• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.265-285
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• 2018

In this study, we applied a deep neural network model to predict four grades of fine dust $PM_{10}$, 'Good, Moderate, Bad, Very Bad' and two grades, 'Good or Moderate and Bad or Very Bad'. The deep neural network model and existing classification techniques (such as neural network model, multinomial logistic regression model, support vector machine, and random forest) were applied to fine dust daily data observed from 2010 to 2015 in six major metropolitan areas of Korea. Data analysis shows that the deep neural network model outperforms others in the sense of accuracy.

• Journal of the Korean Institute of Rural Architecture
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• v.25 no.4
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• pp.9-16
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• 2023

In this study, the status and characteristics of fine dust and its impact on neighboring areas were investigated to proactively respond to the government's environmental regulations expected in the future and to minimize the damage by the fine dust generated at construction waste intermediate treatment plants. In addition, since there are no such plants that can affect the surroundings with no houses or other waste treatment sites nearby, an independently located construction waste intermediate treatment plant was selected to compare the characteristics of fine dust with that from the construction waste intermediate treatment sites located in the downtown area. The conclusions of the study are as follows. (1) The measurement results of PM10 at 4 points in the plant showed that the location where the crushing facility was operating had an elevated level of fine dust at 80㎍/m³ on average and a maximum of 124㎍/m³, and the level rose to 110㎍/m³ at points where vehicles frequent. (2) The PM2.5 measurement results inside the plant showed that the average concentration of the reference point was 16㎍/m³ and the maximum value was 20㎍/m³, which was distributed within the management standard. (3) It was found that the average concentration of PM10 in the nearby area ranged from 28 to 38㎍/m³, which was similar to or lower than 36㎍/m³ of the reference point. Therefore, the concentration of the fine dust generated in the plant had a negligible effect on the increase in concentration of fine dust in nearby areas. (4) The heavy metal contents were measured from the filter paper collected from the plant. The PM10 was found to be about 14 to 26ng/m³, and PM 2.5 was 25 to 28ng/m³, which was the average of domestic atmospheric concentrations. (5) The SEM-EDX analysis results showed that the PM10 contained Si and O around 40% similarly for both. The SiO2, a component of silica occupied the most and C was present as CaCO3, which was assumed to be a limestone component. The remaining components included NaO, Al2O3, and CaO as trace oxides. (6) The SEM-EDX analysis results showed that the PM 2.5 contained 5 to 7% of Cl, which is a chlorine ion, and a small amount of K was detected at 2.51% in the sample from the shutdown plant.

• Journal of the Korean Society of Visualization
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• v.22 no.1
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• pp.34-39
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• 2024

With the fine particulate matter (PM) poses a serious threat to public health and the environment. The ultrafine PM in particular can cause serious problems. This study investigates the effectiveness of a submicron dry fog system in removing fine PM. Two methods are used to create fine dust particles: burning incense and utilizing an aerosol generator. Results indicate that the dry fog system effectively removes fine dust particles, with a removal efficiency of up to 81.9% for PM₁₀ and 61.9% for PM_2.5 after 30 minutes of operation. The dry fog, characterized by a mean size of approximately 1.5 ㎛, exhibits superior performance in comparison to traditional water spraying methods, attributed to reduced water consumption and increased contact probability between water droplets and dust particles. Furthermore, experiments with uniform-sized particles which sizes are 1 ㎛ and 2 ㎛ demonstrate the system's capability in removing ultrafine PM. The proposed submicron dry fog system shows promise for mitigating fine dust pollution in various industrial settings, offering advantages such as energy consumption and enhanced safety for workers and equipment.

• Environmental and Resource Economics Review
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• v.31 no.1
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• pp.85-112
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• 2022

This study analyzed the influence of domestic and Chinese factors on fine dust(PM10 & PM2.5) in Korea by using the panel quantile regression. Daily analysis was conducted for 11 regions in Korea. For domestic factors, electricity demand and traffic volume, and for Chinese factors, interaction term of Chinese three cities' fine dust and the domestic west wind are used. As a result, the influence of domestic factors was different when the domestic fine dust concentration was high and low. When the fine dust concentration was low, electricity demand had a positive effect only on PM2.5, and didn't affect PM10 in the national analysis. In regional analysis, the amount of electricity demand had a significant effect on fine dust and ultrafine dust only in the capital area and Chungcheong. Electricity demand was found to significantly increase both PM2.5 and PM10 when it was high. On the other hand, it was confirmed that the Chinese factor always had a significant effect regardless of the concentration of PM10 and PM2.5. Therefore, in order to solve the problem of high concentration of fine dust, in addition to international cooperation, the reduction of PM2.5 generated by domestic thermal power generation should also be strengthened compared to the present.

