• Clean Technology
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• v.29 no.4
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• pp.279-287
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• 2023

Fine dust emitted from coal combustion is classified into filterable particulate matter (FPM) and condensable particulate matter (CPM). CPM is difficult to control with existing air pollution control devices, so research is being conducted to understand the characteristics of CPM. Components constituting condensable particulate matter (CPM) are divided into inorganic and organic components. There are many quantitative analysis results for the ionic components, which account for a significant proportion of the CPM inorganic components, but little is known about the organic components. Thus, there is a need for a quantitative analysis of CPM organic components. In this study, aromatic hydrocarbons (toluene, ethyl benzene, m,p-xylene, and o-xylene) and n-alkanes with 10 to 30 carbon atoms were quantitatively analyzed to understand the organic components of CPM emitted from a lab-scale coal combustor. Of the aromatic hydrocarbons, toluene accounted for 1.03% of the CPM organic components. On the other hand, the contents of ethyl benzene, m,p-xylene, and o-xylene showed low values of 0.11%, 0.18%, and 0.51% on average, respectively. Among the n-alkanes, triacontane (C₃₀) showed a high content of 2.64% and decane (C₁₀) showed a content of 2.05%. The next highest contents were shown with dodecane (C₁₂), tetradecane (C₁₄), and heptacosane (C₂₇), all of which were higher than that of toluene. The n-alkane substances that had detectable concentrations showed higher contents than ethyl benzene, m,p-xylene, and o-xylene except for tetracosane (C₂₄).

• Journal of Distribution Science
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• v.17 no.7
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• pp.65-76
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• 2019

Purpose - The purpose of this study is to investigate economical ventilation effectiveness to reduce hazardous materials exposure and damage of workers by analyzing exposure amount of noxious substances under various ventilation conditions of nail salon for indoor environments. Research design, data, and methodology - This study was carried out with cooperation of Nail shop located in SeongNam city to involve an analysis of the environmental impact indoor air quality, pollutant exposure and economical cost-effectiveness in the nail workplace. The hazardous substances were PM-10(Particulate Matter-10㎛), VOCs(Volatile Organic Compounds) and Formaldehyde, which are the major materials of nail workplace. Results - PM-10 is reduced by about 60% with air cleaner, forced artificial ventilation by 32%, and natural ventilation by about 12%. TVOCs and Formaldehyde showed similar efficiency (80~100%) after natural ventilation and ventilation after 60 minutes. The removal efficiencies of VOCs and formaldehyde were similar to those of natural ventilation and mechanical ventilation system. However, in case of dust, natural ventilation was reduced by artificial ventilation system due to inflow of external dust during natural ventilation. Conclusions - If the pollution degree of outdoor air is not high, air volume is high, and natural ventilation is performed when the air conditioning and heating system is not operated. Even at the end of the work, it keeps operating for 60 minutes to remove the pollutants generated. Results of this analysis demonstrated that the worker environment can be improved by adopting institutional legislation and guidelines for ventilation.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.573-586
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• 2020

PM (particulate matter) is of interest to everyone because it can have adverse effects on human health by the infiltration from respiratory to internal organs. To date, many studies have made efforts for the prediction of PM10 and PM2.5 concentrations. Unlike previous studies, we conducted the prediction of tomorrow's PM10 concentration for the Air Korea stations using Chinese PM10 data in addition to the satellite AOD and weather variables. We constructed 230,639 matchups from the raw data over 3 million and built an RF (random forest) model from the matchups to cope with the complexity and nonlinearity. The validation statistics from the blind test showed excellent accuracy with the RMSE (root mean square error) of 9.905 ㎍/㎥ and the CC (correlation coefficient) of 0.918. Moreover, our prediction model showed a stable performance without the dependency on seasons or the degree of PM10 concentration. However, part of coastal areas had a relatively low accuracy, which implies that a dedicated model for coastal areas will be necessary. Additional input variables such as wind direction, precipitation, and air stability should also be incorporated into the prediction model as future work.

• Particle and aerosol research
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• v.17 no.4
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• pp.115-123
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• 2021

The emission gas from meat grilled restaurants has unpleasant odor and sticky particulate matters (PM) in form of oil mist. This affects the residents living nearby. In oder to decrease the odor and PM, electrostatic precipitators (ESP) were installed at several restaurants by support of Seoul metropolitan government and emission characteristics of PM and odor were investigated. The removal efficiency of odor and PM at before- and after-ESP was that total hydrocarbon (THC), NH₃, H₂S, acetaldehyde, butyraldehyde were not much decreased. Total suspended particles (TSP) emitted from the vent was below the value of 7 mg/m³ regardless of the concentration of before-ESP and removal efficiency was in the range of 64 ~ 86%. The ratio of PM_1.0 to PM₁₀ of emission gas was over 0.9. So it was revealed that most PM of emission gas was PM1.0. The PM concentration in the air of the street near the vent was higher than another street due to the emission gas and the PM of indoor air of restaurant grilling meats was measured very high compared to outdoor air. So it needs more powerful ventilation of indoor air.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.34 no.2
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• pp.115-124
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• 2024

