• Journal of Korean Society of Environmental Engineers
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• v.39 no.4
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• pp.220-228
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• 2017

Since the fine particulate matters occurred from mainly combustion in industry and road transport effect to human respiratory health, the interest and importance are getting increased. In 2013, the World Health Organization (WHO) concluded that outdoor air pollution is carcinogenic to humans, with the particulate matter component ($PM_{10}$ and $PM_{2.5}$) of air pollution most closely associated with increased cancer incidence, especially cancer of the lung. Therefore, many researches have been studied in the quantification and data development of fine particulate matters. Currently, the Ministry of Environment and cities/regions are developing the fine particulate matter data and air emission information. Particularly just $PM_{10}$ and $PM_{2.5}$ data is used in the fine particulate matters warning and alert. The data of NOx, SOx, $NH_3$, which have the particulate matter formation potential are not well considered. Also, the researches related with particulate matter formation potential and respiratory effects by industrial sectors and cities/regions are not conducted well. Therefore, the purpose of this study is to evaluate and calculate particulate matter formation potential and respiratory effects in 11 industrial sectors and cities using NOx, SOx, $PM_{10}$, $NH_3$ data (developed by Ministry of Environment and National Institute of Environmental Research) in 2001 and 2013. The results of this study will be provided the particulate matter formation potential and respiratory effects and will be used for future the fine particulate matter researches.

• 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.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.141-150
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• 2021

Fine dust is a substance that greatly affects human health, and various studies have been conducted in this regard. Due to the human influence of particulate matter, various studies are being conducted to predict particulate matter grade using past data measured in the monitoring network of Seoul city. In this paper, predictive model have focused on particulate matter concentration in May, 2019, Seoul. The air pollutant variables were used to training such as SO₂, CO, NO₂, O₃. The predictive model based on Adaboost, and training model was dividing PM₁₀ and PM_2.5. As a result of the prediction performance comparison through confusion matrix, the Adaboost model was more conformable for predicting the particulate matter concentration grade. Although air pollutant variables have a higher correlation with PM2.5, training model need to train a lot of data and to use additional variables such as traffic volume to predict more effective PM₁₀ and PM_2.5 distribution grade.

• The Journal of Bigdata
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• v.9 no.1
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• pp.127-140
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• 2024

This study investigated the impact of vehicle grade operation restrictions in green transportation areas on the concentration of fine particulate matter (PM2.5) year by year. The results indicate that these restrictions positively affected the reduction of PM2.5 levels. The green transportation area policy reduced vehicle emissions and encouraged the use of public and eco-friendly transportation, thereby improving air quality. A notable outcome was the decrease in PM2.5 concentrations, which is expected to positively impact the health of residents in urban areas. The study considered various factors and variables related to the effectiveness of the vehicle grade operation restrictions policy. It was determined that there is a need to discuss the implementation methods of the policy, regional characteristics, and other environmental factors. These findings provide important implications for managing fine particulate matter and urban planning, suggesting that reference materials and ongoing research will be necessary considering future urban sustainability.

• Environmental Engineering Research
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• v.18 no.3
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• pp.139-143
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• 2013

This study was performed to examine the in vitro toxicities which are incurred due to the mobilization metals from standard reference material (SRM) 1648, fine ($PM_{2.5}$), and coarse ($PM_{10}$) particulate matter collected in Seoul metropolitan area. DNA single strand breaks of approximately 74% and 62% for $PM_{2.5}$ and for $PM_{10}$, respectively, were observed in the presence of chelator (EDTA or citrate)/reductant (ascorbate), as compared to the control by 2% without chelator or reductant. $PM_{2.5}$ induced about 40% more carbonyl formation with proteins in the presence of EDTA/ascorbate than $PM_{10}$. Therefore, more damage to biomolecules was incurred upon exposure to $PM_{2.5}$ than to $PM_{10}$. The treatment of a specific chelator, desferrioxamine, to the reaction mixture containing chelator plus reductant decreased the extent of damage to DNA to the level of the control, but did not substantially decrease the extent of damage to proteins. This suggests that different arrays of metals were involved in the oxidation of DNA and proteins.

• Journal of Environmental Health Sciences
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• v.33 no.6
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• pp.479-487
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• 2007

[ $PM_{2.5}$ ], particulate matter less than 2.5 um in a diameter, can penetrate deeply into the lungs. Exposure to $PM_{2.5}$ has been associated with increased hospital visits for respiratory aliments as well as increase mortality. $PM_{2.5}$ is a byproduct of combustion processes and as such has a complex composition including a variety of metallic elements, inorganic and organic compounds as well as biogenic materials (microorganisms, proteins, etc). In this study, the average concentrations of fine particulates $PM_{2.5}$ have been measured simultaneously in Asan and Seoul, Korea, by using particulate matter portable sampler from September 2001 to August 2002. Sample collection filters were analyzed by ICP-OES to determine the concentrations of metallic elements (As, Ni, Fe, Cr, Cd, Cu, Pb, Zn, Si). Annual mean $PM_{2.5}$ concentrations in Asan and Seoul were 37.70 and $45.83\;{\mu}g/m^3$, respectively. The highest concentrations of $PM_{2.5}$ were found in spring season in both cities and the concentrations of measured metallic elements except As in Asan were higher than those in Seoul, suggesting that yellow dust in spring could affect $PM_{2.5}$ concentrations in Asan rather than Seoul. The correlation coefficients of Pb and Zn were 0.343 for Asan and 0.813 for Seoul during non-yellow dust condition, suggesting that Pb and Zn were influenced with the same sources. The correlation coefficients between Si and Fe in the fine particulate mode were 0.999 (Asan) and 0.998 (Seoul) during yellow dust condition. It was suggested that these two elements were impacted by soil-related transport from China during the yellow dust storm condition.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.7-13
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• 2021

