• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.20-26
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• 2019

Due to the human influence of particulate matter, various studies are being conducted to predict it using past data measured in the atmospheric environment monitoring network. However, it is difficult to precisely set the measurement environment and detailed conditions of the previously designed predictive model, and it is necessary to design a new predictive model based on the existing research results because of the problems such as the missing of the weather data. In this paper, as a previous study for particulate matter prediction, the conformity of the algorithm for particulate matter prediction was evaluated by designing the prediction model through the multiple linear regression and the artificial neural network, which are machine learning algorithms. As a result of the prediction performance comparison through RMSE, 18.13 for the MLR model and 14.31 for the MLP model, and the artificial neural network model was more conformable for predicting the particulate matter concentration.

• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.409-414
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• 2021

The growing concerns on the emission of particulate matter has prompted a demand for highly reliable particulate matter forecasting. Currently, several studies on particulate matter prediction use various deep learning algorithms. In this study, we compared the predictive performances of typical neural networks used for particulate matter prediction. We used deep neural network(DNN), recurrent neural network, and long short-term memory algorithms to design an optimal predictive model on the basis of a hyperparameter search. The results of a comparative analysis of the predictive performances of the models indicate that the variation trend of the actual and predicted values generally showed a good performance. In the analysis based on the root mean square error and accuracy, the DNN-based prediction model showed a higher reliability for prediction errors compared with the other prediction models.

• Journal of Korean Academy of Nursing
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• v.51 no.1
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• pp.68-79
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• 2021

Purpose: This study aimed to analyze the mass and social media contents and structures related to particulate matter before and after the policy enforcement of the comprehensive countermeasures for particulate matter, derive nursing implications, and provide a basis for designing health policies. Methods: After crawling online news articles and posts on social networking sites before and after policy enforcement with particulate matter as keywords, we conducted topic and semantic network analysis using TEXTOM, R, and UCINET 6. Results: In topic analysis, behavior tips was the common main topic in both media before and after the policy enforcement. After the policy enforcement, influence on health disappeared from the main topics due to increased reports about reduction measures and government in mass media, whereas influence on health appeared as the main topic in social media. However semantic network analysis confirmed that social media had much number of nodes and links and lower centrality than mass media, leaving substantial information that was not organically connected and unstructured. Conclusion: Understanding of particulate matter policy and implications influence health, as well as gaps in the needs and use of health information, should be integrated with leadership and supports in the nurses' care of vulnerable patients and public health promotion.

• Land and Housing Review
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• v.11 no.4
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• pp.93-98
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• 2020

This study examined the particulate removal performance of three different types of air filters inside a heat exchanger. Of interest was the ability of each filter type in reducing the transmission of outdoor particulate matter of PM10 from entering an apartment while the heat exchanger was in operation. The study tested one commonly used medium filter (E11 grade) and two HEPA filters (H13 grade) of different thicknesses. Two different concentrations of particulate matter were used in the experiment to address different ambient air quality conditions in Korea, 32.75 ㎍/㎥ and 67.26 ㎍/㎥. Study results indicated that under the particulate matter concentration of 32.75 ㎍/㎥, all three filters were capable of removing more than 95% of the fine dust. However at a particulate matter concentration of 67.26 ㎍/㎥, the medium E11 grade filter was only able to remove about 90% of the particulates whereas the HEPA H13 grade filters were able to remove 95% or more of the particulates. The thicker HEPA filter (40T) was also more effective in removing particulates than the thinner HEPA filter (20T) by about 1.6 to 3 percentage points. Based on the findings of this study, it is recommended that HEPA filters of 20T thickness or greater be used during the high air pollution seasons of winter and spring in Korea while medium filters can be used during the other seasons to reduce outdoor air pollution transmission indoors.

• 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.

• BMB Reports
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• v.56 no.2
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• pp.96-101
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• 2023

Particulate matter is an air pollutant composed of various components, and has adverse effects on the human body. Particulate matter is known to induce cell death by generating an imbalance in the antioxidant system; however, the underlying mechanism has not been elucidated. In the present study, we demonstrated the cytotoxic effects of the size and composition of particulate matter on small intestine cells. We found that particulate matter 2.5 (PM_2.5) with extraction ion (EI) components (PM_2.5 EI), is more cytotoxic than PM containing only polycyclic aromatic hydrocarbons (PAHs). Additionally, PM-induced cell death is characteristic of ferroptosis, and includes iron accumulation, lipid peroxidation, and reactive oxygen species (ROS) generation. Furthermore, ferroptosis inhibitor as liproxstatin-1 and iron-chelator as deferiprone attenuated cell mortality, lipid peroxidation, iron accumulation, and ROS production after PM_2.5 EI treatment in human small intestinal cells. These results suggest that PM_2.5 EI may increase ferroptotic-cell death by iron accumulation and ROS generation, and offer a potential therapeutic clue for inflammatory bowel diseases in human small intestinal cells.

