• Journal of Korean Society for Atmospheric Environment
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• v.11 no.E
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• pp.23-29
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• 1995

Relationship between atmospheric aerosol transmissivity and air pollution was analyzed using observed data in a large industrial city, Pusan, Korea. The atmospheric aerosol transmissivity predicted by method of present study in Pusan was assessed by the method of Yamamoto et al.(1968) in order to set up an empirical model to predict the transmissivity using the various meteorological parameters and air pollution. As a result, good correlation between these tow method re observed. Thus, it is possible to conclude that the parameterization of air pollution suggested by this study is another method to give reliable estimate of atmospheric aerosol transmissivity and direct solar irradiance in Pusan.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.E2
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• pp.83-88
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• 2003

The present study details air pollution measurements in the Yellow Sea of East Asia. Large-scale air pollution was observed through satellite images and ground monitors in Chongju-Chongwon of central Korea. Evidence of a duststorm transport and resulting dustfall from the Gobi Desert in north China and Mongolia is shown. Also, transport of anthropogenic air pollutants from China to the Yellow Sea, Korea, and Japan was detected and discussed. It was found that the level of air pollution concentrations at a regional back-ground site increased 2 ∼ 4 times than the values observed with the relatively clean air, when massive air pollution from China moved to the Korean Peninsula. Satellite measurements will be useful for monitoring regional- and global-scale air pollution in the future.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2005.11a
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• pp.63-64
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• 2005

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.328-337
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• 2021

Air pollution, which is a class 1 carcinogen, such as asbestos and benzene, is the cause of various diseases. The spread of ultra-air pollution is one of the important causes of the spread of the corona virus. This paper analyzes and predicts fine dust and ultra-air pollution from 2015 to 2019 based on weather data such as average temperature, precipitation, and average wind speed in Seoul and atmospheric environment data such as SO2, NO2, and O3. Linear regression, SVM, and ensemble models among machine learning models were compared and analyzed to predict fine dust by grasping and analyzing the status of air pollution and ultra-air pollution by season and month. In addition, important features(attributes) that affect the generation of fine dust and ultra-air pollution are identified. The highest ultra-air pollution was found in March, and the lowest ultra-air pollution was observed from August to September. In the case of meteorological data, the data that has the most influence on ultra-air pollution is average temperature, and in the case of meteorological data and atmospheric environment data, NO2 has the greatest effect on ultra-air pollution generation.

• Journal of Korean Society for Atmospheric Environment
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• v.7 no.1
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• pp.41-47
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• 1991

Air pollution potentials of the 7 cities in the central part of Korea were obtained with the mean wind speed within the mixed layer and the mixed layer height calculated by the Jump Model. Seasonal variation of the afternoon mixed layer height in Seoul area shows that low in winter and high in summer. Annual mean of the morning air pollution potential was lowest in Incheon and highest in Wonju. On the other hand annual mean of the afternoon air pollution potential was lowest in Incheon and highest in Chuncheon. Relatively low air pollution potential in Incheon can be explained as high mixed layer height and the effect of sea breeze.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2009.05a
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• pp.41-47
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• 2009

• Journal of Environmental Science International
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• v.3 no.4
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• pp.357-365
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• 1994

Air pollution characteristics and the influence of sea breeze on air pollution concentration were studied using the data measured at 7 air quality continuous monitoring stations in Pusan, 1993. Maximum air pollution concentration in Pusan was Gamjeondong for $SO_2$, Sinpyeongdong for TSP, Daeyeondong for $O_3, Kwangbokdong for $NO_2$, Beomcheondong for CO and all substances were under annual ambient air quality standards. Increased rate of concentration for sea breeze was 24.4% for 502, 31.5% for TSP, 8.0% fort $O_3, 26.7% for $NO_2$, 15.7% for CO. Frequencies distribution of $SO_2$, TSP, $O_3$, $NO_2$, and CO concentration for sea breeze moved toward high concentration class.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.786-789
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• 2003

The detection of sandstorms and industrial pollutants has been the emphasis of this study. Data obtained from meteorological satellites, NOAA and GMS, have been used for detailed analysis. MODIS and Landsat images are also used for the application of future KOMPSAT- 2. Verification of satellite observations has been made with air pollution data obtained by ground-level monitors. It was found that satellite measurements agree well with concentrations and variations of air pollutants measured on the ground, and that satellite technique is a very useful device for monitoring large-scale air pollution in East Asia. The quantitative analysis of satellite image data on air pollution is the goal in the future studies.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.3
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• pp.357-365
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• 2005

The typical patterns of surface $O_3$ pollution and their dependence on meteorology were studied in the Greater Seoul Area (GSA) during warm season (April-September) from 1998 to 2002. In order to classify the $O_3$ pollution patterns, two-stage (average linkage then k-means) clustering technique was employed based on daily maximum $O_3$ concentrations obtained from 53 monitoring sites during high $O_3$ events (118 days). The clustering technique identified four statistically distinct $O_3$ pollution patterns representing the different horizontal distributions and levels of $O_3$ in GSA. The prevailed pattern (93 days, $49.5\%$) distinctly showed the gradient of $49.5\%$ concentrations going from west to east in GSA. Very high $49.5\%$ concentrations throughout GSA (24 days, $12.8\%$) were also found as a significant pattern of severe $O_3$ pollution. In order to understand the characteristics of $O_3$ pollution patterns, the relationship between $O_3$ pollution patterns and meteorological conditions were analyzed using both synoptic charts and surface/upper air data. Each pattern was closely associated with surface wind interacted with synoptic background flow allowing to transport and accumulate $O_3$ and its precursor. In particular, the timing and inland penetration of sea-breeze were apparently found to play very important role in determining $O_3$ distributions.

• Korean Journal of Environmental Agriculture
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• v.15 no.4
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• pp.494-500
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• 1996

To investigate the relationship between the atmospheric pollution level and the pollution level of soil adjacent to the roadside in Suwon suspended particles and soil samples were collected in August to October 1993, and analyzed by AAS and ICP. The Ca, K, Mg concentration in atmosphere were observed as high level and assumed that it was effected by the acid-rain originated from the erosion of concreate vicinity to the roadside, and the Pb, Zn concentration were higher at outer area than that of central area. Hazardous heavy metal (Cu, Pb, Zn) concentrations in soil were observed as high level at all sampling sites. The relationship between the heavy metal concentrations in the atmosphere and those in the soil were analyzed, by using the correlation coefficient value(r) and the result was appeared similarly. And this study indicated that the atmospheric pollution affect the level of the soil pollution adjacent to the road side.