• Proceedings of the Korea Air Pollution Research Association Conference
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• 2010.04a
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• pp.638-639
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• 2010

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2009.10a
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• pp.98-99
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• 2009

• Proceedings of the Korea Air Pollution Research Association Conference
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• 1999.10a
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• pp.353-354
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• 1999

입자상물질에 관한 초기연구는 TSP (Total Suspended Particle)에 집중되었으나, TSP만으로는 인체에 미치는 영향이나 시정악화 같은 현상을 정확하게 설명한 수 없었다. 보건학적 관점에서, PM10과 사망률사이에 강한 상관관계가 있는 것으로 조사된 바 있으며 (Pope et al, 1995), 특히 PM10 중에서 미세입자 (PM2.5미만)는 인체에 미치는 영향이 더욱 큰 것으로 알려지고 있다 (Joel et al., 1996). 이에 따라 입자상 물질의 규제 관리는 미세입자 측면에서 강화될 필요가 있다.(중략)

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2002.11a
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• pp.308-309
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• 2002

대기오염의 주요발생원은 자동차 배기가스, 산업시설 발전소, 배경오염물질이다. 비차량 발생원, 질산화물, 일산화탄소, 차량으로부터 배출되는 일부 미세먼지들의 비교는 과거 30년에 걸쳐 사용되고 있는 전세계 차량의 증가 때문에 불균형하게 증가하였다. 대기질에 있어 인위적인 개선의 건강상 영향을 연구할 기회는 드물다. 월드컵기간동안 서울에서의 대기질 개선을 위한 노력의 일환으로 차량 2부제를 시행은 대도시 교통수단 변화의 영향을 연구할 기회를 제공하였다. (중략)

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.04a
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• pp.221-223
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• 2000

Cyclones are among the oldest types of industrial particulate control equipment and air sampling device. Until now, a great number of experimental studies have been performed on cyclones. In these studies, almost all of the eight cyclone dimensions were varied and Changes in cyclone performance produced by these variations were studied. However, very little information is available on the effects of the cone bottom diameter. (omitted)

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.3
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• pp.276-285
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• 2010

Recently the Korean government has tried to cut down the $PM_{10}$ concentration by the Special Law for Air Quality Improvement. But the concentrations of $PM_{10}$ have exceeded the air quality standard at most monitoring stations. Primary $PM_{10}$ emitted from various sources and emission data have large uncertainty. The biomass burning is one of the major sources of $PM_{10}$ emission. The biomass burning is composed of wood stove usage, meat cooking and agricultural combustion etc.. Activity data and emission factors for the biomass burning are limited, and it is hard to calculate the air pollution emissions from these sources. In this study, we tried to estimate the air pollution emission from residential wood stove usage. The number of total wood stoves is estimated by the survey of wood stove manufacturer. And air pollution emission factors for the wood stove are investigated using the flue gas measurement by U.S. EPA particulate test method (Method 5G). As the results, the $PM_{10}$ and CO emission factors of wood stove are estimated as 7.7 g/kg-wood and 78.8 g/kg-wood respectively. The annual $PM_{10}$ and CO emissions from wood stove are calculated as 1,200~3,600 ton/year and 12,600~36,400 ton/year in Korea. It is confirmed that wood stove is the one of major sources of biomass burning, and the survey for activity data and the measurement for emission factors are needed for reducing the uncertainty of these emission data.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.1
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• pp.53-68
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• 2021

The study aims to propose a locating method of green space for reducing Particulate Matter (PM) in ambient air in conjunction with its source traces and vulnerable groups. In order to carry out the aims and purposes, a literature review was conducted to derive indicators of vulnerable area to PM. Based on the developed indicators, the vulnerable areas and green spaces creation strategies for each cluster were developed for the case of Seongdong-gu, Seoul. As a result, six indicators for vulnerability analysis were came out including the vulnerable groups (children's facilities, old people's facilities), emission sources (air pollutant emission workplaces, roads), and environmental indicators (particulate matter concentration, NDVI). According to the six selected indicators, the target area was divided into 39 hexagons and analyzed to result the most vulnerable areas to particulate matter. As a result of comprehensive vulnerability analysis, the Seongsu-dong area was found to be the most vulnerable to particulate matter, and 5 clusters were derived through k-means cluster analysis. Cluster 1 was analyzed as areas that most vulnerable to particulate matter as a result of the comprehensive analysis, therefore urgent need to create green spaces to reduce particulate matter. Cluster 2 was areas that mostly belonged to the Han River. Cluster 3 corresponds to the largest number of hexagons, and since many vulnerable groups are distributed, it was analyzed as a cluster that required the creation of a green spaces to reduce particulate matter, focusing on facilities for vulnerable groups. Three hexagons are included in cluster 4, and the cluster has many roads and lacks vegetation in common. Cluster 5 has a lot of green spaces and is generally distributed with fewer vulnerable groups and emission sources; however, it has a high level of particulate matter concentration. In a situation where various green spaces creation projects for reducing particulate are being implemented, it is necessary to consider the vulnerable groups and emission sources and to present green space creation strategies for each space characteristic in order to increase the effectiveness of such projects. Therefore, this study is regarded as meaningful in suggesting a method for selecting a green area for reducing PM.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.3
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• pp.348-356
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• 2012

