• Journal of Wellbeing Management and Applied Psychology
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• v.6 no.1
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• pp.1-7
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• 2023

Purpose: Fine dust is classified as a group 1 carcinogen and poses a significant environmental problem that urgently requires improvement to protect the environmental rights of citizens. Given the difficulty of implementing measures to reduce overseas sources of fine dust, it is essential to first devise specific measures to address domestic emission sources. As such, this study aims to analyze the correlation between earthwork volume control and fine dust concentration as preliminary management measures to reduce the impact of scattering dust at construction sites. Based on real-time air quality information, field management measures will be presented to mitigate the effects of dust emissions. Research design, data and methodology: As examples, we selected construction sites that had recently undergone small-scale environmental impact assessment consultations. The standard earthwork volume was classified into grades using 20% intervals, and we applied AERMOD to predict the weighted concentration of fine dust based on the earthwork volume class and analyzed its correlation. Results: The results of this study demonstrate a strong correlation between earthwork volume and fine dust concentration. By utilizing the correlation analysis between earthwork volume and fine dust concentration on-site, this finding can be utilized as an effective fine dust management plan. Conclusions: This involves determining the daily earthwork intensity based on real-time air quality information and implementing measures to reduce scattering dust.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.173-186
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• 2019

Recently, as the amount of fine dust has risen rapidly, our interest is increasing day by day. It is virtually impossible to remove fine dust. However, it is best to predict the concentration of fine dust and minimize exposure to it. In this study, we developed a mathematical model that can predict the concentration of fine dust using various information related to the weather and air quality, which is provided in real time in 'Air Korea (http://www.airkorea.or.kr/)' and 'Weather Data Open Portal (https://data.kma.go.kr/).' In the mathematical model, various domestic seasonal variables and atmospheric state variables are extracted by multiple regression analysis. The parameters that have significant influence on the fine dust concentration are extracted, and using ANN (Artificial Neural Network) and SVM (Support Vector Machine), which are machine learning techniques, we proposed a prediction model. The proposed model can verify its effectiveness by using past dust and weather big data.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.30-37
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• 2022

Because fine dust (PM10) has a close effect on the environment, fine dust generated in the climate and living environment has a bad effect on the human body. In this study, the LSTM model was applied to predict and analyze the effect of fine dust on Gwangju Metropolitan City in Korea. This paper uses prediction values of input variables selected through correlation analysis to confirm fine dust prediction performance. In this paper, data from the Gwangju Metropolitan City area were collected to measure fine dust. The collection period is one year's worth of data was used from january to December of 2021, and the test data was conducted using three-month data from January to March of 2022. As a result of this study, in the as a result of predicting fine dust (PH10) and ultrafine dust (PH2.5) using the LSTM model, the RMSE was 4.61 and the test result value was as low as 4.37. This reason is judged to be the result of the contents of the one-year sample.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.345-347
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• 2021

Various studies are being conducted to improve the accuracy of fine dust, but there is a problem that deep learning models are not well learned due to various characteristics according to the concentration of fine dust. This paper proposes an EEMD-based fine dust concentration prediction model to decompose the characteristics of fine dust concentration and reflect the characteristics. After decomposing the fine dust concentration through EEMD, the final fine dust concentration value is derived by ensemble of the prediction results according to the characteristics derived from each. As a result of the model's performance evaluation, 91.7% of the fine dust concentration prediction accuracy was confirmed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.510-512
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• 2022

Various studies are being conducted to improve the accuracy of fine dust, but there is a problem that deep learning models are not well learned due to various characteristics according to the concentration of fine dust. This paper proposes an EEMD-based fine dust concentration prediction model to decompose the characteristics of fine dust concentration and reflect the characteristics. After decomposing the fine dust concentration through EEMD, the final fine dust concentration value is derived by ensemble of the prediction results according to the characteristics derived from each. As a result of the model's performance evaluation, 91.7% of the fine dust concentration prediction accuracy was confirmed.

• Journal of Service Research and Studies
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• v.10 no.1
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• pp.55-67
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• 2020

