• 한국환경보건학회지
• /
• 제27권3호
• /
• pp.87-98
• /
• 2001

There has been increased interest in the health effects of the Environmental Tobacco Smoke(ETS) as a confirmed human carcinogen. It has been known to be extremely difficult to make an accurate assessment of exposure to ETS since it is consisted of a variety of components and there are a number of labile chemicals. Therefore, it is necessary to obtain, to interpretate and to provide the data of quantitative exposure assessment to ETS in the field of environmental health. The purpose of this research is to evaluate the concentration of ETS using VOC in indoor office environments. The correlations and concentrations of benzene, RSP, 3-EP, nicotine that are indicators for ETS were investigate with smoking density, air change per hour(ventilation rate). Air samples were taken in smoking room(7 sites), smoking allowed office (3 sites), corridor outside smoking room(7 sites), non-smoking office (9 sites). The concentrations of benzene showed significant difference according to category of indoor office environments. The geometric mean concentration of benzene were 23.56 ${\mu}{\textrm}{m}$/㎥(range 4.80~192.90 ${\mu}{\textrm}{m}$/㎥) in smoking rooms. 6.16 ${\mu}{\textrm}{m}$/㎥ in smoking allowed offices, 1.32 ${\mu}{\textrm}{m}$/㎥ in the non-smoking offices respectively. The ratios of the concentration of benzene between outdoor air and smoking room, smoking allowed office, and non-smoking indicators concentrations, SD, and SI were 0.82(benzene and nicotine). 0.76(benzene and RSP), 0.60(benzene and SD), 0.76(benzene and SI). It is proposed that benzene is a good indicator for ETS.

• 한국산학기술학회논문지
• /
• 제17권5호
• /
• pp.17-25
• /
• 2016

본 연구에서는 지하역사와 지상역사에서의 실내공기질을 측정하고, 외부 공기에 의한 영향 정도를 확인하고자 하였다. 측정된 물질은 '실내공기질 관리법'상에서 유지기준과 권고기준으로 지정된 항목 중, 농도가 검출되지 않은 석면을 제외한 미세먼지($PM_{10}$), 이산화탄소($CO_2$), 일산화탄소(CO), 이산화질소($NO_2$), 폼알데하이드(HCHO), 오존($O_3$), 총부유세균(TAB), 총휘발성유기화합물(TVOC), 라돈 등 9종이다. 또한 미세먼지, 이산화질소, 오존 등 세 가지 물질은 I/O ratio를 통해 외기에의한 영향을 확인하였다. 공기질 측정결과 지상역사에 비해 지하역사에서 미세먼지, 폼알데하이드, 총휘발성유기화합물, 이산화질소, 라돈 등이 높은 농도로 검출되었으며, 이는 지하역사 내부에 그 오염물질의 발생요소가 존재하기 때문으로 판단된다. 오존 농도는 지상역사에서 지하역사보다 높은 농도로 검출되었으며, 특히 외부로 노출되어있는 지상역사 승강장에서 높은 농도를 보임으로써 외기 유입에 의한 영향이 있는 것으로 확인되었다. 따라서, 외기에 의한 영향을 받는 오염물질은 역사의 기계환기시 제거 과정을 거쳐 오염물질의 실내유입을 차단하고, 지하역사에서 기인한 미세먼지 등의 물질들은 실내에서 그 발생원에 따른 별도의 처리가 필요할 것으로 판단된다.

• 한국환경보건학회지
• /
• 제39권5호
• /
• pp.438-446
• /
• 2013

Objectives: This study was performed in order to determine airborne fungi levels in homes and find related factors that may affect airborne fungi concentration. Methods: Fifty homes were study subjects for measuring airborne fungi. For sampling airborne fungi, the impaction method on agar plates was used and samples were counted as colony forming units per cubic meter of air ($CFU/m^3$). In addition, information regarding housing characteristics and atopic disease in each home were collected via questionnaire. Results: The geometric means (GM) of airborne fungi concentrations in fifty living rooms and bedrooms were 68.03 and 62.93 $CFU/m^3$, respectively. The GM of airborne fungi concentration in atopy homes was 78.42 $CFU/m^3$. This was higher than non-atopy homes' 54.34 $CFU/m^3$ (p-value=0.051). In the results of the multiple regression analysis, outdoor airborne fungal concentration proved a strong effective factor on indoor airborne fungal concentration. Also, construction year, floor area of house, indoor smoking and frequency of ventilation were factors that showed a significant association with indoor airborne fungi concentration. Conclusions: The results of this study show that some housing and living characteristics may affect the development and increase of airborne fungi. In addition, exposure to airborne fungi may be a risk factor for the prevalence of childhood atopic diseases.

