• Land and Housing Review
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• v.12 no.3
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• pp.39-50
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• 2021

This research aims to provide guidelines with the appropriate type of smart bus stop to reduce the concentration of fine dust. To this end, we divided smart bus stops into two types: closed and open bus stops. The estimated reduction effect was compared and analysed by measuring the estimated PM₁₀ and the estimated PM_2.5 at five locations inside and outside a smart bus stop located in Gangnam gu, Seoul. The effect of reducing the amount of the fine dust concentration in external space was insignificant for both types of bus stops. The different effect of reducing the concentration of the amount between in internal space was relatively significant: the fine dust concentration was 26.0 ㎍/m³ for PM₁₀ and 20.2 ㎍/m³ for PM_2.5 at open-type bus stops; whilst was 2.4 ㎍/m³ for PM₁₀ and 1.8 ㎍/m³ for PM_2.5 at closed type bus stops. Based on the findings, a closed type bus stop is recommended when considering the cost of reducing fine dust. In addition, due to the ineffectiveness of reducing the amount of fine dust from the outside of the bus stop, additional provision of smart bus stops is required particularly in locations where demand exceeds the capacity of the inside. A clear definition of smart bus stop and it's minimum standard should also be considered.

• 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.

• Particle and aerosol research
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• v.19 no.4
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• pp.129-144
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• 2023

The PM_1.0 and PM_2.5 samples were collected synchronously using a single channel particulate sampler equipped with PM_1.0 and PM_2.5 cyclones, respectively, and seasonal mass concentration and chemical composition of PM_1.0 and PM_2.5 were quantified in Seoul and Gwangju in 2021-2022. The mass concentrations of PM_1.0 and PM_2.5 were 17±11 and 22±14 ㎍/m³ in Seoul, and 16±9 and 19±12 ㎍/m³ in Gwangju, respectively. The average ratios of PM_1.0/PM_2.5 were 83±16% in Seoul and 83±7% in Gwangju. The chemical compositions of PM_1.0 and PM_2.5 were similar at both sites with OC component being the most dominant, and NO₃^- increasing from summer to winter, while, the difference of chemical distribution at the two sites was most distinct in the autumn. Gwangju showed a higher proportion of OC and a lower proportion of NO₃^- compared to Seoul during the autumn. Both sites appear to reflect their urban characteristics, with Gwangju also reflecting the impact of biomass combustion as a part of rural activities.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.814-824
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• 2020

Recently, public interest in air pollutants has increased, and the Korean government and local governments have attempted to improve air quality. This study examined the secondary air pollutant contribution in Ulleung Island, Jeju Island, and Baengnyeong Island and compared the differences between them by analyzing the air pollution level and weather conditions in these regions. The weather conditions of the island regions, such as wind speed, precipitation, and sunshine duration, and the average concentration of air pollutants, such as SO₂, NO₂, CO, O₃, PM₁₀, PM_2.5, were examined. The correlation coefficient between air quality factors of each island region and weather conditions was calculated. Regression analysis was conducted by setting primary air pollutants, SO₂, NO₂, and CO as independent variables, and secondary air pollutants, O₃, PM₁₀, and PM_2.5 as dependent variables to identify the regional contribution and impact. Therefore, the government and local governments should establish air quality management for each island region.

• Journal of Soil and Groundwater Environment
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• v.25 no.3
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• pp.74-83
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• 2020

Since fine dust (PM₁₀) has a significant influence on soil and groundwater composition during dry and wet deposition processes, it is of a vital importance to understand the fate and transport of aerosol in geological environments. Fine dust is formed after the chemical reaction of several precursors, typically observed in short intervals within a few hours. In this study, deep learning approach was applied to predict the fate of fine dust in an urban area. Deep learning training was performed by combining convolutional neural network (CNN) and recurrent neural network (RNN) techniques. The PM₁₀ concentration after 1 hour was predicted based on three-hour data by setting SO₂, CO, O₃, NO₂, and PM₁₀ as training data. The obtained coefficient of determination value, R², was 0.8973 between predicted and measured values for the entire concentration range of PM₁₀, suggesting deep learning method can be developed into a reliable and viable tool for prediction of fine dust concentration.

