• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.16 no.2
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• pp.101-109
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• 2006

The objective of this study is to provide fundamental data for pertinent management of indoor air quality through investigating the size-based characteristics of bioaerosol distributed in the general hospital. Measurement sites are main lobby, ICU, ward and laboratory and total five times were sampled with six-stage cascade impactor. Based on the result of this study, concentrations of airborne bacteria and fungi were the highest in main lobby as followed by an order of ward, ICU and laboratory. Concentrations of airborne bacteria was generally higher than those of airborne fungi and the ratio of indoor and outdoor concentration of both exceeded 1.0 in all the measurement sites of the general hospital. The predominant genera of airborne bacteria identified in the general hospital were Staphylococcus spp.(50%), Micrococcus spp.(15-20%), Corynebacterium spp.(5-20%), and Bacillus spp.(5-15%). On the other hand, the predominant genera of airborne fungi identified in the general hospital were Cladosporium spp.(30%), Penicillium spp.(20-25%), Aspergillus spp.(15-20%), and Alternaria spp.(10-20%). In regard to size distribution of bioaerosol, the detection rate was generally highest on 5 stage($1.1-2.1{\mu}m$) for airborne bacteria and on 1 stage(>$7.0{\mu}m$) for airborne fungi. Cleanliness of facilities in the general hospital and condition of HVAC system should be monitored regularly to prevent indoor air contamination by airborne microorganisms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3307-3314
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• 2012

The annual energy demand of the standard rural house models was analyzed using the DesignBuilder. Indoor temperature set-point, U-value of outer wall, type of window, and degree of ventilation were selected as simulation parameters. In all the simulation cases, heating energy demand was higher than cooling energy demand regardless of the building size. When the lower U-value of the outer wall was applied to account for the thicker insulation layer, heating energy demand was decreased while cooling energy demand was increased. However, it is better to reduce the area of outer wall which is directly exposed to outdoor air because reducing the U-value of the outer wall is not effective in decreasing heating energy demand. Among the four different window types, the double skin window is most favorable because heating energy demand is the lowest. For a fixed infiltration rate, higher ventilation rate resulted in an increased heating energy demand and had minor impact on cooling energy demand. As long as the indoor air quality is acceptable, lower ventilation rate is favorable to reduce the annual energy demand.

• Particle and aerosol research
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• v.18 no.4
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• pp.137-145
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• 2022

As many studies on the harmfulness of fine dust have been reported, awareness of its seriousness is spreading. Recently, interest in indoor air quality as well as air pollution is increasing, and research on measures to block fine dust flowing into the room from the outside is being conducted. The clothing company is launching functional clothing to prevent fine dust attached to clothing from entering the room through outdoor activities. However, it is difficult to confirm whether there is actually fine-dust blocking performance, and there is no evaluation standard. In this study, the contamination rate caused by fine dust was quantitatively compared through image processing after contamination of the outer fabric for 4 types of commercially available functional clothing with fine-dust blocking effect. The difference in particle contamination according to the material of the outer fabric was analyzed by comparing the surface resistance, and it was found that the higher the surface resistance of the outer fabric material, the more fine dust was attached. The analysis method of this study is expected to be able to quantitatively compare and evaluate the fine-dust blocking performance of functional clothing.

• The Journal of Bigdata
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• v.6 no.2
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• pp.39-49
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• 2021

Recently, large and diverse weather data are being collected by sensors from various sources. Efforts to predict the concentration of fine dust through machine learning are being made everywhere, and this study intends to compare PM10 and PM2.5 prediction models using data from 840 outdoor air meters installed throughout the city. Information can be provided in real time by predicting the concentration of fine dust after 5 minutes, and can be the basis for model development after 10 minutes, 30 minutes, and 1 hour. Data preprocessing was performed, such as noise removal and missing value replacement, and a derived variable that considers temporal and spatial variables was created. The parameters of the model were selected through the response surface method. XGBoost, Random Forest, and Deep Learning (Multilayer Perceptron) are used as predictive models to check the difference between fine dust concentration and predicted values, and to compare the performance between models.