• 한국환경보건학회지
• /
• 제42권1호
• /
• pp.1-9
• /
• 2016

Objectives: Personal exposure to air pollution is affected by contact over time and by location. The purpose of this study was to determine the relationship between personal exposure to $PM_{2.5}$ and the time-activity patterns of the elderly in urban and rural areas. Methods: A total of 44 elderly participants were recruited for a 24-hour $PM_{2.5}$ personal exposure measurement. Twenty-four were from Seoul (urban area) and 20 were from Asan (rural area). Energy expenditure and spatiotemporal positioning were monitored through $PM_{2.5}$ measurement. Spearman correlation analysis was conducted to determine the relationship between $PM_{2.5}$ and time-activity pattern. Results: Daily average $PM_{2.5}$ personal exposures were $19.1{\pm}9.7{\mu}g/m^3$ in Seoul and $29.1{\pm}16.9{\mu}g/m^3$ in Asan. Although outdoor exposure was higher in Seoul than in Asan, residential indoor exposure was higher in Asan than in Seoul. Higher $PM_{2.5}$ personal exposure in Asan could be explained by longer time in residential indoor environments and higher indoor $PM_{2.5}$ concentrations. Seoul elderly had higher energy expenditure, which may be due to the use of mass transportation. Conclusion: Personal exposure to $PM_{2.5}$ was higher among Asan elderly than Seoul elderly because of high residential indoor concentrations and longer residential time. Lack of energy spent and higher personal exposure to $PM_{2.5}$ might have led to higher risk among the Asan elderly.

• 분석과학
• /
• 제20권6호
• /
• pp.474-482
• /
• 2007

보육시설은 미취학전 아이들에게 중요한 실내 환경이며 하루 중 가장 많은 시간을 보내는 영유아들에 잠재적인 유해한 건강영향을 줄 수 있다. 따라서 본 연구는 2006년 2월에서 12월에 걸쳐 서울에 위치한 보육시설 29개 시설을 선정하여 실태조사를 하였다. 측정물질은 휘발성유기화합물 중 7개의 물질(벤젠, 톨루엔, 에틸벤젠, m, p-자일렌, 스틸렌, o-자일렌 및 TVOC)을 선정하였다. 보육시설에서의 TVOC, 톨루엔, m, p-자일렌, 에틸벤젠, 벤젠 그리고 스틸렌의 평균농도는 $318.7{\mu}g/m^3$, $51.6{\mu}g/m^3$, $11.7{\mu}g/m^3$, $6.5{\mu}g/m^3$, $4.2{\mu}g/m^3$, $3.6{\mu}g/m^3$이고, 정성분석된 TVOC 중 톨루엔은 17.6%로 가장 많은 비중을 차지하였다. TVOC와 톨루엔의 실내/실외 농도비는 각각 2.0, 1.6으로 나타났다. TVOCs의 계절에 따른 오염물질의 특성결과는 여름이 $433.9{\mu}g/m^3$로 가장 높게 나타났으며, 다른 계절은 겨울 280.5, 봄 298.3 및 가을 $264.6{\mu}g/m^3$로 비슷한 경향으로 나타났다.

• 한국대기환경학회지
• /
• 제27권5호
• /
• pp.569-579
• /
• 2011

This study evaluated the indoor air quality of 26 government offices located in Seoul. The pollutant samples were taken from Jan. 13th to Jan. 29th and Feb. 20th to Feb. 23rd, 2010 in the offices. The target indoor pollutants for this study were $PM_{10}$, formaldehyde, carbon monoxide, carbon dioxide, total bacteria counts, total volatile organic compounds, radon, nitrogen dioxide, ozone, and asbestos which were controlled by the indoor air quality law for the multiple-use facilities management. The results of this study showed that some pollutants of the 38.5% offices exceeded the standards of the air quality guideline. The correlation analysis of the same pollutants between indoor and outdoor represented that $NO_2$ (r=0.629, p<0.05) and $O_3$ (r=0.459, p<0.01) were significant, however, $PM_{10}$ and CO were not. The correlation analysis between different pollutants showed that CO and TVOC (total volatile organic compounds: r=0.724; p<0.01), CO and $NO_2$ (r=0.674; p<0.01), HCHO and humidity (r=0.605; p<0.01), $CO_2$ and TVOC (r=0.534; p<0.01), TBC (total bacteria counts) and Asbestos (r=0.520; p<0.01) were significant. The energy-saving system of government buildings in winter caused under-ventilated and poor air quality. This study suggests that the concentrations of radon and $CO_2$ should be used as an indicator for monitoring indoor air quality and maintaining effective ventilations.