It is very important to investigate air pollutants and emissions emitted from open burning in order to control nonpoint sources effectively. In this study, we utilized incineration simulator proposed by U.S. EPA and investigated concentrations of TSP, PM10, PM2.5 from woods and household wastes burning to calculate emission factors and build emission inventories. The results of experiment with 15 kg of woods and 3 kg of household wastes using the incineration simulator were as follows: in case of woods burning, TSP concentration was $66.4mg/m^3$, PM10 concentration was $28.4mg/m^3$, PM2.5 concentration was $17.9mg/m^3$, respectively; in case of household wastes burning, TSP concentration was $118.4mg/m^3$, PM10 concentration was $66.8mg/m^3$, PM2.5 concentration was $55.2mg/m^3$, respectively. Concentrations from household burning, as stated above, were higher than those from woods burning. Emission factors (EFs) for woods and household wastes burning were calculated as 2.45 and 6.75 g/kg for TSP, 0.86 and 5.45 g/kg for PM10, 0.78 and 4.81 g/kg for PM2.5, respectively. EFs of TSP, PM10, PM2.5 calculated from household wastes burning were higher than those of woods burning. When we added PM emissions from woods burning and household wastes burning to Korean National Emission Inventory named as Clean Air Policy Support System (CAPSS), CAPSS annual emissions of TSP, PM10, PM2.5 were increased by 0.08~0.26% (An increase rate for TSP, PM10, PM2.5 were 0.08~0.10%, 0.16~0.20% and 0.18~0.26%, respectively). Note that we assumed that the 1% of household wastes is emitted by open burning.

• Particle and aerosol research
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• v.16 no.2
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• pp.31-47
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• 2020

Although interest in air quality has increased due to the frequent occurrence of high-concentration fine particulate matter recently, the official fine particulate matter measuring network has failed to provide spatial detailed air quality information. This is because current measurement equipment has a high cost of installation and maintenance, which limits the composition of the measuring network at high resolution. To compensate for the limitations of the current official measuring network, this study constructed a spatial high density measuring network using the fine particulate matter simple measuring device developed by Observer, W-Station. W-Station installed 48 units on Jeju Island and measured PM_2.5 for six months. The data collected in W-Station were corrected by applying the first regression equation for each section, and these measurements were compared and analyzed based on the official measurements installed in Jeju Island. As a result, the time series of PM_2.5 concentrations measured in W-Station showed concentration characteristics similar to those of the environmental pollution measuring network. In particular, the results of comparing the measurements of W-Station within a 2 km radius of the reference station and the reference station showed that the coefficient of determination (R²) was 0.79, 0.81, 0.67, respectively. In addition, for W-Station within a 1 km radius, the coefficient of determination was 0.85, 0.82, 0.68, respectively, showing slightly higher correlation. In addition, the local concentration deviation of some regions could be confirmed through 48 high density measuring networks. These results show that if a network of measurements is constructed with adequate spatial distribution using a number of simple meters with a certain degree of proven performance, the measurements are effective in monitoring local air quality and can be fully utilized to supplement or replace formal measurements.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1721-1732
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• 2020

Elementary school students spend a lot of time in a school and are more exposed to indoor air pollution. Also the students are physically growing and have a relatively high respiratory rate per unit weight compared to adults, so it is known that there is a high sensitivity to indoor air pollution. Therefore, indoor air quality in a school is becoming an important factor for the student's health. In this study, a correlation analysis using levels of indoor and outdoor Particulate Matter (PM) measured from five elementary schools in Seoul was performed to evaluate the effect of outdoor PM on indoor PM. PM ratio and indoor/outdoor (I/O) ratio were also analyzed to investigate the actual condition of indoor air quality and effect of outdoor PM on indoor PM. As a result, the correlation between indoor and outdoor PM in elementary school was more significant in PM2.5 and PM1 than PM10. In the case of I/O ratio, the I/O ratio of PM10 was higher than that of 1 in four elementary schools except SD (BB:2.21, NS: 1.67, IS: 1.73, SI: 1.17). This indicates that the activity of students has a great effect on the concentration of indoor PM10.