Objectives: The aim of this study is to assess indoor air quality within and around buildings and evaluate the health risks associated with exposure to indoor air pollution. The study compares IAQ standards established by the World Health Organization with those set by South Korea's Ministry of Environment and Ministry of Education. Methods: The study utilized an Anderson Sampler and DustTrak^TM II to collect samples of total airborne bacteria and PM in indoor and outdoor environments. Collected samples were analyzed using biological and biochemical methods. Statistical analysis was conducted using SPSS to examine the correlation between airborne bacteria and PM. Results: The study revealed that the concentration of total airborne bacteria in indoor air generally remained below the Ministry of Environment's standard of 800 CFU/m³, although it surpassed this threshold in certain instances. PM concentrations did not exceed the standards. Indoor fine dust concentration was higher when there were people (P<0.05). There was no difference in total floating bacterial concentrations between indoor and outdoor environments (P=0.184). Finally, there was a correlation between fine dust and airborne bacteria concentrations. Conclusion: The study evaluated the concentrations of total airborne bacteria and PM in indoor air, emphasizing the importance of managing IAQ. Further research in various environments is essential to ensure a healthy indoor environment. The findings underscore the need for ongoing research and management to enhance IAQ and create safer and healthier living environments.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1293-1300
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• 2014

The temperature data influences on various policies of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly and seasonal temperature data at the northern part of the Chungcheong Namdo, Seosan monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). The result showed that the monthly ARE model explained about 39-63% for describing the temperature. However, the ARE model will be expected better when we add the more explanatory variables in the model.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.

• Korean Journal of Environmental Agriculture
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• v.23 no.2
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• pp.99-103
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• 2004

This study was carried out to determine impacts of burning of barley straw produced from rice-barley double cropping paddy field on air quality by investigating emissions of greenhouse gases ($CO_2$, $CH_4$ and $N_2O$), air pollution gases (CO, $SO_2$, $H_2S$, $NH_3$ and NO) and particulate matters (PM 10 and PM 2.5). When the barley straw at a rate of 4.5 t/ha was burned at open status, the emitted GHGs amounts were $CO_2$ 376.8 kg/l0a, $CH_4$ 1.56 and $N_2O$ 0.06. The amount of CO emission was the largest among air pollution gases. These results showed that the range of $45{\sim}55%$ of total C in barley straw was emitted as $CO_2-C$, followed by CO-C ($6.4{\sim}5.9%$) and $CH_4-C$ ($0.5{\sim}0.7%$). As far as moisture content in barley straw is concerned, the higher moisture content that the barley straw contains, the larger amount of air pollution gases and the higher portion of PM 2.5 in PM 10 were emitted when it burned. In case of harvesting time of barley straw, emission amounts of greenhouse, air pollution gases and PM 2.5 portion in PM 10 had tendency to increase when earlier harvested barley straw was burned.

• Asian Journal of Atmospheric Environment
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• v.9 no.4
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• pp.237-246
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• 2015

We conducted intensive observations of ozone, CO, $NO_x$ (=NO and $NO_2$), $NO_y$ (total odd nitrogen species including particulate nitrate) and total nitrate (the sum of gaseous $HNO_3$ and particulate nitrate) at Cape Hedo, Okinawa, Japan, from 19 March to 3 April, 2009, to investigate ozone production during long-range transport from the Asian continent. Ozone production efficiency (OPE) was used to evaluate photochemical ozone production. OPE is defined as the number of molecules of ozone produced photochemically during the lifetime of a $NO_x$ molecule. OPE is calculated by the ratio of the concentration increase of ozone to that of $NO_z$ ($=NO_y-NO_x$). Average OPE during observation was estimated to be $12.6{\pm}0.5$, but concentrations of ozone increased nonlinearly with those of $NO_z$. This non-linearity suggests that OPE depends on air mass origin and $NO_z$ concentrations. There were very different values of OPE for the same air mass origin, so that only the air mass origin alone does not control OPE. OPE was low when $NO_z$ concentration was high. We examined the correlation between $NO_z$ and $CO/NO_y$ ratios, which we used instead of the ratio of non-methane hydrocarbons (NMHCs) to $NO_x$. The $CO/NO_y$ ratios decreased with increasing $NO_z$ concentrations. These results indicate that competition reactions of OH with NMHCs and $NO_2$ are the rate determining steps of photochemical ozone production during long-range transport from the Asian continent to Cape Hedo, for high concentrations of nitrogen oxides.

• Environmental Analysis Health and Toxicology
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• v.14 no.1_2
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• pp.21-33
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• 1999

A large number of studies have indicated associations between the impairment of respiratory health and exposure to ambient air pollutants such as ozone (O$_3$) , nitrogen dioxide (NO$_2$) , sulfur dioxide(SO$_2$) , particulate matters (PM$\_$10/). To evaluate this associations, we used the pulmonary function tests (peak expiratory flow rate : PEFR) by mini-wright peak flow meter and counting neutrophils in the nasal lavage (NL) as biomarker. From 15 June to 16 July 1998, for the workers in the highway tollgates, PEFR and NL were measured three times daily and twice per week. and association between the level of air pollutants and PEFR and NL were analyzed using the multiple regression model and the poisson regression model respectively. The results indicated that the effects of all measured air pollutants (SO$_2$, NO$_2$, O$_3$, PM$\_$10/) were not significantly associated with the value of PEFR. On the other side, SO$_2$, NO$_2$, PM$\_$10/ were significantly associated with the number of neutrophils in NL. The increase in SO$_2$, NO$_2$of 10ppb and in PM$\_$10/ of 10$\mu\textrm{g}$/m$^3$was associated with 24%, 21%, 35% increases in neutrophil counts. But the ozone exposure was not associated with NL.