• Journal of Environmental Health Sciences
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• v.45 no.1
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• pp.30-41
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• 2019

Objectives: The effects of particulate matter and ozone on health are being reported in a number of studies. These effects are likely to be stronger on the elderly population, but studies in this regard are scarce. The purpose of this study was to examine the effects of particulate matter ${\leq}2.5{\mu}m$ and ozone on the acute health status of the elderly population. Methods: In order to analyze the health status of the elderly population, the NHIS-Senior Cohort data was used. In this study of people 60 years or older in Seoul, the number of outpatient visits and ER visits between 2002 and 2013 were calculated. Each disorder and the lag effect were analyzed separately. Particulate matter and ozone were analyzed using both the single exposure model and the adjusted multi-exposure model. Results: In the single exposure analysis with PM2.5 as the exposure variable, with each increase of $10{\mu}g/m^3$, the number of outpatient visits increased by 1.0081 times, vascular disease 1.0065 times, chronic pulmonary disease 1.0086 times, and diabetes 1.0055 times. In the multi-exposure model adjusting for ozone, the number of outpatient visits increased by 1.0066 times. There was a one-day lag effect and 1.0066 times increase between PM2.5 and ER visits in the multi-exposure model and 1.0057 times when adjusted for ozone (p value <0.10). There was a one-day lag effect in all multi-exposure models with ozone as the main variable, and when the particulate matter was adjusted, there was a one-day delay and 1.0143 times increase in ER visits. Conclusions: In our study, an increase in the number of outpatient and ER visits in the elderly population in accordance with the increase in PM2.5 and ozone was found. The association found in our study could also produce a socioeconomic burden. Future studies need to be performed in regards to younger populations and other air pollutants.

• Journal of dental hygiene science
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• v.24 no.1
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• pp.22-28
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• 2024

Background: Particulate matter (PM) has been extensively observed due to its negative association with human health. Previous research revealed the possible negative effect of air pollutant exposure on oral health. However, the predictive model between air pollutant exposure and the prevalence of periodontitis has not been observed yet. Therefore, this study aims to propose a predictive model for the number of patients with periodontitis exposed to PM and atmospheric factors in South Korea using deep learning. Methods: This study is a retrospective cohort study utilizing secondary data from the Korean Statistical Information Service and the Health Insurance Review and Assessment database for air pollution and the number of patients with periodontitis, respectively. Data from 2015 to 2022 were collected and consolidated every month, organized by region. Following data matching and management, the deep neural networks (DNN) model was applied, and the mean absolute percentage error (MAPE) value was calculated to ensure the accuracy of the model. Results: As we evaluated the DNN model with MAPE, the multivariate model of air pollution including exposure to PM_2.5, PM₁₀, and other atmospheric factors predict approximately 85% of the number of patients with periodontitis. The MAPE value ranged from 12.85 to 17.10 (mean±standard deviation=14.12±1.30), indicating a commendable level of accuracy. Conclusion: In this study, the predictive model for the number of patients with periodontitis is developed based on air pollution, including exposure to PM_2.5, PM₁₀, and other atmospheric factors. Additionally, various relevant factors are incorporated into the developed predictive model to elucidate specific causal relationships. It is anticipated that future research will lead to the development of a more accurate model for predicting the number of patients with periodontitis.

• Journal of The Korean Association For Science Education
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• v.37 no.1
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• pp.25-37
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• 2017

The purpose of this study is to analyze the effects on particulate concept formation based on abductive reasoning model for elementary science class. For this study, an author selected two groups in the sixth grade. One group is an ordinary textbook-based control group (N=26) and the other group is an abductive reasoning model-based treatment group (N=26). After twelve lessons, the scores of Concepts Test for Gas were analyzed by t-test and two-way ANOVA. The result of t-test showed both the control and treatment groups have higher score than before they take the lesson. But after the lesson, an author found out that the treatment group had higher score than that of the control group. And compared to the number of particles expressed, the number of the treatment group were higher than that of the control class. The two-way ANOVA result revealed that the interaction effect between their cognitive level and treatment was not significant. And regardless of the level of cognition, the scores of treatment group are higher than those of control group. Therefore, abductive reasoning model-based elementary science class were found to be more effective for particulate concept formation. Based on the results, an author concluded that abductive reasoning model is very effective in teaching particulate concepts to elementary students.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.6
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• pp.815-826
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• 2013

Using a developed mathematical model and calibrated kinetic constants, numerical experiments for a aerobic digestion of wastewater sludge by SBR aerobic digestion process combined with ultrasonication (USSBR) were performed in this study. It simulated well the phenomena of the decomposition of particulate organics and the release of organic nitrogen and transformation. To achieve 40 % of particulate organics removal, USSBR process requires only 6 days of SRT and 14 W/L of ultrasonic power whereas SBR aerobic digestion process requires 12 days of SRT. Based on the model simulation results, an empirical equation was presented here. This equation will be used to predict digestion efficiency for the given variables of SRT and ultrasonic power dose. USSBR aerobic digestion process can reduce the nitrogen concentration. The optimal operation strategy for the simultaneous removal of solids and soluble nitrogen in this process is estimated to 7 days of SRT with 14 W/L of ultrasonic power dose while anoxic period was 6 hours out of 24 hours of cycle time. In this condition, 40 % of particulate organics as well as 36 % of total nitrogen will be removed and the soluble nitrogen concentration of the centrate will be lower less then 40 mg/L.

• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.1
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• pp.78-88
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• 2007

In the present study a detailed examination of various porous media models for predicting filtration efficiency and pressure drop of diesel particulate filter (DPF), such as sphere-in-cell and constricted tube models, are attempted. In order for demonstrating their validities of correct estimation on permeability, geometry of property configurations common in commercial cordierite DPFs are correlated to the porous media flow models, and validations of predicted filtration efficiencies due to the use of different unit collectors are made with experiments. The result shows that the porosity, pore size and permeability of cordierite DPF can be successfully correlated by Kuwabara flow field with correction factor of 0.6. The unit collector efficiency predicted by sphere-in-cell model agrees very well with measurements in accumulation mode, whereas that by constricted tube model with significant prediction error.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1208-1215
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• 2017

Recently, people's attention and worries about fine particulate matter have been increasing. Due to the construction and maintenance costs, there are insufficient air quality monitoring stations. As a result, people have limited information about the concentration of fine particulate matter, depending on the location. Studies have been undertaken to estimate the fine particle concentrations in areas without a measurement station. Yet there are limitations in that the estimate cannot take account of other factors that affect the concentration of fine particle. In order to solve these problems, we propose a framework for estimating the concentration of fine particulate matter of a specific area using meteorological data and traffic data. Since there are more grids without a monitor station than grids with a monitor station, we used a domain adversarial neural network based on the domain adaptation method. The features extracted from meteorological data and traffic data are learned in the network, and the air quality index of the corresponding area is then predicted by the generated model. Experimental results demonstrate that the proposed method performs better as the number of source data increases than the method using conditional random fields.

• 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 Society for Atmospheric Environment
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• v.19 no.6
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• pp.789-803
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• 2003

The purpose of this study is to compare the usefulness between Gaussian dispersion model and receptor model with the experimental result of the dispersion tracing of the particulate pollutants from Taean coal-fired power plants. For this purpose, the component analysis of the collected PM 10 samples was performed. In order to trace the pollution sources, factor analysis was done with the result of the component analysis. As a result of the correlativity analysis of the fifteen power plants' profiles offered by US EPA, the correlativity of No.11202 source profile showed highest rate up to 84.5%. Thus it was adopted as proper one and the contribution rate by each pollution source was calculated by Chemical Mass Balance (CMB)-8 model. The contribution rate, which was the effect rate of the power plants on each measuring point, were calculated with a range of 24∼52％ and the standard error was below 0.9 $\mu\textrm{g}$/㎥. This indicates the selection of the source profile was appropriate. Also, the concentrations of each point were calculated by the ISCST3 which is suggested by US EPA as one of the regulatory Gaussian dispersion model. The calculation result showed that the predicted concentration was 50∼58 $\mu\textrm{g}$/㎥, comparing with the measured result of 9∼65 $\mu\textrm{g}$/㎥. It was found that the concentration calculated by ISCST3 was underpredicted. It was thought that the receptor model was more favorable than the Gaussian dispersion model in estimating the effect of the particulate matter on a certain receptive point.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.32 no.1
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• pp.27-35
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• 2006

Purpose: This study was performed to investigate the expression of the transforming growth factor (TGF)-1, in a rat calvarium defect model using particulate dentin and/or plaster of Paris, and correlate the bone regeneration process with the histologic events. Materials and Methods: Thirty-two Sprague-Dawley rats were divided into 4 groups of 8 animals each. A 1.0 cm-sized calvarial defects were made and the defect was filled with different graft materials as follows : Group A, the defects were filled with a mixture of particulate dentin and plaster of Paris with a 2:1 ratio; Group B, the defects were filled with plaster of Paris only; Group C, defects were filled with particulate dentin only; Group D, untreated control group. The animals were sacrificed by 1, 2, 4, 8 weeks after implantation. Excised wound tissues were processed for histology, immunohistochemistry and RT-PCR for the analysis of TGF-1 expression. Results: Gene expression of TGF-1 was detected for all experimental groups. The highest gene expression was observed in the specimen taken at the first week after implantation in Group A. According to the histologic and immunohistochemical studies, TGF-1 positive osteoblast-like cells were found in the early stage of healing after the implantation of particulate dentin and plaster of Paris. Conclusion: These findings suggest that TGF-1 may be related to new bone formation at the early healing process after the implantation of particulate dentin and plaster of Paris.

• Journal of the korean society of oceanography
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• v.39 no.4
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• pp.222-233
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• 2004

Using nitrogen as the limiting nutrient, the default version of a microplankton-detritus model linked chlorophyll concentration to the autotroph nitrogen. However, phosphorus dynamics were added to simulate the results of a microcosm experiment. Using standard parameter values with a single value of microheterotroph fraction in the microplankton taken from the observed range, the best simulation successfully captured the main features of the time-courses of chlorophyll and particulate organic carbon, nitrogen and phosphorus, with root-mean-square error equivalent to 29% of particulate concentration. A standard version of microbiological model assumes complete internal cycling of nutrient elements; adding a term for ammonium and phosphate excretion by microheterotrophs did not significantly improve predictions. Relaxing the requirement for constant microheterotroph fraction resulted in an autotroph-heterotroph model AH, with dynamics resembling those of a Lotka-Volterra predator-prey system. AH fitted the microcosm data worse than did MP, justifying the suppression of Lotka-Volterra dynamics in MP. The paper concludes with a discussion of possible reasons for the success of the simple bulk dynamics of MP in simulating microplankton behaviour.