• Journal of People, Plants, and Environment
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• v.22 no.1
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• pp.31-38
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• 2019

Experiments were carried out to investigate the effects of mosses on the removal of particulate matter (PM 10) and volatile organic compounds (VOCs) in an indoor space and on the composition of air. For particulate matter removal experiments, 0.2 g mosquitto coil was burned in a glass chamber, where three kinds of mosses (Plagiomnium cuspidatum, Myuroclada maximowiczii, Etodon luridus) were placed. For VOCs removal experiments, 1 mL paint thinner was volatilized in a glass chamber, where Plagiomnium cuspidatum and Myuroclada maximowiczii were used. As a result, it was found that particulate matter was effectively removed by the three mosses, and the removal efficiency of particulate matter increased as the amount of mosses increased. The amount of VOCs was similar to the level in the control when a low amount of mosses (2 and 4 plates) was used. However, the removal efficiency of VOCs was significant when 6 plates of mosses were used. On the other hand, formaldehyde concentration was 40 times more than the control and carbon monoxide 30 times, when 0.2 g of mosquito repellent was completely burned in a glass chamber. Also formaldehyde removal effect was significant when 6 plates of mosses were placed. However, there was no change in the concentration of indoor oxygen, temperature and humidity by moss plants. In conclusion, the moss plants were effective in removing particulate matter and VOCs, and they are expected to be used for indoor decoration and landscape in order to improve indoor air quality in the future.

• Journal of Environmental Science International
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• v.32 no.2
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• pp.131-137
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• 2023

Filter and microbalance sensitivity in measuring fine particulate matter mass is greatly influenced by particulate properties and environmental factors. Temperature and humidity control inside a measuring chamber with a microbalance, and neutralization of static charges on filters are essential for consistent filter weighing. Commercial weighing chambers are expensive with a unit price of tens of millions won. This study developed an inexpensive weighing chamber for weighing fine particulate matter and evaluatedits weighing performance. A microbalance with 1 ㎍ precision was used to measure the weight of a filter. The microbalance was set in a transparent acrylic enclosure (100 × 60 × 65 cm³) equipped with temperature and humidity control equipments. Weighing performance of the chamber was examined using Teflon filters with or without different particulate sample types. Temperature and humidity were maintained at approximately 23.2±1.2 ℃ and 36.2±1.8℃ for 8 days, respectively.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.341-349
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• 2020

The present study aims to investigate the sequential mediation model in the relationships between optimistic bias, subjective perception, risk perception, and the future time perspective in terms of a particulate matter and depression, using the sequential mediation model. An online self-reported survey was conducted on 545 participants who agreed to participate in the current study. We considered depression as a dependent variable, optimistic bias as an independent variable, and subjective perception of particulate matter, the risk perception of particulate matter, future-time-perspective as mediators. The sequential mediation analysis was conducted using the SPSS Macro. The results show that optimistic bias was not directly related to depression, but was related to indirect paths through the subjective perception of particulate matter, the risk perception of particulate matter, and future time perspective. More specifically, the lack of optimistic bias was related to a tendency to subjectively perceive the quality of air pollution more seriously and a limited future time perspective, which subsequently related to depression. Future studies should pay more attention to the effects of particulate matter on the quality of life and mental health.

• Journal of Korean Elementary Science Education
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• v.19 no.1
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• pp.101-112
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• 2000

The purpose of this study was to develop the modular multimedia instructional materials emphasizing the particulate nature on the realm of matter in elementary science classes. Features of the material are as follows: 1 To be in focus on particulate model named 'phenomenal magnifying glasses' in order to change the student's belief system of continuous matter into the belief system of particulate matter . The 'phenomenal magnifying glasses' is a new instructional strategy designed to change into the view of particulate matter through facilitating the reflective thinking resulted from the simultaneous consideration of experimental phenomena(macroscopic world) and particulate model(microscopic world). 2. To introduce modular system into the instructional materials, which was consisted of 14 subunits according to the sequential instruction unit of 'molecule and molecular motion'. Each subunit was composed of 5 types of modules(module 1: motivation, module 2: experimental result, module 3: discussion, module 4: phenomenal magnifying glasses, module 5: related experiment) 3. The multimedia program was composed of 36 kinds of experimental animation and 59 kinds of computer animation materials combined with text resources, photographic materials and sounds.