• Journal of Korean Society for Atmospheric Environment
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• v.20 no.2
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• pp.195-204
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• 2004

In this study, the concentrations of major reduced sulfur compounds (H$_2$S, $CH_3$SH, DMS, and DMDS) were determined from ambient air in a monitoring station located in the mid-eastern area of Seoul. Measurements of sulfur species were conducted by the combination of on -line air sampling, thermal desorption, and capillary GC/PFPD analysis. A total number of 143 hourly samples were collected in the two time periods set between June and July 2003. The mean concentrations of four sulfur species measured in the whole study period were found on the order: DMS (535$\pm$183) > H$_2$S (47$\pm$10) > DMDS (35$\pm$22) > $CH_3$SH (6.19$\pm$29.4 pptv). The results of this study show that the concentrations of DMS at the study area are generally higher than those reported previously in the oceanic environments, while those of other sulfur species are not easy to compare with due to the lack of data. The H$_2$S concentrations were generally higher during the daytime than the nighttime, whereas those of others generally exhibited a reversed diurnal pattern. The overall results of our study suggest that the distribution of major reduced S compounds should be controlled by diverse processes in the urban area.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2001.11a
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• pp.317-318
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• 2001

PM$_{10}$ 대기먼지는 자연적이고 인위적인 발생원에 의해 다양한 미량원소들을 함유하고 있기 때문에 흔히 대기질이나 환경영향 평가를 위한 대기관측시료로 이용되고 있으며, 특히 PM$_{2.5}$ 먼지의 질량농도와 특정원소의 농도가 높을 때, 장\ulcorner단기적으로 인체 보건에 큰 영향을 미치는 것으로 알려져 있다. 중성자 방사화분석법은 핵반응을 통해 생성시킨 방사성동위원소로부터 방출되는 방사선을 검색하여 절대적으로 성분원소를 정량하는 핵분석기술(Nuclear Analytical Techniques)중의 하나로서 소량의 시료로부터 미량 원소의 비파괴, 동시 다원소 분석이 가능하고 분석감도($10^{-3}$ - $10^{-7}$$\mu\textrm{g}$)가 탁월한 분석기술이다. (중략)략)

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.6
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• pp.559-574
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• 2016

Based on air quality monitoring data ('10~'14) of Suwon City provided by the Korean Department of Environment, a fundamental statistic for 5 criteria pollutants such as $SO_2$, CO, $NO_2$, $O_3$, and $PM_{10}$ was initially investigated. The purpose of this paper was to review the ambient air quality standards(AAQS) of Korea, the U.S.A. and the EU and further to assess the degree of standard strength by the achievability of the AAQS comparing each national standard. Since the level and the way of standards for each air pollutant are various among countries, it is difficult to determine the degree of relative attainment using the same set of data above. Further when all the areas having bad air quality for a specific pollutant are classified as non-attainment case, it is more difficult to assess the degree of standard strength. Thus we introduced a new concept called the average concentration distance (ACD) to quantitatively estimate the strength. As results, the Korean 1-hr $NO_2$ AAQS is slightly stronger than the US standard and weaker than the EU standard. The Korean $O_3$ standard is strongest; however, the Korean $PM_{10}$ standard is weakest. Furthermore the equivalent levels of 8-hr $O_3$ standards in the U.S.A. and the EU were respectively estimated as 97.5 and 95.7 percentiles of the Korean standard with maintaining the existing standard of 60 ppb.

• Journal of Environmental Science International
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• v.14 no.11
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• pp.1035-1047
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• 2005

By using hourly $NO_2$ concentration data$(1998\~2000)$ at the Busan Metropolitan City air qualify monitoring sites, characteristics of daily mean value of $NO_2$ concentration was discussed in space and time. The correlation between $NO_2$ concentration and other relating air pollutants was analyzed by using SAS program and meteorological parameters as well. After choosing representative 4 areas, this study used hourly concentration data$(1998\~2000)$ from air quality monitoring sites on $NO_2,\;NO,\;O_3,\;CO,\;SO_2\;and\;PM_{10}$. Typical metropolitan characteristics of two peaks in a day was shown in the variation of $NO_2$ concentration of Busan city.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1711-1720
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• 2020

Particulate matter (PM) that has been artificially generated during the recent of rapid industrialization and urbanization moves and disperses according to weather conditions, and adversely affects the human skin and respiratory systems. The purpose of this study is to predict the PM10 concentration in Seoul using meteorological factors as input dataset for multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models, and compared and evaluated the performance of the models. First, the PM10 concentration data obtained at 39 air quality monitoring sites (AQMS) in Seoul were divided into training and validation dataset (8:2 ratio). The nine meteorological factors (mean, maximum, and minimum temperature, precipitation, average and maximum wind speed, wind direction, yellow dust, and relative humidity), obtained by the automatic weather system (AWS), were composed to input dataset of models. The coefficients of determination (R2) between the observed PM10 concentration and that predicted by the MLR, SVM, and RF models was 0.260, 0.772, and 0.793, respectively, and the RF model best predicted the PM10 concentration. Among the AQMS used for model validation, Gwanak-gu and Gangnam-daero AQMS are relatively close to AWS, and the SVM and RF models were highly accurate according to the model validations. The Jongno-gu AQMS is relatively far from the AWS, but since PM10 concentration for the two adjacent AQMS were used for model training, both models presented high accuracy. By contrast, Yongsan-gu AQMS was relatively far from AQMS and AWS, both models performed poorly.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.118-127
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• 2021