• Journal of Environmental Health Sciences
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• v.47 no.3
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• pp.259-266
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• 2021

Objectives: This study was conducted to identify the emissions characteristics of total particulate matter (TPM), fine dust (PM₁₀, PM_2.5), and gaseous pollutants (SOx, NOx) in iron and steel manufacturing facilities in order to investigate emissions factors suitable for domestic conditions. Methods: Total particulate matter (TPM), fine dust (PM₁₀, PM_2.5), and gas phase materials were investigated at the outlet of electric arc furnace facilities using a cyclone sampling machine and a gas analyzer. Results: The concentrations of TPM ranged from 1.64 to 3.14 mg/Sm³ and the average was 2.47 mg/Sm³. Particulate matter 10 (PM₁₀) averaged 1.49 mg/Sm³ with a range of 0.92 to 1.99 mg/Sm³, and the resulting ratio of PM₁₀ to TPM was around 60 percent. PM_2.5/PM₁₀ ranged from 33.7 to 47.9% and averaged 41.6%. Sulfur oxides (SOx) were not detected, and nitrogen oxides (NOx) averaged 6.8 ppm in the range of 5.50 to 8.67 ppm. TPM emission coefficients per product output were in the range of 0.60 to 1.26 g/kg, 0.13 to 0.79 g/kg for PM₁₀ and 0.12 to 0.36 g/kg for PM_2.5, and showed many differences from the emissions coefficients previously announced. An emissions coefficient for NOx is not currently included in the domestic notices, but the results were calculated to be 0.42 g/kg per product output. Conclusions: Investigation and research on emissions coefficients that can reflect the characteristics of various facilities in Korea should be conducted continuously, and the determination and application of unique emissions coefficients that are more suitable for domestic conditions are needed.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.151-151
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• 2023

본 연구는 호우(heavy rain) 발생 시 대기 중 미세먼지(particulate matter, PM) 저감효과를 규명하고 강우 지속에 따른 빗물 수질(pH, 전기전도도(electrical conductivity, EC), 수용성 이온) 분석을 통해 대기 중 PM이 빗물 수질에 미치는 영향을 평가하였다. 2020년 3월부터 2021년 2월까지강우 강도(7.5 mm/h)를 기준으로 총 6회의 강우를 대상으로 하였으며 빗물 샘플은 집수장치를 통해 50 mL를 연속적으로 수집하여 수질을 분석하였다. 대기 중 PM_2.5 (≤ 2.5 ㎛ in diameter) 및 PM₁₀ (≤ 10 ㎛ in diameter) 농도는 기상청 내 부산 남구 대연동 관측소의 automatic weather system (AWS)에서 측정된 일평균 자료를 이용하였다. 강우에 따른 대기 중 PM의 저감효율은 상대적으로 PM₁₀에서 뚜렷하게 나타났으며, 특히 강우 강도 7.5 mm/h 이상(유형 1)의 호우 발생 시60% 이상의 저감효율을 보였다. 반면, 강우 강도 7.5 mm/h 이하(유형 2)일 때는 10% 이하의 저감효율을 보였으며, 강우 지속에 따라 대기 중 PM₁₀ 농도가 증가하는 경향을 보이기도 하였다. 총108개의 빗물 샘플 수질을 분석한 결과, 유형 1의 경우 초기 빗물의 평균 EC는 58.5 µS/cm으로 상대적으로 높았으며 대기 중 PM₁₀과 양의 상관관계(r = 0.99)를 보였고 평균 pH는 4.3으로 산성도가 높게 나타났으며 대기 중 PM₁₀과 음의 상관관계(r = -0.99)를 보였다. 반면, 유형 2의 경우 대기 중 PM₁₀과 EC (r = -0.56) 및 pH (r = -0.41) 간 뚜렷한 상관관계가 나타나지 않았다. 또한 강우가 지속됨에 따라 EC와 수용성 양이온(Na⁺, Mg²⁺, K⁺, Ca²⁺, NH₄⁺) 및 음이온(Cl^-, NO₃^-, SO₄^2-)의 농도는 지속적으로 감소하는 경향을 보였으나 pH의 경우 강우 강도에 따라 증감의 경향이 다르게 나타났다. 유형 1의 경우 강우 지속에 따라 pH가 증가하여 산성도가 낮아졌으나 유형 2는 pH의 증감 형태를 뚜렷하게 확인하기 어려웠다. 연구 결과를 통해 강우 초기 높은 강도로 강우가 지속될 경우 대기 중 PM₁₀이 빗물 수질에 영향을 미칠 수 있는 것으로 판단되며, 이에 따라 호우 발생 시 강우가 대기 중 오염물질을 지표면으로 유입시킬 수 있는 매개체로 작용할 수 있음을 지시한다.

