• Journal of the Korean earth science society
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• v.33 no.6
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• pp.469-480
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• 2012

This study was to analyze the characteristics of diurnal variation of high $PM_{2.5}$ concentration, $PM_{2.5}/PM_{10}$ concentration ratio by spatio-temporal wind system (wind speed and wind direction) for high $PM_{2.5}$ concentration (over the 24 hr environmental standard of $PM_{2.5}$, $50{\mu}g/m^3$) in the air quality observation sites (Jangrimdong: Industrial area, Jwadong: Residential area) that were measured for 3 years (2005. 12. 1-2008. 11. 30) in Busan. The observation days of high $PM_{2.5}$ concentration were 182 at Jangrimdong and 27 at Jwadong. The seasonal diurnal variation of hourly mean of high $PM_{2.5}$ concentration and of $PM_{2.5}/PM_{10}$ concentration ratio showed a similar pattern that had higher variation at dawn, and night and in the morning than in the afternoon. Durning daytime in summer at Jwadong, the $PM_{2.5}/PM_{10}$ concentration ratio increased because a secondary particulate matter, which was created by photochemical reaction, decreased the coarse particles of $PM_{10}$ more than the fine particles of $PM_{2.5}$ concentrations in ocean condition. We did an analysis of spatio-temporal wind system (wind speed range and wind direction) in each time zone. The result showed that high $PM_{2.5}$ concentration at Jangrimdong occurred due to the congestion of pollutants emissions from the industrial complex in Jangrimdong area and the transportation of pollutants from places nearby Jangrimdong. It also showed that high $PM_{2.5}$ concentration occurred at Jwadong because of a number of local residential and commercial activities that caused the congestion of pollutants.

• Journal of environmental and Sanitary engineering
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• v.24 no.3
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• pp.37-54
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• 2009

The study has analyzed impacts and intensity of weather that affect $PM_{10}$ concentration based on PM10 forecast conducted by the city of Seoul in order to identify ways to improve the accuracy of PM10 forecast. Variables that influence $PM_{10}$ concentration include not only velocity and direction of the wind and rainfalls, but also those including secondary particulate matter, which were identified to greatly influence the concentration in complicated manner as well. In addition, same variables were found to have different impacts depending on seasons and conditions of other variables. The study found out that improving accuracy of $PM_{10}$ concentration forecast face some limits as it is greatly influenced by the weather. As an estimation, this study assumed that basic research units and artificially estimated pollutant emissions, study on mechanisms of secondary particulate matter productions, observatory compliment, and enhanced forecaster's expertise are needed for better forecast.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.199-215
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• 2018

Interest in $PM_{10}$ concentrations have increased greatly in Korea due to recent increases in air pollution levels. Therefore, we consider a forecasting model for next day $PM_{10}$ concentration based on the principal elements of air pollution, weather information and Beijing $PM_{2.5}$. If we can forecast the next day $PM_{10}$ concentration level accurately, we believe that this forecasting can be useful for policy makers and public. This paper is intended to help forecast a daily mean $PM_{10}$, a daily max $PM_{10}$ and four stages of $PM_{10}$ provided by the Ministry of Environment using various data mining techniques. We use seven models to forecast the daily $PM_{10}$, which include five regression models (linear regression, Randomforest, gradient boosting, support vector machine, neural network), and two time series models (ARIMA, ARFIMA). As a result, the linear regression model performs the best in the $PM_{10}$ concentration forecast and the linear regression and Randomforest model performs the best in the $PM_{10}$ class forecast. The results also indicate that the $PM_{10}$ in Seoul is influenced by Beijing $PM_{2.5}$ and air pollution from power stations in the west coast.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1069-1079
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• 2019

In this study, we developed a $PM_{10}$ forecasting model using DNN and Membership Function, and improved the forecasting performance. The model predicts the $PM_{10}$ concentrations of the next 3 days in the Seoul area by using the weather and air quality observation data and forecast data. The best model(RM14)'s accuracy (82%, 76%, 69%) and false alarm rate(FAR:14%,33%,44%) are good. Probability of detection (POD: 79%, 50%, 53%), however, are not good performance. These are due to the lack of training data for high concentration $PM_{10}$ compared to low concentration. In addition, the model dose not reflect seasonal factors closely related to the generation of high concentration $PM_{10}$. To improve this, we propose Julian date membership function as inputs of the $PM_{10}$ forecasting model. The function express a given date in 12 factors to reflect seasonal characteristics closely related to high concentration $PM_{10}$. As a result, the accuracy (79%, 70%, 66%) and FAR (24%, 48%, 46%) are slightly reduced in performance, but the POD (79%, 75%, 71%) are up to 25% improved compared with those of the RM14 model. Hence, this shows that the proposed Julian forecast model is effective for high concentration $PM_{10}$ forecasts.