• Journal of Environmental Health Sciences
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• v.44 no.2
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• pp.143-152
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• 2018

Objectives: The purpose of this study was to provide basic data for air pollutant exposure modelling and understanding the contribution of respective microenvironments by assessing the time-activity patterns of Korean students according to variables such as grade, sex, weekday, and weekend. Methods: In this study, we compared the residential time of 521 (both weekday and weekend) lower elementary students, 1,735 (1,054 on weekdays, 681 on weekends) upper elementary students, 2,210 (1,294 on weekdays, 916 on weekends) middle school students, and 2,366 (1,387 on weekdays, 979 on weekends) high school students in different microenvironments according to grade, sex, weekday, and weekend. We used data from the 2014 Time-Use Survey by the Korean National Statistical Office for upper elementary students through high school students, and surveyed time-activity patterns of 521 lower elementary students aged 7-9 years. Each microenvironment was divided into indoor, outdoor, and transport. Indoor environments were divided into home, school, and other places. In addition, the results of previous studies were compared to this study. Results: Weekday time-activity patterns of Korean students indicated that lower elementary students spent $16.02{\pm}2.53hr$ in the home and $5.37{\pm}2.32hr$ in school. Upper elementary students spent $14.11{\pm}1.79hr$ in the home and $6.27{\pm}1.37hr$ in school. Middle school students spent $12.83{\pm}2.22hr$ in the home and $7.48{\pm}1.88hr$ in school. High school students spent $10.65{\pm}2.86hr$ in the home and $10.23{\pm}2.86hr$ in school on weekdays. High school students spent the least amount of time in the home and the most time in school compared to other grades Conclusions: Students spent most of their time indoors, including in the home, school, and other indoors. On weekdays, as the grade increases, home residential times were decreased and school residential times were increased. Differences in time-activity patterns according to sex were not found for either weekdays or weekends. It is estimated that Korean students could be affected by school indoor air quality. High school students could be most affected by school indoor air quality since they spent the most time at school.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.1
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• pp.74-85
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• 2013

Since the underground transportation system is a closed environment, indoor air quality problems may seriously affect many passengers' health. The purpose of this study was to understand $PM_{10}$ characteristics in the underground air environment and further to quantitatively estimate $PM_{10}$ source contributions in a Seoul Metropolitan subway station. The $PM_{10}$ was intensively collected on various filters with $PM_{10}$ aerosol samplers to obtain sufficient samples for its chemical analysis. Sampling was carried out in the M station on the Line-4 from April 21 to 28, July 13 to 21, and October 11 to 19 in the year of 2010 and January 11 to 17 in the year of 2011. The aerosol filter samples were then analyzed for metals, water soluble ions, and carbon components. The 29 chemical species (OC1, OC2, OC3, OC4, CC, PC, EC, Ag, Al, Ba, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Si, Ti, V, Zn, $Cl^-$, $NO_3{^-}$, $SO_4{^{2-}}$, $Na^+$, $NH_4{^+}$, $K^+$, $Mg^{2+}$, $Ca^{2+}$) were analyzed by using ICP-AES, IC, and TOR after proper pretreatments of each sample filter. Based on the chemical information, positive matrix factorization (PMF) model was applied to identify the $PM_{10}$ sources and then six sources such as biomass burning, outdoor, vehicle, soil and road dust, secondary aerosol, ferrous, and brakewear related source were classified. The contributions rate of their sources in tunnel are 4.0%, 5.8%, 1.6%, 17.9%, 13.8% and 56.9% in order.

• Journal of Environmental Science International
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• v.24 no.12
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• pp.1559-1567
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• 2015

This study investigated the association between $PM_{2.5}$ concentrations obtained with portable real-time monitors and those obtained with gravimetric methods in national urban air-quality monitoring sites in Seoul, South Korea. We used the SidePak AM510 Personal Aerosol Monitor (TSI Inc., 500 Cardigan Road Shoreview, MN) and DustTrak DRX 8533 (TSI Inc., 500 Cardigan Road Shoreview, MN) as portable real-time monitors for measuring $PM_{2.5}$ concentrations and compared these values with those measured with the PMS-103 or SEQ 47/50 models operated by Federal Reference Method (FRM) or the European Committee for Standardization(ECS), respectively, in national urban air-quality monitoring sites in Seoul. Measurements were conducted every other day in the winter and spring seasons of 2014. The estimated daily mean concentrations of $PM_{2.5}$ ranged between 13.4 and $161.9{\mu}g/m^3$ using AM 510 and between 22.0 and $156.0{\mu}g/m^3$ using DustTrak. The Spearman correlation coefficient for $PM_{2.5}$ concentrations between AM 510 and gravimetric results was 0.99, and the correlation between DustTrak and gravimetric results was 0.87. The correction factor suggested was 0.42 and 0.29 for AM 510 and DustTrak, respectively. We found that $PM_{2.5}$ concentrations measured with real-time monitors could overestimate true $PM_{2.5}$ concentrations and therefore the application of a correction factor (0.43) is strongly suggested for quantification when Real-time monitors were operated of $PM_{2.5}$ levels at urban atmospheric environment of South Korea.

