• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2876-2882
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• 2015

The object of this study is to identify changes in air pollution in the maximum ground level concentration and the surrounding area when air pollution control facilities are improved in the thermal power plants. The effects of improved facilities are analyzed by comparing air quality after applying improved air pollution control facilities. For prediction of air quality, the change of wind field can be represented with movement of Puff and CALPUFF Model, air pollution diffusion models which can implement abnormal conditions. Major air pollutants of thermal power plants such as $SO_2$, $NO_2$, and $PM_{10}$ are selected as prediction items. That results show that improvement of air pollution control facilities is significantly effective in reduction of air pollution of $SO_2$ and $NO_2$ in the maximum ground level concentration and areas around of thermal power plants. In the case of $PM_{10}$, it is found that the effect of reduction in pollution is high in the maximum ground level concentration, but the effect of reduction in air pollution is somewhat low in the area around of the thermal power plant.

• Journal of Preventive Medicine and Public Health
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• v.32 no.1
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• pp.60-64
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• 1999

Objectives. Changes in lung function are frequently used as biological markers to assess the health effects of criteria air pollutants. We tried to formulate the prediction models of pulmonary functions based on height, weight, age and gender, especially for children aged 12 years who are commonly selected for the study of health effects of the air pollution. Methods. The target pulmonary function parameters were forced vital capacity(FVC) and forced expiratory volume in one second(FEV1). Two hundreds and fifity-eight male and 301 female 12-year old children were included in the analysis after excluding unsatisfactory tests to the criteria recommended by American Thoracic Sosiety and excluding more or less than 20% predicted value by previous prediction equations. The weight prediction equation using height as a independent variable was calculated, and then the difference of observed weight and predicted weight (i.e. residual) was used as the independent variable of pulmonary function prediction equations with height. Results. The prediction equations of FVC and FEV1 for male are FVC(ml) = $50.84{\times}height(cm)+7.06{\times}weight$ residual 4838.86, FEV1(ml) = $43.57{\times}height(cm)+3.16{\times}weight$ residual - 4156.66, respectively. The prediction equations of FVC and FEV1 for female are FVC(ml) = $42.57{\times}height(cm)+12.50{\times}weight$ residual - 3862.39, FEV1(ml) = $36.29{\times}height(cm)+7.74{\times}weight$ residual - 3200.94, respectively.

• Journal of Industrial Convergence
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• v.21 no.7
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• pp.19-27
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• 2023

As the secondary battery market expands, the process of producing laterite ore using the rotary kiln and electric furnace method is expanding worldwide. As ESG management expands, the management of air pollutants such as nitrogen oxides in exhaust gases is strengthened. The rotary kiln, one of the main facilities of the pyrometallurgy process, is a facility for drying and preliminary reduction of ore, and it generate nitrogen oxides, thus prediction of nitrogen oxide is important. In this study, LSTM for regression prediction and LightGBM for classification prediction were used to predict and then model optimization was performed using AutoML. When applying LSTM, the predicted value after 5 minutes was 0.86, MAE 5.13ppm, and after 40 minutes, the predicted value was 0.38 and MAE 10.84ppm. As a result of applying LightGBM for classification prediction, the test accuracy rose from 0.75 after 5 minutes to 0.61 after 40 minutes, to a level that can be used for actual operation, and as a result of model optimization through AutoML, the accuracy of the prediction after 5 minutes improved from 0.75 to 0.80 and from 0.61 to 0.70. Through this study, nitrogen oxide prediction values can be applied to actual operations to contribute to compliance with air pollutant emission regulations and ESG management.

• Journal of Environmental Health Sciences
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• v.34 no.3
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• pp.219-225
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• 2008

Many building materials may contain high concentrations of volatile organic compounds (VOCs) and other hazardous pollutants(HAPs). Specifically, VOCs discharged by indoor building material may cause "new house" syndrome, atopic dermatitis etc. The diffusion coefficient and initially contained total VOC quantity were determined using microbalance experiments and small chamber tests. Interactions between volatile organic compounds (VOCs) and vinyl flooring (VF), a relatively homogenous, diffusion-controlled building material, were characterized. Rapid determination of the material/air partition coefficient (K) and the material-phase diffusion coefficient (D) for each VOC was achieved by placing thin VF slabs in a dynamic microbalance and subjecting them to controlled sorption/desorption cycles. K and D are shown to be independent of concentration for all of the VOCs and water vapor. This approach can be applied to other diffusion-controlled materials and should facilitate the prediction of their source/sink behavior using physically-based models.

• 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.