• 한국환경보건학회지
• /
• 제45권3호
• /
• pp.247-257
• /
• 2019

Objectives: The Korea Ministry of Environment regulates concentrations of hazardous air pollutants (HAPs) through Atmosphere Environmental Standards to protect public health from HAPs. However, simply determining the exceedance of HAP concentrations has several limitations and more comprehensive assessment is required. In addition, integrated risk assessment is needed considering exposure in all microenvironments, including outdoor as well as indoor environments. The purpose of this study was to assess the differences in risk by sub-population groups according to time-activity patterns and reported concentrations, as well as the lifetime risk for Koreans. Methods: In this study, we calculated time-weighted average exposure concentrations for benzene and $PM_{10}$ among preschool-age children, students, housewifes, workers, and the elderly using residential time and concentrations for indoor (house, school or workplace, other), outdoor, and transport by the meta-analysis method. The risk assessments were conducted by excess cancer risk and disease death risk using 1,000,000 Monte-Carlo simulations for probabilistic analysis. Results: Preschool-age children, students, housewifes, workers, and the elderly spent 91.9, 86.0, 79.8, 82.2, and 77.3% of their day in their house, workplace, or school, respectively. The more than 99% excess cancer risk for benzene exceed 1.0E-06 in all sub-populations and lifetime. The acute disease death risk for $PM_{10}$ for housewifes and workers for lifetime were 3.35E-04 and 3.18E-04, and chronic disease death risks were 2.84E-03 and 2.70E-03, respectively. Conclusions: The risk of benzene and $PM_{10}$ by sub-population group and for the lifetime of housewifes and workers were assessed. Benzene showed risky results for this study. All disease death risks of $PM_{10}$ were higher than 1.0E-04 and showed different risks by sub-population. This study can be used as a basis for lifetime exposure and risk assessment to benzene and $PM_{10}$.

• 한국환경보건학회지
• /
• 제50권1호
• /
• pp.6-15
• /
• 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.

• 환경위생공학
• /
• 제15권3호
• /
• pp.1-7
• /
• 2000

There is increasing evidence suggestion that passive smoking increases the risk of lung cancer and other disease, though the potential health effects of exposure to environmental tobacco smoke (ETS) is a controversial subject. Since smoking in restaurant is prevalent in Korea, the concern on passive smoking exposure of non-smoking service-workers has been requested. ETS exposure of non-smoking service-workers at restaurant was assessed because they hare spent their times in restaurant indoors. The purpose of this study was feasibility of nitrogen dioxide($NO_2$) as exposure marker of ETS. The results of the study were as follows; 1. Average $NO_2$ concentrations in indoor and outdoor t restaurants were 57.1ppb(${\pm}12.4$) and 54.29ppb(${\pm}9.54$), respectively. Comparing office-workers, service-workers at restaurants were exposured highly. 2. The personal $NO_2$ measurement as exposure marker of ETS could cause the exposure error because $NO_2$ can be generated by combustion appliances in indoor. 3. Service-workers spent their most time(86.6%) in indoor. Mean time spent at restaurant indoors and at home was 9.4 hours and 10.9 hours, respectively. 4. Personal $NO_2$ levels correlated with indoor $NO_2$ concentrations of restaurant (r=0.70) and of their home (r=0.52) rather than of outdoor $NO_2$ concentration of restaurant (r=0.35). The cause of personal $NO_2$ exposure of non-smoking service-workers were considered as smoking of guests and combustion appliance indoors. 5. personal $NO_2$ exposures were estimated using Monte-Carlo simulation and time-weighted model. Estimated personal $NO_2$ level was 47.25ppb(${\pm}8.3$).

• 한국산업보건학회지
• /
• 제6권1호
• /
• pp.67-76
• /
• 1996

This study was performed to estimate total dust concentrations and particle size distribution of wood dust in the furniture and sawmill industries. To compare total wood dust concentrations, two samplers recommended by the American Conference of Governmental Industrial Hygienists and by the National Institute for Occupational Safety and Health were used. Concentration data were analyzed by paired-t tests using the SAS program and two parameters of the particle size distributions were determined by histogram. The results were as follows: 1. Particle size distributions showed a unimodal pattern in cutting and a bimodal in sanding operations. Mass median aerodynamic diameters(MMAD) were $17.35{\mu}m$ in cutting, and $1.39{\mu}m$ for small mode and $18.89{\mu}m$ for large mode in sanding operations. The proportions of particle size larger than $9.8{\mu}m$ estimated by the impactor were 61.16 % in cutting and 62.33 % in sanding operations, respectively. 2. The average personal total dust concentrations measured by IPM sampler were $17.12mg/m^3$ (GSD=1.45) from indoor samples, $2.97mg/m^3$(GSD=1.90) from outdoor samples in cutting, and $8.01mg/m^3$(GSD=1.58) from sanding operation. And those of by 37 mm closed-face cassette were $9.12mg/m^3$(GSD=1.46), $1.06mg/m^3$(GSD=1.99) from cutting, and $3.32mg/m^3$(GSD=2.16) from sanding operations. 3. The average area total dust concentrations measured by IPM sampler were $1.88mg/m^3$(GSD=2.04) from indoor cutting, $4.76mg/m^3$(GSD=2.83) from indoor sanding operations. And those of by 37mm closed-face cassette were $0.49mg/m^3$(GSD=2.34) from cutting, and $1.32mg/m^3$(GSD=3.03) from sanding operations. 4. The ratio of personal total dust concentrations measured by 37 mm closed-face cassette to those by IPM sampler were 35.7 %, 53.3 % from cutting, and 41.4 % from sanding operations. 5. The ratio of area total dust concentrations measured by 37 mm closed-face cassette to those by IPM sampler were 26.1 % from cutting, and 27.7 % from sanding operations. 6. A statistically significant difference of total dust concentrations between the 37 mm closed-face cassette and the IPM sampler was found.