• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.1
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• pp.89-96
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• 2020

Adverse impact of Particulate Matters(PM10, PM2.5; PMs) significantly affects daily lives. Major countermeasures for reducing concentration of PMs were focused on emission source without considering spatial difference of PMs concentration. Thus, this study analyzed spatial·temporal distribution of PMs with observation data as well as potential contributing factors on PMs concentration. The annual average concentration of PMs have been decreased while the particulate matter warnings and alerts were significantly increased in 2018. The average concentration of PMs in spring and winter was higher than the other seasons. Also, the spatial distribution of PMs were also showed seasonality while concentration of PMs were higher in Seoul-metropolitan areas in all seasons. Climate variables, emission source, spatial structure and potential PM sinks were selected major factors which could affects on ambient concentrations of PMs. This paper suggest that countermeasures for mitigating PM concentration should consider characteristics of area. Climatic variables(temperature, pressure, wind speed etc.) affects concentrations of PMs. The effects of spatial structure of cities(terrain, ventilation corridor) and biological sinks(green infrastructure, urban forests) on concentration of PMs should be analyzed in further studies. Also, seasonality of PMs concentration should be considered for establishing effective countermeasures to reduce ambient PMs concentration.

• Asian Journal of Atmospheric Environment
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• v.12 no.3
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• pp.244-254
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• 2018

Ulaanbaatar, the capital of Mongolia, is subject to high levels of atmospheric pollution during winter, which severely threatens the health of the population. By analyzing surface meteorological data, ground-based LIDAR data, and radiosonde data collected from 2008 to 2016, we studied seasonal variations in particulate matter (PM) concentration, visibility, relative humidity, temperature inversion layer thickness, and temperature inversion intensity. PM concentrations started to exceed the 24-h average standard ($50{\mu}g/m^3$) in mid-October and peaked from December to January. Visibility showed a significant negative correlation with PM concentration. Relative humidity was within the range of 60-80% when there were high PM concentrations. Both temperature inversion layer thickness and intensity reached maxima in January and showed similar seasonal variations with respect to PM concentration. The monthly average temperature inversion intensity showed a strong positive correlation with the monthly average $PM_{2.5}$ concentration. Furthermore, the temperature inversion layer thickness exceeded 500 m in midwinter and overlaid the weak mixed layer during daytime. Radiative cooling enhanced by the basin-like terrain led to a stable urban atmosphere, which strengthened particulate air pollution.

• Journal of People, Plants, and Environment
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• v.23 no.6
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• pp.637-646
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• 2020

Background and objective: The main objective of this study is to measure the amount of particulate matter (PM) reduction under different characteristics of leaves in 18 different species of indoor plants. Methods: First, a particular amount of PM was added to the glass chambers (0.9×0.86×1.3 m) containing the indoor plant (height = 40 ± 20 cm), and the PM concentration were measured at 2-hour intervals. The experiment with the same conditions was conducted in the empty chamber as the control plot. Results: The range of PM reduction per unit leaf area of 18 species of experimental plants was 3.3-286.2 ㎍·m^-2 leaf, total leaf area was 1,123-4,270 cm², and leaf thickness was 0.14-0.80 mm and leaf size 2.27-234.47 cm². As time passed, the concentration of PM decreased more in the chamber with plants than in the empty chamber. Among the 18 indoor plants, the ones with the greatest reduction in PM2.5 in 2 hours and 4 hours of exposure to PM2.5 were Pachira aquatica and Dieffenbachia amoena. As the exposure time of PM increased, the efficiency of reducing PM2.5 was higher in plants with medium-sized leaves than plants with large or small leaves. The effect of reducing PM2.5 was higher in linear leaves than round or lobed leaves. Plants with high total leaf area did not have advantage in reducing PM because the leaves were relatively small and there were many overlapping parts between leaves. In the correlation between leaf characteristics and PM 2.5 reductions, all leaf area and leaf thickness showed a negative and leaf size showed a positive correlation with PM reduction. Conclusion: The PM reduction effect of plants with medium-sized leaves and long linear leaves was relatively high. Moreover, plants with a large total leaf area without overlapping leaves will have advantaged in reducing PM. Plants are effective in reducing PM, and leaf characteristics are an important factor that affects PM reduction.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.423-433
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• 2018

Particulate matter air pollution is a serious problem affecting human health and visibility. The variations in $PM_{10}$ concentrations are influenced by not only local emission sources, but also atmospheric circulation conditions. In this study, we investigate the temporal features of $PM_{10}$ concentrations in South Korea and the atmospheric circulation patterns associated with high concentration episodes of $PM_{10}$ during winter (December-January-February) 2001-2016. Based on those analyses, a Korea Particulate matter Index (KPI) is developed to represent the large-scale atmospheric pattern associated with high concentration episodes of $PM_{10}$. The atmospheric patterns are characterized by persistent high-pressure anomalies, weakened lower-level north-westerly anomalies, and northward shift of the upper-level meridional wind anomalies near the Korean Peninsula. To evaluate the change in occurrence of high concentration episodes of $PM_{10}$ under a possible future warmer climate, we apply KPI analysis to CMIP5 climate simulations. Here, historical and two representative concentration pathway (RCP) scenarios (RCP 4.5 and RCP 8.5) are used. It is found that the occurrence of atmospheric conditions favorable for high $PM_{10}$ concentration episodes tends to increase over South Korea in response to climate change. This suggests that large-scale atmospheric circulation changes under future warmer climate can contribute to increasing high $PM_{10}$ concentration episodes in South Korea.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.04a
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• pp.188-190
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• 2008

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.10a
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• pp.521-523
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• 2008

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2010.04a
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• pp.383-385
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• 2010

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2010.04a
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• pp.657-658
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• 2010

• Environmental and Resource Economics Review
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• v.26 no.3
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• pp.417-449
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• 2017

The purpose of this study is to analyze the effect of local area concentration of particulate matter on life satisfaction, by matching subjective satisfaction in the Korea Labor & Income Panel Study data with daily data of $PM_{10}$ and $PM_{2.5}$. We find that the concentration of particulate matter has a significant negative effect on satisfaction. A $1{\mu}g/m^3$ increase in $PM_{10}$($PM_{2.5}$) leads to lower the probability of choosing 'satisfaction' by 0.042%p~0.091%p(0.034%p~0.153%p) and a 1% increase in annual income per household raises the probability of choosing 'satisfaction' by 0.16%p~0.18%p respectively. To estimate the monetary value of reducing $PM_{10}$ and $PM_{2.5}$, we calculate willingness-to-pay for mitigating air pollution, which represents the tradeoff between the reduction in particulate matter and income. We find that people on average are willing to pay \108,787($96)~209,519($186) for a $1{\mu}g/m^3$ reduction in $PM_{10}$ and to pay 89,345($79)~362,930($322) in $PM_{2.5}$. This amount corresponds to 0.26%~0.50%(0.22%~0.88%) of the average annual household income in South Korea.

• 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.