• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.48-54
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• 2020

Researchers recognized air pollution changes causing diseases and difficulties in living due to environmental pollution following various human activities, and have studied how to avoid fine dust harmful to the human respiratory system to be healthy. To this end, Arduino is used to equip fine dust level sensors in drones to measure the fine dust levels, visualize the measurements with LED indicator colors depending on the measurements to inform users of the danger of fine dust, and use the benefits of drones to specify dangerous fine dust zones and measure the fine dust levels. Users can see the changes depending on the fine dust levels in real time with the LED indicators. This will contributes to measuring fine dust levels easily in dangerous areas. Mission Planner (ArduPilot) is used to set up the GPS of drone, and store the data from the dust sensor as contents. This study aims to establish a method for improving the environment to measure fine dust levels with drones with LED indicators for fine dust, and reduce fine dust.

• 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 Building Construction Conference
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• 2019.11a
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• pp.172-173
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• 2019

As the damage to fine dust increased, the Republic of Korea designated fine dust as a social disaster. The composition of the fine dust is composed of carbon, sulfate, nitrate, ammonium and minerals. The cause of fine dust is naturally generated by dirt, pollen, etc. In addition, there are artificial causes such as gaseous vehicle exhaust gas emitted from the use of fossil fuel. When fine dust enters the human body through breathing, it causes various respiratory diseases and skin diseases. In IARC, fine dust was designated as a carcinogen group 1. In this research, we tried to adsorb fine dust by physical adsorption using powdered activate carbon. Powdered activate carbon is a powdered activated carbon activated in a carbonized state. Porous material with high specific surface area and low density. Experimental items were tested for density, water absorption, and fine dust concentration according to the PAC addition ratio. Basic experiments were carried out to fabricate the fine dust adsorption matrix.

• 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 Platform Technology
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• v.8 no.4
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• pp.59-69
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• 2020

Recently, the seriousness of ultrafine dust and fine dust has emerged as a national disaster, but small and medium-sized cities in provincial areas lack fine dust monitoring stations compared to their area, making it difficult to manage fine dust. Although the computing resources for collecting and processing fine dust data are not large, it is necessary to utilize cloud and private and public data to share data. In this paper, we proposed a small edge computing system that can measure fine dust, ultrafine dust and temperature and humidity and process it to provide real-time control of fine dust and service to the public. Collecting fine dust data and using public and private data to service fine dust ratings is efficient to handle with edge computing using raspberry pie because the amount of data is not large and the processing load is not large. For the experiment, the experiment system was constructed using three sensors, raspberry pie and Thinkspeak, and the fine dust measurement was conducted in northern part of kyongbuk region. The results of the experiment confirmed the measured fine dust measurement results over time based on the GIS data of the private sector.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.55-58
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• 2024

In this paper, we propose a design and implementation of big data-based fine dust anomaly detection machine learning system. The proposed is system that classifies the fine dust air quality index through meteorological information composed of fine dust and big data. This system classifies fine dust through the design of an anomaly detection algorithm according to the outliers for each air quality index classification categories based on machine learning. Depth data of the image collected from the camera collects images according to the level of fine dust, and then creates a fine dust visibility mask. And, with a learning-based fingerprinting technique through a mono depth estimation algorithm, the fine dust level is derived by inferring the visibility distance of fine dust collected from the monoscope camera. For experimentation and analysis of this method, after creating learning data by matching the fine dust level data and CCTV image data by region and time, a model is created and tested in a real environment.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.120-121
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• 2019

The construction industry is known to be one of the representative industries that generate fine dust. Therefore, reducing the amount of fine dust generated in construction sites is very important for the overall fine dust management. Based on this, this study proposed the concept of fine dust measurement and removal technology combined with advanced technologies such as drones and IoT. The qualitative, quantitative and risk elimination effects that can be expected when applying the proposed technique are analyzed. We will verify the effectiveness of the proposed concept through system development and field application, and evaluate specific economic feasibility through cost analysis. The proposed concept will be validated through system development and field application and evaluated specific economics through cost analysis.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.163-164
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• 2020

When measuring fine dust at a large-scale site such as complex construction, the change in the value of fine dust measurement is large due to the influence of the time, location, wind speed, wind direction, and humidity. This study aims to find out the results of measuring fine dust in an actual construction site and inferring the changes.