• Journal of Environmental Science International
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• v.22 no.6
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• pp.651-666
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• 2013

This study introduces a novel approach to the differentiation of two phenomena, Asian Dust and haze, which are extremely difficult to distinguish based solely on comparisons of PM10 concentration, through use of the Optical Particle Counter (OPC), which simultaneously generates PM10, PM2.5 and PM1.0 concentration. In the case of Asian Dust, PM10 concentration rose to the exclusion of PM2.5 and PM1.0 concentration. The relative ratios of PM2.5 and PM1.0 concentration versus PM10 concentration were below 40%, which is consistent with the conclusion that Asian Dust, as a prime example of the coarse-particle phenomenon, only impacts PM10 concentration, not PM2.5 and PM1.0 concentration. In contrast, PM10, PM2.5 and PM1.0 concentration simultaneously increased with haze. The relative ratios of PM2.5 and PM1.0 concentration versus PM10 concentration were generally above 70%. In this case, PM1.0 concentration varies because a haze event consists of secondary aerosol in the fine-mode, and the relative ratios of PM10 and PM2.5 concentration remain intact as these values already subsume PM1.0 concentration. The sequential shift of the peaks in PM10, PM2.5 and PM1.0 concentrations also serve to individually track the transport of coarse-mode versus fine-mode aerosols. The distinction in the relative ratios of PM2.5 and PM1.0 concentration versus PM10 concentration in an Asian Dust versus a haze event, when collected on a national or global scale using OPC monitoring networks, provides realistic information on outbreaks and transport of Asian Dust and haze.

• Journal of Environmental Health Sciences
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• v.45 no.2
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• pp.126-133
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• 2019

Objectives: In this study, a nine-stage cascade impactor was used to collect dust, and the concentration of $PM_{2.1}$ & $PM_{10}$ and alveolar deposition ratio were investigated. Methods: This study was conducted at Kunsan National University from May to June 2016. A nine-stage Cascade Impactor was used to analyze the concentrations of fine and ultrafine dust and to estimate the alveolar deposition rate by particle size of atmospheric dust particles. The pore size of each stage of the collector used in this study gradually increased from F to 0, with the F-stage as the last stage. Results: The mass fraction of PM showed a bimodal distribution divided into $PM_{2.1}$ & $PM_{10}$ based on $2.1-3.1{\mu}m$. The average mass fraction of particulate matter in the range of $2.1-3.1{\mu}m$ was 44%, and the area occupied by $PM_{2.1}$ was similar. Therefore, the Gunsan area is considered to be a region where there are similar effects from anthropogenic and natural sources. Conclusion: Dust collecting efficiency increased with the stage of collecting fine dust, and the efficiency of collection was very low at the stage of collecting ultra-fine dust. The seasonal overall efficiency of the Cascade Impactor was 44% in spring and 37.4% in summer, and the average overall efficiency was 40.7%. The alveolar deposition rate of $PM_{2.1}/PM_{10}$ during the sampling period was estimated to be about 75% deposited in the alveoli.

• 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 the Korea Academia-Industrial cooperation Society
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• v.10 no.7
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• pp.1642-1647
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• 2009

Changes in aerosol characteristics during 2006 ${\sim}$2008 Asian dust events in Cheoan were investigated by measuring mass, ion and elemental concentrations of fine and coarse particles. The average mass concentrations of daily TSP, PM10, PM2.5 were 214.9, 160.3, and 95.9${\mu}\;g/m^3$during Asian dust events, which were 3.08, 2.58, and 1.95 times higher than Non-asian dust events. The maximum concentrations of TSP, PM10, and PM2.5 were 850.1, 534.4, and 233.3${\mu}\;g/m^3$, which were 12.19, 8.60, and 4.76 times higher, respectively. Increases in ion concentrations were not significant during Asian dust events, but elemental concentrations including soil components such as Fe, Al, Ti increased by 17.1 and 43.4 times for fine and coarse particles, respectively. The results clearly indicate that metallic components from soil constituents were the major components in Asian dusts sampled at Cheonan.

• Journal of Platform Technology
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• v.9 no.4
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• pp.79-89
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• 2021

In this paper, the fine dust (PM10) and ultrafine dust (PM2.5) measurement platforms are designed to be mobile and fixed using oneM2M, the international standard for IoT. The fine dust measurement platform is composed and designed with a fine dust measurement device, agent, oneM2M platform, oneM2M IPE, and monitoring system. The main difference between mobile and fixed is that the mobile uses the MQTT protocol for interconnection between devices and services without blind spots based on LTE connection, and the fixed uses the LoRaWAN protocol with low power and wide communication range. Not only fine dust, but also temperature, humidity, atmospheric pressure, volatile organic compounds (VOC), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and noise data related to daily life were collected. The collected sensor values were managed using the common API provided by oneM2M through the agent and oneM2M IPE, and it was designed into four resource types: AE and container. Six functions of operability, flexibility, convenience, safety, reusability, and scalability were analyzed through the fine dust measurement platform design.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.595-601
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• 2018

Recently, as the fine dust level has risen rapidly, there is a great interest. Exposure to fine dust is associated with the development of respiratory and cardiovascular diseases and has been reported to increase death rate. In addition, there exist damage to fine dusts continues at industrial sites. However, exposure to fine dust is inevitable in modern life. Therefore, predicting and minimizing exposure to fine dust is the most efficient way to reduce health and industrial damages. Existing fine dust prediction model is estimated as good, normal, poor, and very bad, depending on the concentration range of the fine dust rather than the concentration value. In this paper, we study and implement to predict the PM10 level by applying the Artificial neural network algorithm and the K-Nearest Neighbor algorithm, which are machine learning algorithms, using the actual weather and air quality data.