• Korean Journal of Environmental Agriculture
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• v.40 no.3
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• pp.211-218
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• 2021

BACKGROUND: Fine particulate matter (PM_2.5) is produced by chemical reactions between various precursors. PM_2.5 has been found to create greater human risk than particulate matter (PM₁₀), with diameters that are generally 10 micrometers and smaller. Ammonia (NH₃) and nitrogen oxides (NOx) are the sources of secondary generation of PM_2.5. These substances generate PM_2.5 through some chemical reactions in the atmosphere. Through chemical reactions in the atmosphere, NH₃ generates PM_2.5. It is the causative agent of PM_2.5. In 2017 the annual ammonia emission recorded from the agricultural sector was 244,335 tons, which accounted for about 79.3% of the total ammonia emission in Korea in that year. To address this issue, the agricultural sector announced the inclusion of reducing fine particulate matter and ammonia emissions by 30% in its targets for the year 2022. This may be achieved through analyses of its emission characteristics by monitoring the PM_2.5 and NH₃. METHODS AND RESULTS: In this study, the PM_2.5 concentration was measured real-time (every 1 hour) by using beta radiation from the particle dust measuring device (Spirant BAM). NH₃ concentration was analyzed real-time by Cavity Ring-Down Spectroscopy (CRDS). The concentrations of ozone (O₃) and nitrogen dioxide (NO₂) were continuously measured and analyzed for the masses collected on filter papers by ultraviolet photometry and chemiluminescence. CONCLUSION: This study established air pollutant monitoring system in agricultural areas to analyze the NH₃ emission characteristics. The amount of PM_2.5 and NH₃ emission in agriculture was measured. Scientific evidence in agricultural areas was obtained by identifying the emission concentration and characteristics per season (monthly) and per hour.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1881-1890
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• 2021

Particulate matter (PM) affects the human, ecosystems, and weather. Motorized vehicles and combustion generate fine particulate matter (PM_2.5), which can contain toxic substances and, therefore, requires systematic management. Consequently, it is important to monitor and predict PM_2.5 concentrations, especially in large cities with dense populations and infrastructures. This study aimed to predict PM_2.5 concentrations in large cities using meteorological and chemical variables as well as satellite-based aerosol optical depth. For PM_2.5 concentrations prediction, a random forest (RF) model showing excellent performance in PM concentrations prediction among machine learning models was selected. Based on the performance indicators R², RMSE, MAE, and MAPE with training accuracies of 0.97, 3.09, 2.18, and 13.31 and testing accuracies of 0.82, 6.03, 4.36, and 25.79 for R², RMSE, MAE, and MAPE, respectively. The variables used in this study showed high correlation to PM_2.5 concentrations. Therefore, we conclude that these variables can be used in a random forest model to generate reliable PM_2.5 concentrations predictions, which can then be used to assess the vulnerability of schools to PM_2.5.

• Journal of the Korean earth science society
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• v.43 no.2
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• pp.283-302
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• 2022

Despite the nationwide COVID-19 lockdown in China since January 23, 2020, haze days with high PM₁₀ levels of 88-98 ㎍ m^-3 occurred on February 1 and 2, 2020. During these haze days, the East Asian region was affected by a warm and stagnant air mass with positive air temperature anomalies and negative zonal wind anomalies at 850 hPa. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was used to analyze the variation of regional PM₁₀ aerosol transport in Korea due to decreased anthropogenic emissions in East Asia. The base experiment (BASE), which applies the basic anthropogenic emissions in the WRF-Chem model, and the control experiment (CTL) applied by reducing the anthropogenic emission to 50%, were used to assess uncertainty with ground-based PM₁₀ measurements in Korea. The index of agreement (IOA) for the CTL simulation was 0.71, which was higher than that of BASE (0.67). A statistical analysis of the results suggests that anthropogenic emissions were reduced during the COVID-19 lockdown period in China. Furthermore, BASE and CTL applied to zero-out anthropogenic emissions outside Korea (BASE_ZEOK and CTL_ZEOK) were used to analyze the variations of regional PM₁₀ aerosol transport in Korea. Regional PM₁₀ transport in CTL was reduced by only 10-20% compared to BASE. Synthetic weather variables may be another reason for the non-linear response to changes in the contribution of regional transport to PM₁₀ in Korea with the reduction of anthropogenic emissions in East Asia. Although the regional transport contribution of other inorganic aerosols was high in CTL (80-90%), sulfate-nitrate-ammonium (SNA) aerosols showed lower contributions of 0-20%, 30-60%, and 30-60%, respectively. The SNA secondary aerosols, particularly nitrates, presumably declined as the Chinese lockdown induced traffic.