• Journal of the Korean association of regional geographers
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• v.23 no.1
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• pp.151-167
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• 2017

The main objective of this study was to investigate the climatic impact of $PM_{10}$ concentration on the temperature change pattern in Busan Metropolitan City(BMC), Korea during 2001~2015. Mean $PM_{10}$ concentration of BMC has gradually declined over the past 15 years. While the highest $PM_{10}$ concentration was observed in spring followed by winter, summer, and fall on average, the seasonal variations of $PM_{10}$ concentration differed from place to place within the city. Frequency analysis showed that the most frequently observed $PM_{10}$ concentration ranged from $20{\mu}g/m^3$ to $60{\mu}g/m^3$, which accounted for 64.6% of all daily observations. Overall, the west-high and east-low pattern of $PM_{10}$ concentration was relatively strong during the winter when the effect of yellow-dust events on the air quality was weak. Comparative analyses between $PM_{10}$ concentration and monthly temperature slope derived from generalized temperature curves indicated that the decreasing trend of $PM_{10}$ concentration was associated with increases of annual temperature range, and $PM_{10}$ concentration had a negative relationship with the temperature slope of warming months. Overall, $PM_{10}$ concentration had a weak correlation with the annual mean temperature, but it had a significant, positive correlation with the winter season, which had a dominant influence on the annual mean temperature. In terms of energy budget, it has been known that the change in $PM_{10}$ concentration contributes to the warming or cooling effect by affecting the radiative forcing due to the reflection and absorption of radiant energy. The correlation between $PM_{10}$ concentration and temperature changes in the study area was not seasonally and spatially consistent, and its significance was statistically limited partly due to the number of observations and the lack of potential socioeconomic factors relevant to urban air quality.

• Journal of Environmental Science International
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• v.13 no.1
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• pp.27-36
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• 2004

Present study analyzed the temporal and spatial characteristics of PM10 pollution in Metropolitan Daegu area based on air pollution monitoring station data and measurements of PM10 concentrations in background area in order to provide essential data for efficient PM10 pollution management. The significant variation of spatial and temporal PM10 concentrations in Daegu area was observed during the study years. The highest maximum PM10 concentration(332 $\mu\textrm{g}$/㎥), average concentration(88 $\mu\textrm{g}$/㎥) and frequency exceeding PM10 daily standard(150 $\mu\textrm{g}$/㎥) were all observed in Namsandong located near a major roadway. The hourly and weekly variations of PM10 concentrations had different pattern for the measurement sites. The monthly and seasonal concentrations exhibited a notable characteristic： the maximum concentration was obtained in spring season, most likely due to Yellow sand effects. Furthermore, this temporal variation of PM10 pollution varied with study site. Meanwhile, the PM10 values measured at the monitoring site, Manchondong, were comparable with those of a control site. The average PM10 concentration ranged from 23 $\mu\textrm{g}$/㎥ to 115 $\mu\textrm{g}$/㎥ with a mean value of 53 $\mu\textrm{g}$/㎥ in the former site and from 22 $\mu\textrm{g}$/㎥ to 91 $\mu\textrm{g}$/㎥ with a mean value of 45 $\mu\textrm{g}$/㎥ in the latter site.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.128-135
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• 2022

Because construction work is conducted outdoors, construction workers are affected by harmful environmental factor. Especially, Particulate Matter (PM₁₀) is one of the harmful environmental factors with a diameter of 10㎍/m³ or less. When PM₁₀ is inhaled by human, it can cause fatal impact on the human. Contrary to the various analyses of health impact on PM₁₀, the research on the relationship between construction accidents and PM₁₀ are few. Therefore, this study aims to conduct the relative frequency analysis which find out the correlation between construction accidents and PM₁₀, and the modified PM₁₀ grade is suggested to expect accidents probability caused by PM₁₀ in the construction industry. This study is conducted by four steps. i) Establishment of the database; ii) Classification of data; iii) Analysis of the Relative Frequency of accidents in the construction industry by PM₁₀ concentration; iv) Modified PM₁₀ groups to classify the impact of PM₁₀ on accident. In terms of frequency analysis, the most accidents were occurred in the average concentration of PM₁₀ (32㎍/m³). However, we found that the relative frequency of accident was increased as the concentration of PM₁₀ increased. This means the higher PM₁₀ concentration can cause more accidents during construction. In addition, PM₁₀ concentration was divided as 6 groups by the WHO, but the modified PM₁₀ grade by the relative frequency on accident was suggested as 3 groups.