• Journal of the Korea Organic Resources Recycling Association
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• v.30 no.4
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• pp.41-50
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• 2022

Recently, research on carbon adsorbents has been active as an interest in improving the environment such as indoor and outdoor air quality. Considering that causative substances deteriorate the air quality are basically volatile organic compounds, it is important to improve the hydrophobicity of the carbon materials for better removal efficiency. This study presents a method for improving hydrophobicity of carbon and a measurement of the hydrophobicity. Generally, methods of improving the hydrophobicity of carbon materials are heat treatment, acid/alkali treatment, coating and immersion with hydrophobic materials. However, it collapses the pore structure and reduces the adsorption capacity. Therefore, this study briefly introduce not only the general method for improving carbon materials' hydrophobicity but also the method for converting the precursor of the material is briefly introduced. Futhermore, this study introduces a analytical technique used to determine hydrophobic modification or not, and aims to enhance the understanding of carbon materials.

• Journal of Environmental Health Sciences
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• v.44 no.3
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• pp.267-274
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• 2018

Objectives: This study examined the impact of a smoke-free policy on indoor air quality at indoor recreation facilities by assessing $PM_{2.5}$ concentrations before and after the implementation of the new policy. Methods: Using real-time monitors, $PM_{2.5}$ concentrations were measured in 50 billiard rooms and 50 golf simulator rooms in Seoul, Korea. The characteristics of the indoor recreation facilities, smoking status, and atmospheric conditions were recorded at the same time.After the enforcement of a smoke-free policy, $PM_{2.5}$ concentrations, installation of smoking room, and smoking status were examined when the facilities were revisited. Results: Almost a half of the billiard rooms and over 80% of golf simulator rooms were located underground. Seventy percent of the billiard rooms and one hundred percent of the golf simulator rooms were equipped with a local exhaust ventilation system. After the implementation of the smoke-free policy, 46% of the billiard rooms and 20% of the golf simulator rooms newly installed a smoking room. In the billiard rooms with a newly-installed smoking room, the $PM_{2.5}$ concentrations decreased from 97.9 to $45.6{\mu}g/m^3$ after the implementation of the smoke-free policy. The same change of 29.0 to $ 26.3{\mu}g/m^3$ was not statistically significant in golf simulator rooms. Indoor $PM_{2.5}$ concentrations were correlated with outdoor $PM_{2.5}$ concentrations, number of smokers, and number of people in the room. Conclusions: The smoke-free policy for indoor recreation facilities was not effective at making the indoor spaces free from second hand smoke. Although a few billiard rooms installed a smoking room, indoor $PM_{2.5}$ concentrations were still higher than those of outdoor $PM_{2.5}$ or atmospheric $PM_{2.5}$. Stricter enforcement of the smoke-free policy should be achieved to prevent secondhand smoke exposure.

• Journal of Environmental Health Sciences
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• v.50 no.1
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• pp.6-15
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• 2024

Background: People's activities have been restricted due to the COVID-19 pandemic. These changes in activity patterns may lead to a decrease in fine particulate matter (PM_2.5) concentrations. Additionally, the level of population exposure to PM_2.5 may be changed. Objectives: This study aimed to analyze the impact of population movement and meteorological factors on the distribution of PM_2.5 concentrations before and after the outbreak of COVID-19. Methods: The study area was Guro-gu in Seoul. The research period was selected as January to March 2020, a period of significant population movement changes caused by COVID-19. The evaluation of the dynamic population was conducted by calculating the absolute difference in population numbers between consecutive hours and comparing them to determine the daily average. Ambient PM_2.5 concentrations were estimated for each grid using ordinary kriging in Python. For the population exposure assessment, the population-weighted average concentration was calculated by determining the indoor to outdoor population for each grid and applying the indoor to outdoor ratio to the ambient PM_2.5 concentration. To assess the factors influencing changes in the ambient PM_2.5 concentration, a statistical analysis was conducted, incorporating population mobility and meteorological factors. Results: Through statistical analysis, the correlation between ambient PM_2.5 concentration and population movement was positive on both weekends and weekdays (r=0.71, r=0.266). The results confirmed that most of the relationships were positive, suggesting that a decrease in human activity can lead to a decrease in PM_2.5 concentrations. In addition, when population-weighted concentration averages were calculated and the exposure level of the population group was compared before and after the COVID-19 outbreak, the proportion of people exceeding the air quality standard decreased by approximately 15.5%. Conclusions: Human activities can impact ambient concentrations of PM_2.5, potentially altering the levels of PM_2.5 exposure in the population.