• Food Science and Industry
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• v.54 no.1
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• pp.29-33
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• 2021

Particulate matter (PM) is an air pollutant that causes serious environmental problems in Korea and other countries. The annual average PM10 concentration in Korea is around 40 ㎛/㎥, which is more than twice as high as the WHO recommended standard. When consumed with food, fine PM can pose a risk to humans. However, the risk of fine PM has been focused on the risk of fine PM introduced through the respiratory system. We investigated the quantitative measuring methods of PM10 on food surface to identify possible risk analysis of fine PM. The surfaces of food with artificially contaminated PM10 were observed with a scanning electron microscope(SEM). An automatic object-based image analysis was used to analyze the amount and size distribution of particulate matter contained in SEM micrographs.

• 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.

• Economic and Environmental Geology
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• v.55 no.6
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• pp.701-716
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• 2022

This study investigated the distribution behaviors of PM2.5 and PM10 at two air quality monitoring sites, Go-eup (GO) and Backseokeup (BS), located in Yangju City, South Korea. The amounts of emissions sources of pollutants were analyzed based on the Clean Air Policy Support System (CAPSS), and the contribution rates of neighboring cities were enumerated in Yangju. Yangju has a geological basin structure, and it is a city with mixed urban and rural characteristics. The emission concentration of particulate matter was affected by geological and seasonal factors for all sites observed in this study. Therefore, these factors should be considered when establishing policies related to particulate matter. Because the official GO and BS station sites in Yangju are both situated in the southern part of the city, the representativeness of both stations was checked using correlation analysis for the measurement of PM2.5 and PM10 by considering two more sites-those of Bongyang-dong (BY) and the Gumjun (GJ) industrial complex. The data included discharge amounts for business types 4 and 5, which were not sufficiently considered in the CAPSS estimates. Because the 4 and 5 types of businesses represent over 92.6% of businesses in this city, they are workplaces in Yangju that have a significant effect on the total air pollutant emission. These types of businesses should be re-inspected as the main discharge sources in industry, and basic data accumulation should be carried out. Moreover, to manage the emission of particulate matter, attainable countermeasures for the main sources of these emissions should be prepared in a prioritized fashion; such countermeasures include prohibition of backyard burning, supervision of charcoal kilns, and management of livestock excretions and fugitive dust in construction sites and on roads. The contribution rates by neighboring cities was enumerated between 6.3% and 10.9% for PM2.5. Cooperation policies are thought to be required with neighboring cites to reduce particulate matter.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.471-479
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• 2019

The purpose of this study was to investigate the determinants of preventive behavior intention to the particulate matter. The results based on the survey of 280 university students showed that the perceived susceptibility and barriers to the particulate matter do not have statistically significant effects on the preventive behavior intention. However, perceived severity and benefits, subjective norm, and self-efficacy to the particulate matter had statistically significant positive effects on the preventive behavior intention. The results of this study suggested that communication strategies to increase perceived severity and benefits, subjective norm and self-efficacy should be required to improve the degree of preventive behavior intention to the particulate matter of college students. It is expected to contribute explaining preventive actions against environmental hazards such as air pollution in the future.

• 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.

• 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 Environmental Science International
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• v.29 no.5
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• pp.531-539
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• 2020

To understand the composition, quantity, and quality of Suspended Particulate Organic Matter (SPOM) in the Gangneung Namdae Stream, Korea, we examined spatiotemporal variations in water temperature, salinity, chlorophlly a (Chl a), Particulate Organic Carbon (POC) and nitrogen (PON), and carbon stable isotope (δ¹³C) of SPOM at six stations in June (pre-monsoon), July (monsoon), and September (post-monsoon) 2017. With increasing precipitation, the average POC and C/N values increased significantly in July than in June. In September, the values decreased with decreasing precipitation. The δ¹³C values showed irregular spatiotemporal fluctuations among the stations and periods, thereby suggesting a greater contribution of autochthonous organic matter to the pool of SPOM than that of allochthonous organic matter derived from upstream. In addition, the large and irregular changes in POC, C/N ratio, C:Chl a, and δ¹³C compared to that of PON were observed for all periods among the stations, indicating a serial discontinuity of the stream. Our results suggest that the Gangneung Namdae Stream is significantly influenced by the increase in freshwater discharge caused by heavy rainfalls during the summer monsoon and post-monsoon periods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.56-62
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• 2021

High forecast accuracy is required as social issues on particulate matter increase. Therefore, many attempts are being made using machine learning to increase the accuracy of particulate matter prediction. However, due to problems with the distribution of imbalance in the concentration and various characteristics of particulate matter, the learning of prediction models is not well done. In this paper, to solve these problems, a binary classification model was proposed to predict the concentration of particulate matter needed for prediction by dividing it into two classes based on the value of 80㎍/㎥. Four classification algorithms were utilized for the binary classification of PM10. Classification algorithms used logistic regression, decision tree, SVM, and MLP. As a result of performance evaluation through confusion matrix, the MLP model showed the highest binary classification performance with 89.98% accuracy among the four models.