• Particle and aerosol research
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• v.6 no.1
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• pp.9-20
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• 2010

This study examined the concentration level of the major air pollutants in public transportation. The study was conducted between February 2009 and March 2008 at Suwon-Yeosu line in Korea. $PM_{10}$ concentration level was $100{\mu}g/m^3$ on average. The $PM_{2.5}$ to PM10 ratio in transport is 0.37, which was lower than the results published by other researches. The result also demonstrated that outdoor $PM_{10}$ concentration was about 56~60% level compared to that of the cabin. $CO_2$ concentration level in the cabin was 1,359ppm, which does not exceed 2,000ppm, which is the guideline concentration level according to the Ministry of Environment. $CO_2$ concentration level in the cabin was $CO_2=23.4{\times}N+460.2$, and about 23.4ppm in $CO_2$ concentration level increased every time one passenger was added on. The experiment conducted on the train demonstrated that the average $PM_{10}$ concentration level was $100{\mu}g/m^3$ in case of the reference cabin while average $PM_{10}$ concentration level of the modified vehicle was $68{\mu}g/m^3$. Likewise, effect of the particle reduction device for the reduction of $PM_{10}$ concentration level was approximately 21%. Meanwhile there was almost no difference in the concentration level between reference and modified cabin in case of $PM_{2.5}$. Using zeolite as an adsorbent was made to reduce the $CO_2$ concentration level in the cabin. Number of passengers was factored in, to calculate the effect of the adsorption device, which demonstrated that about 36% of $CO_2$ concentration level was reduced in the modified cabin effect of the $CO_2$ reduction device. This research analyzed the current status concerning the quality of air in the public transportation and technologies were developed that reduces major air pollutants.

• Journal of Environmental Health Sciences
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• v.26 no.4
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• pp.1-8
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• 2000

The objective of this study was to identify the sources and to estimate the source contributions to the atmospheric TSP(total suspended particulate matter) and PM-10(particulate matter with aerodynamic diameters less than 10$\mu\textrm{m}$) concentration in Taegu area. A total of 84 samples was collected during the January to December 1999. TSP and PM-10 were collected on filters by portable air sampler, and heavy metals in TSP and PM-배 were analyzed by ICO(Inductively Coupled Plasma Spectrometery) after preliminary treatment. The results were follow as : First, annual average of TSP and PM-10 concentration was 123 and 69$\mu\textrm{g}$/㎥ respectively. The concentration of TSP and PM-10 were highest in winter season compared to other seasons. Second, the concentration of Al, Fe, Mn were higher in TSP than in PM-10, indicating that these heavy metals are generally associate with natural contributions. Third, metal combinations showed that a high correlation among concentrations of heavy metals were follows: As Al, Fe and Mn in TSP ; Ni, Cr, Cd and Pb in PM-10. Finally, Statistical analysis was performed using Principal Components Analysis(PCA) in order to find possible sources of the pollutants. The factor analysis was permitted to identify four major sources(soil/road dust resuspension, waste incineration, furl combustion, vehicular emission) in each fraction. These source accounted for at least 83, 85% of variance of TSP and PM-10 concentration in Taegu area.

• Journal of Environmental Science International
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• v.26 no.1
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• pp.55-65
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• 2017

Average concentration of PM in Seoul metropolitan area satisfied the Korean air quality standard in 2010. Furthermore, concentration of PM in all boroughs across Seoul met the air environment standard in 2012. $PM_{10}$ concentration was relatively higher in center of Seoul in comparison to the rest, while $PM_{2.5}$ concentration showed exactly the contrary result. We analyzed the effect that PM emissions from vehicles would have on PM concentrations across Seoul. The results showed that average annual PM concentration recently decreased in Seoul although the number of vehicles registered annually continued its upward trend. By contrast, average fine dust concentrations in Seoul showed a decline which suggested that correlation between annual average PM concentrations and number of registered vehicles remained low. However, year-on-year vehicle registration rate recently showed a declining tendency in the same way as the trend of changes in average PM concentrations. Particularly, the upward trend in annual average PM concentrations in 2002 and 2007 was consistent with the increase in vehicle registration rate, suggesting that vehicle registration rate was closely associated with changes in PM concentrations.