Fine dust has recently become one of the greatest concerns of Korean people and has been a target of considerable efforts by governments and local governments. In the academic world, many researches have been carried out in relation to fine dust, but the research on the economic field has been relatively few. So we wanted to know how fine dust affects the economy. Big data of PM10 concentration for fine dust and fine dust theme stock price were collected for five years from 2013 to 2017. Regression analysis was performed using the linear regression model, the generalized least squares method. As a result, the change in the fine dust concentration was found to have a effect on the related theme stocks' price. When the fine dust concentration increased compared to the previous day, the fine dust theme stocks' price also showed a tendency to increase. Also, according to the analysis of stock price change from 2013 to 2017 based on fine dust theme stocks, companies with large regression coefficients were changed every year. Among them, the regression coefficients of Monalisa were repeatedly high in 2014, 2015, 2017, Samil Pharmaceutical in 2015, 2016 and 2017, and Welcron in 2016 and 2017, and the companies were judged to be sensitive to the concentration of fine dust. The companies that responded the most in the past 5 years were Wokong, Welcron, Dongsung Pharmaceutical, Samil Pharmaceutical, and Monalisa. If PM2.5 measurement data are accumulated enough, it would be meaningful to compare and analyze PM2.5 concentration with independent variables. In this study, only the fine dust concentration is used as an independent variable. However, it is expected that a more clear and well-explained result can be found by adding appropriate additional variables to increase the explanatory power.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.587-591
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• 2019

Fine dust aerated in the atomsphere penetrates our lungs and blood lines through respiratory. Recent fine dust problems in Korea leads to development of various air purifiers. The researchers used this to study systems that could replace chemical filters. In order to compare the effect of the reduction of moss and conventional chemical filter(Hepa), a 1 cubic meter cube was prepared and the amount of the concentration of fine dust reduction was compared under controlled environment. Under the high concentration of fine dust, a test was done to figure out the reduction rate of the fine dust concentration by using air purification system with moss, hepa, and no filter. The air purification system(moss, hepa, and no filter) were operated 90 times in total, 30 times each. The test explains that the reduction of the fine dust amount and the rate of fine dust concentration. The results illustrate that the reduction of the amount fine dust was 138.93 after using air purification system with moss filter. In contrast, the usage of air purification system with hepa filter reduced the amount of fine dust to 76.57. And the air purification with no filter shows that the slight reduction of fine dust amount at 0.10. In the rate of fine dust concentration, moss filter was significantly higher than that of hepa, no filter (0.2379, 0.1298 and 0.0063 each). The results have confirmed that moss is effective in reducing fine dust concentration, and it is expected that with further improvement it can be used as a means to replace or supplement existing chemical filters in air purifier.

• Journal of the Korean housing association
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• v.26 no.4
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• pp.55-62
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• 2015

The international concern on the inhalable fine dust is continuing to increase. In addition to the toxic properties of the fine dust itself, it can be more dangerous than other environmental factors since the dust pollution is hard to be detected by human sense. Although the information on outdoor air condition can be acquired easily, the indoor dust concentration is another problem because the indoor air condition is influenced by the architectural environment and human activity. It means occupants may be exposed to indoor dust pollution over a long period without being aware. Therefore the indoor dust concentration should be measured separately and visualized as an intuitive information. By visualizing, the indoor dust concentration in each space can be recognized practically in compare with the degree of pollution in adjacent spaces. Besides the visualization outcome can be used as base data for related research such as an analysis of the relation between indoor dust concentration and architectural environment. Meanwhile, with the development of network and micro sensing devices, it became possible to collect wide range of indoor environment data. In this regards, this paper suggests a system for visualization of indoor dust concentration and demonstrates it on an actual space.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.285-292
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• 2023

Recently, the impact of fine dust on health has become a major topic. Fine dust is dangerous because it can penetrate the body and affect the respiratory system, without being filtered out by the mucous membrane in the nose. Since fine dust is directly related to the industry, it is practically impossible to completely remove it. Therefore, if the concentration of fine dust can be predicted in advance, pre-emptive measures can be taken to minimize its impact on the human body. Fine dust can travel over 600km in a day, so it not only affects neighboring areas, but also distant regions. In this paper, wind direction and speed data and a time series prediction model were used to predict the concentration of fine dust in Seoul, and the correlation between the concentration of fine dust in Seoul and the concentration in each region was confirmed. In addition, predictions were made using the concentration of fine dust in each region and in Seoul. The lowest MAE (mean absolute error) in the prediction results was 12.13, which was about 15.17% better than the MAE of 14.3 presented in previous studies.

• Journal of the Health Care and Life Science
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• v.9 no.2
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• pp.231-235
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• 2021

This study collects and provides environmental data related to weather by measuring the concentration levels of fine dust at different altitudes, with the aim of forecasting fine dust concentration changes, particularly in the areas where the vulnerable reside. Institutions in the healthcare-related fields can use the real-time data on the changing fine dust concentration, which is collected through different combinations of various measuring devices and drone technologies, which have recently developed at a rapid pace. The study first collects data on the following: PM1 (fine dust particles <1 ㎛ in size), PM2.5 (fine dust particles <2.5 ㎛ in size), and PM10 (fine dust particles <10 ㎛ in size) and predicts respective changes and suggests data on various high levels. The device that was used in the study measured fine dust concentration, humidity, temperature, atmospheric pressure, carbon dioxide, total volatile organic compounds (TVoc), and formaldehyde.