• Particle and aerosol research
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• v.5 no.2
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• pp.37-43
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• 2009

Particulate matter (PM) is one of the major indoor air pollutants especially in the subway station in Korea. In order to remove PM in the subway station, several kinds of PM removal system such as roll-filter, auto-washable air filter, demister, and electrostatic precipitator are used in the air handling unit (AHU) of subway stations. However, those systems are prone to operation and maintenance problems since the filter-regeneration unit consisting of electrical or water jet parts might malfunction due to the high load of particulates unless the filter medium is periodically replaced. In this study, the use of axial-flow cyclone was proposed for particulate filter unit in the AHU for its low operation and maintenance cost. Novel shape of axial-flow cyclone was designed by using computational fluid dynamics (CFD). The shape of vortex vane was optimized in terms of pressure drop and tangential velocity. In addition, CFD analysis was validated experimentally through the pressure drop measurement of mock-up model. We found that pressure drop and tangential velocity of fluid through the axia-flow cyclone was significantly affected by the rotating degree of vortex vane and the numerical prediction of pressure drop agreed well with experimental measurement.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1191-1205
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• 2022

Particulate matter(PM) among air pollutants with complex and widespread causes is classified according to particle size. Among them, PM_2.5 is very small in size and can cause diseases in the human respiratory tract or cardiovascular system if inhaled by humans. In order to prepare for these risks, state-centered management and preventable monitoring and forecasting are important. This study tried to predict PM_2.5 in Seoul, where high concentrations of fine dust occur frequently, using two ensemble models, random forest (RF) and extreme gradient boosting (XGB) using 15 local data assimilation and prediction system (LDAPS) weather-related factors, aerosol optical depth (AOD) and 4 chemical factors as independent variables. Performance evaluation and factor importance evaluation of the two models used for prediction were performed, and seasonal model analysis was also performed. As a result of prediction accuracy, RF showed high prediction accuracy of R² = 0.85 and XGB R² = 0.91, and it was confirmed that XGB was a more suitable model for PM_2.5 prediction than RF. As a result of the seasonal model analysis, it can be said that the prediction performance was good compared to the observed values with high concentrations in spring. In this study, PM_2.5 of Seoul was predicted using various factors, and an ensemble-based PM_2.5 prediction model showing good performance was constructed.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.1
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• pp.1-15
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• 2008

Chemical Accident Response Information System (CARIS) which has been designed for the efficient emergency response of chemical accidents produces the real-time atmospheric fields through the Regional Atmospheric Modeling System, RAMS. The previous studies were emphasized that improving an initial input data had more effective results in developing prediction ability of atmospheric model. In a continuous effort to improve an initial input data, we replaced the land-use dataset using in the RAMS, which is a high resolution USGS digital data constructed in April, 1993, with the latest land-use data of the Korea Ministry of Environment over the South Korea and simulated atmospheric fields for developing a real-time prediction in dispersion of chemicals. The results showed that the new land-use data was written in a standard RAMS format and shown the modified surface characteristics and the landscape heterogeneity resulting from land-use change. In the results of sensitivity experiment we got the improved atmospheric fields and assured that it will give more reliable real-time atmospheric fields to all users of CARIS for the dispersion forecast in associated with hazardous chemical releases as well as general air pollutants.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.139-146
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• 2009

While air pollutants emission caused by the traffic is one of the major sources, few researches have done. This study investigated the extent to which traffic and road related characteristics such as traffic volumes, speeds and road weather data including wind speed, temperature and humidity, as well as the road geometry affect the air pollutant emission. We collected the real time air pollutant emission data from Seoul automatic stations and real time traffic volume counts as well as the road geometry. The regression air pollutant emission models were estimated. The results show followings. First, the more traffic volume increase, the more pollutant emission increase. The more vehicle speed increase, the more measurement quantity of pollutant decrease. Secondly, as the wind speed, temperature, and humidity increase, the amount of air pollutant is likely to decrease. Thirdly, the figure of intersections affects air pollutant emission. To verify the estimated models, we compared the estimates of the air pollutant emission with the real emission data. The result show the estimated results of Chunggae 4 station has the most reliable data compared with the others. This study is differentiated in the way the model used the real time air pollutant emission data and real time traffic data as well as the road geometry to explain the effects of the traffic and road characteristics on air quality.

• Journal of Environmental Impact Assessment
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• v.29 no.5
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• pp.391-402
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• 2020

Health Impact Assessment based on municipal law is performed and written in the sanitary and public health part in the current environmental impact assessment. Residential development projects such as housing site development etc., are not subject to health impact assessment under Article 13 of the Environmental Health Act. However, health impact assessment is conducted partially based on the review that health impact assessment targets which are identified among substances emitted from pollutants nearby industrial complexes should be assessed risk (including carcinogenic and non-carcinogenic) at the stage of the environmental impact assessment consultation. Although residential development projects do not have plans for pollutant emitting facilities that emit hazardous air pollutants, there is a possibility that residents might be affected by pollutants from industrial complex near residential area in the future. In this study, Health impact assessment was conducted to examine the impact on residents in planned areas by analyzing previous residential development projects. We predicted future impact by using the literature survey results on surrounding area (case1) and conducting contribution analysis (case2) and predicting exposure concentration of carcinogenic substances applying Atmospheric Diffusion Model (AERMOD). By this study, we concluded that applying on-site survey, contribution analysis and prediction of exposure concentration by using AERMOD complementarily will be effective to assess the health impact to the receptors by pollutants from industrial complexes near the planned zone.