This study develops an artificial intelligence prediction system for Fine particulate Matter(PM2.5) based on the deep learning algorithm GAN model. The experimental data are closely related to the changes in temperature, humidity, wind speed, and atmospheric pressure generated by the time series axis and the concentration of air pollutants such as SO2, CO, O3, NO2, and PM10. Due to the characteristics of the data, since the concentration at the current time is affected by the concentration at the previous time, a predictive model for recursive supervised learning was applied. For comparative analysis of the accuracy of the existing models, CNN and LSTM, the difference between observation value and prediction value was analyzed and visualized. As a result of performance analysis, it was confirmed that the proposed GAN improved to 15.8%, 10.9%, and 5.5% in the evaluation items RMSE, MAPE, and IOA compared to LSTM, respectively.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.6
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• pp.67-77
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• 2019

The purpose of this study is to analyze how much particulate matter at the center of the urban park is reduced compared to the entrance of the park, where the particulate matter problem is serious. It also endeavored to analyze the relationship between the space closure rate and particulate matter reduction rate in the center of the park through the collection and analysis of experimental data. Seven flat land type urban neighborhood parks in Suseong-gu, Daegu were measured at the same place for three days. The research results are as follows. First, the center of the urban neighborhood park had an average temperature 1.05℃ lower than at the entrance and an average humidity of 2.57% higher. Second, the rate of fine dust reduction was PM1- 17.09%, PM2.5- 17.65%, PM10- 14.99%. As for the reduction rate of particulate matter, the smaller the size of the park, the greater the reduction rate. In addition, the reduction rate at the center of the park was lower on days when particulate matter concentration based on the weather reports was low. The higher the concentration at the park entrance, the higher the reduction rate was. Third, a higher the rate of space closures at the center of the park resulted in a higher effect of particulate matter reduction. Noting this, the relationship between particulate matter reduction and the space closure rate in urban neighborhood parks was clearly shown. We hope to be the basis for more extensive experimental data collection.

• Journal of the Korean Chemical Society
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• v.58 no.3
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• pp.267-276
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• 2014

The collection of $PM_{10}$ and $PM_{2.5}$ fine particulate matter samples was made at the 1100 m site of Mt. Halla of Jeju Island, located at the atmospheric boundary layer (ABL) of background area, during the fall of 2012. Their ionic and elemental species were analyzed, in order to investigate the chemical compositions and size distribution characteristics. In $PM_{2.5}$ fine particles ($d_p$ < $2.5{\mu}m$), the concentrations of the secondary formed nss-$SO{_4}^{2-}$, $NH_4{^+}$ and $NO_3{^-}$ species were 4.84, 1.98, and $1.27{\mu}g/m^3$, respectively, showing 58.2% of the total $PM_{2.5}$ mass. On the other hand, their concentrations in $PM_{10-2.5}$ coarse particles (2.5 < $d_p$ < $10{\mu}m$) were 0.63, 0.21 and $1.10{\mu}g/m^3$, respectively, occupying 22.8% of the total $PM_{10-2.5}$ mass. The comparative study of size distribution has resulted that $NH_4{^+}$, nss-$SO{_4}^{2-}$, $K^+$ and $CH_3COO^-$ are mostly existed in fine particles, and $NO_3{^-}$ is distributed in both fine and coarse particles, but $Na^+$, $Cl^-$, $Mg^{2+}$ and nss-$Ca^{2+}$ are rich in coarse particle mode.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.92-99
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• 2021

Light-scattering fine particulate matter monitors can measure particulate matter (PM) concentrations in every second and can be designed in a portable size. They can measure the concentrations of various PM sizes (PM_1.0, PM_2.5, PM_4.0 and PM₁₀) with a single sensor. They measure the number and size of particulate matters and convert them to weight per volume (concentration). These devices show a large error for asian dust. This paper proposes a scheme that compensates the PM_2.5 concenstration error for asian dust by multiple linear regression machine learning in light-scattering PM monitors. This scheme can be effective with only two or three types of PM sizes. The experimental results compare a beta-ray PM monitor of national institute of environmental research and a light-scattering PM monitor during a month. The correlation coefficient (R²) of theses two devices was 0.927 without asian dust, but it was 0.763 due to asian dust during the entire experimental period and improved to 0.944 by the proposed machine learning.