, in which large-scale emissions facilities such as coal-fired power plants and steel mills are concentrated, are very much concerned about their health despite the local government's aggressive efforts to improve air quality and reduce greenhouse gases. To understand the impact of coal-fired power plants and external factors on local air pollution, the origins of local pollutants were investigated using stable carbon isotopes that are generally used as tracers of the provenance of fine or ultrafine dust. The origins of the pollutants were analyzed with the data library, built using the seasonally measured data for the two separate locations selected considering the distance from the coal-fired power plant and the analysis of previous studies, and with the back trajectory analysis. As a result of analyzing stable isotope ratios, the tendency of high concentration was found in the order of winter > spring > fall > summer. According to the data matching with the library, the mobile pollutants and open-air incineration had a relatively higher impact on the local air pollution. It is believed that this study, as a pilot study, should focus on securing the reliability of the study results through continuous monitoring and data accumulation.

• Journal of Environmental Impact Assessment
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• v.27 no.5
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• pp.431-446
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• 2018

In this study, chemical characteristics of $PM_{2.5}$ samples collected in an agricultural area in Nonsan of Korea were investigated focusing on of black carbon, 3 inorganic ions and 22 trace elements. It was found that the relative error and relative standard deviation of many trace elements fell below 10%, which indicates good analytical accuracy and precision. The mean values of $PM_{2.5}$ in an agricultural area were exceeded by new Korean air quality standard of March 2018. The concentration of $PM_{2.5}$ was well correlated with those of black carbon and ions. The concentrations of trace elements were in a wide range of seven orders of a magnitude. Based on these $PM_{2.5}$ data sets, a total of 6 sources were identified using PMF (Positive Matrix Factorization; secondary aerosol (34.4%), soil/road dust (20.1%), biomass burning (16.9%), incineration/fuel combustion (13.2%), vehicle exhaust(12.2%), sea-salt (3.17%). Results of our study indicate that it is very important to control illegal burning activities in agricultural area.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.5
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• pp.445-457
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• 2017

In this study, we developed an approach to better account for uncertainties in estimated contributions from fine particulate matter ($PM_{2.5}$) modeling. Our approach computes a Concentration Correction Factor (CCF) which is a ratio of observed concentrations to baseline model concentrations. We multiply modeled direct contribution estimates with CCF to obtain revised contributions. Overall, the modeling system showed reasonably good performance, correlation coefficient R of 0.82 and normalized mean bias of 2%, although the model underestimated some PM species concentrations. We also noticed that model biases vary seasonally. We compared contribution estimates of major source sectors before and after applying CCFs. We observed that different source sectors showed variable magnitudes of sensitivities to the CCF application. For example, the total primary $PM_{2.5}$ contribution was increased $2.4{\mu}g/m^3$ or 63% after the CCF application. Out of a $2.4{\mu}g/m^3$ increment, line sources and area source made up $1.3{\mu}g/m^3$ and $0.9{\mu}g/m^3$ which is 92% of the total contribution changes. We postulated two major reasons for variations in estimated contributions after the CCF application: (1) monthly variability of unadjusted contributions due to emission source characteristics and (2) physico-chemical differences in environmental conditions that emitted precursors undergo. Since emissions-to-$PM_{2.5}$ concentration conversion rate is an important piece of information to prioritize control strategy, we examined the effects of CCF application on the estimated conversion rates. We found that the application of CCFs can alter the rank of conversion efficiencies of source sectors. Finally, we discussed caveats of our current approach such as no consideration of ion neutralization which warrants further studies.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.3
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• pp.457-468
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• 2018

To improve understanding of the physico-chemical characteristics of aerosols in the national park and comparing the air pollution between national park and the urban area nearby national park, the aerosol characterization study was conducted in Bukhansan National Park, Seoul, from July through September 2017. Semi-continuous measurements of $PM_{2.5}$ using PILS (Particle Into Liquid System) coupled with IC (Ion Chromatography) and TOC (Total Organic Carbon) analyzer allowed quantification of concentrations of major ionic species($Cl^-$, $SO_4{^{2-}}$, $NO_3{^-}$, $Na^+$, $NH_4{^+}$, $K^+$, $Mg{^{2+}}$ and $Ca{^{2+}}$) and water soluble organic carbon (WSOC) with 30-minute time resolution. The total mass concentration of $PM_{2.5}$ was measured by T640 (Teledyne) with 5-minute time resolution. The black carbon (BC) and ozone were measured with a minute time resolution. The timeline of aerosol chemical compositions reveals a strong influence from urban area (Seoul) at the site in Bukhansan National Park. Inorganic aerosol composition was observed to be dominated by ammoniated sulfate at most times with ranging from $0.1{\sim}32.6{\mu}g/m^3$ (6.5~76.1% of total mass of $PM_{2.5}$). The concentration of ammonium nitrate, a potential indicator of the presence of local source, ranged from below detection limits to $20{\mu}g/m^3$ and was observed to be highest during times of maximum local urban (Seoul) impact. The total mass of $PM_{2.5}$ in Bukhansan National Park was observed to be 10~23% lower than the total mass of $PM_{2.5}$ in urban area (Gireum-dong and Bulgwang-dong, Seoul). In general, ozone concentration in Bukhansan National Park was observed to be similar or higher than urban sites in Seoul, suggesting additional biogenic VOCs with $NO_x$ from vehicle emission were to be precursors for ozone formation in Bukhansan National Park.

• Journal of Environmental Health Sciences
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• v.47 no.6
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• pp.521-529
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• 2021

Background: The coronavirus disease (COVID-19) has caused changes in human activity, and these changes may possibly increase or decrease exposure to fine dust (PM_2.5). Therefore, it is necessary to evaluate the exposure to PM_2.5 in relation to the outbreak of COVID-19. Objectives: The purpose of this study was to compare and evaluate the exposure to PM_2.5 concentrations by the variation of dynamic populations before and after the outbreak of COVID-19. Methods: This study evaluated exposure to PM_2.5 concentrations by changes in the dynamic population distribution in Guro-gu, Seoul, before and after the outbreak of COVID-19 between Jan and Feb, 2020. Gurogu was divided into 2,204 scale standard grids of 100 m×100 m. Hourly PM_2.5 concentrations were modeled by the inverse distance weight method using 24 sensor-based air monitoring instruments. Hourly dynamic population distribution was evaluated according to gender and age using mobile phone network data and time-activity patterns. Results: Compared to before, the population exposure to PM_2.5 decreased after the outbreak of COVID-19. The concentration of PM_2.5 after the outbreak of COVID-19 decreased by about 41% on average. The variation of dynamic population before and after the outbreak of COVID-19 decreased by about 18% on average. Conclusions: Comparing before and after the outbreak of COVID-19, the population exposures to PM_2.5 decreased by about 40%. This can be explained to suggest that changes in people's activity patterns due to the outbreak of COVID-19 resulted in a decrease in exposure to PM_2.5.