Recently, the public's interest in the air environment has increased, and public health is threatened by fine particulate matter. Furthermore, the government continues efforts to improve air quality by expanding the monitoring of air pollutants and reinforcing environmental standards. Since air quality differs depending on the region in the Korean Peninsula, it is currently necessary to identify the cause and search for influencing factors. In this study, the atmospheric environment and regional differences in cities located in the Chungnam Province were observed. As a research method, regression analysis was performed for weather conditions, such as temperature, wind speed, precipitation, and season and targeted at air pollutants, such as SO₂, NO₂, CO, O₃, PM₁₀, and PM_2.5, as well as heavy metals contained in particulate matter, such as Pb, Cd, Cr, Cu, Ni, As, Mn, Fe, Al, Ca, and Mg. In the case of PM₁₀, the concentrations of Mn(0.4884) in Cheonan, CO(0.3329) in Dangjin, and Mg(0.5691) in Seosan were highest. In the case of PM_2.5, Cheonan NO₂(0.4759), Dangjin CO(0.4128), and Seosan NO₂(0.3715) were significantly affected. In summary, the influencing factors vary according to the region in Chungnam province in terms of air quality, and there is a difference in the degree of contribution. Therefore, it is considered that the Korean government's management of air quality is required for each region.

• Particle and aerosol research
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• v.14 no.4
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• pp.191-203
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• 2018

IIn this study, we have developed drying systems for reducing the error by humidity on measuring particulate matter (PM) in the ambient air with optical particle measuring instruments. Two types of drying systems were designed: drying systems using heating and dilution methods. In addition, 3 types of drying systems using a heating method were designed: Type A (1 hole), B (3 holes) and C (7 holes). After making them, the laboratory and field tests were carried out to evaluate the developed drying systems. As a result, it was shown that the PM concentrations obtained by PM monitoring devices with drying systems agree well with that of the reference devices. Therefore, it could be concluded that the drying systems can be applied to PM monitoring devices for real-time monitoring of the ambient aerosols.

• Journal of Environmental Health Sciences
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• v.48 no.6
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• pp.298-305
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• 2022

Background: The concentration of air pollutants as measured by the Air Quality Monitoring System (AQMS) is not an accurate population exposure level since actual human activities and temporal and spatial variability need to be considered. Therefore, to increase the accuracy of exposure assessment, the population should be considered. However, it is difficult to obtain population data due to limitations such as personal information. Objectives: The existing population defined in this study is the number of people in each region's grid. The purpose is to provide a methodology for evaluating exposure to PM_2.5 through existing population data provided by the National Geographic Information Institute. Methods: The selected study period was from October 26 to October 28, 2021. Using PM_2.5 concentration data measured at the Sensor-based Air Monitoring Station (SAMS) installed in Guro-gu and Wonju-si, the concentration for each grid was estimated by applying inverse distance weights through QGIS version 3.22. Considering the existing population, population-weighted average concentration (PWAC) was calculated and the exposure level of the population was compared by region. Results: The outdoor PM_2.5 concentration as measured through the SAMS was high in Wonju-si on all three days. Wonju-si showed an average 22% higher PWAC than Guro-gu. As a result of comparing the PWAC and outdoor PM_2.5 concentration by region, the PWAC in Guro-gu was 1~2% higher than the observed value, but it was almost the same. Conversely, observations of Wonju-si were 10.1%, 11.3%, and 8.2% higher than PWAC. Conclusions: It is expected that the Geographic Information System (GIS) method and the existing population will be used to evaluate the exposure level of a population with a narrow activity radius in further research. In addition, based on this study, it is judged that research on exposure to environmental pollutants and risk assessment methods should be expanded.

• Journal of Environmental Health Sciences
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• v.49 no.4
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• pp.183-189
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• 2023

Background: The indoor air quality of classrooms, in which the capacity per unit area is high and students spend time together, must be managed for safety and comfort. It is necessary to develop an eco-friendly indoor air quality reduction method rather than biased management that relies solely on air purifiers. Objectives: In this study, plants and air purifiers were installed in middle school classrooms to evaluate the indoor PM_2.5 reduction. Methods: Four middle school classrooms were selected as test beds. Air quality was monitored in real-time every one minute using IoT equipment installed in the classrooms, corridors, and rooftops. After measuring the background concentration, plants and air purifiers were installed in the classroom and the PM_2.5 reduction effect was analyzed through continuous monitoring. Results: After installing the plants and air purifiers, the average PM_2.5 concentration was 33.7 ㎍/m³ in the classrooms without plants and air purifiers, 25.6 ㎍/m³ in classrooms with plants only, and 21.7 ㎍/m³ in classrooms with air purifiers only. In the classroom where plants and air purifiers were installed together, it was 20.0 ㎍/m³. The reduction rates before and after installation were 4.5% for classrooms with plants only, 16.5% for classrooms with air purifiers only, and 27.6% for classrooms with both plants and air purifiers. The I/O ratio, which compares the concentration of PM_2.5 in classrooms with corridors and outside air, also showed the lowest in the order of plants and air purifiers, air purifiers, and plant-only classrooms. Conclusions: The PM_2.5 reduction effect of using plants was confirmed, and it is expected to be used as basic data for the development of environmentally-friendly indoor air quality improvement methods.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.3
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• pp.358-366
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• 2011

The purpose of this study was to analyze the monthly and seasonal PM10 data using the Autoregressive Error (ARE) model at the southern part of the Gyeonggi-Do, Pyeongtaek monitoring site in Korea. In the ARE model, six meteorological variables and four pollution variables are used as the explanatory variables. The six meteorological variables are daily maximum temperature, wind speed, amount of cloud, relative humidity, rainfall, and global radiation. The four air pollution variables are sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), carbon monoxide (CO), and ozone ($O_3$). The result shows that monthly ARE models explained about 17~49% of the PM10 concentration. However, the ARE model could be improved if we add the more explanatory variables in the model.