• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.275-290
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• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.E2
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• pp.63-73
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• 2008

In this study, neural network models (NNMs) were examined as alternatives to dispersion models in predicting the short-term $SO_2$ concentrations in a coastal area because the performances of dispersion models in coastal areas have been found to be unsatisfactory. The NNMs were constructed for various combinations of averaging time and prediction time in advance by using the historical data of meteorological parameters and $SO_2$ concentrations in 2002 in the coastal area of Boryeung, Korea. The NNMs were able to make much more accurate predictions of 1 hr $SO_2$ concentrations at ground level in the morning in coastal area than the atmospheric dispersion models such as fumigation models, ADMS3 and ISCST3 for identical conditions of atmospheric stability, area, and weather. Even when predictions of 24-h $SO_2$ concentrations were made 24 hours in advance, the predictions and measurements were in good accordance(correlation coefficient=0.65 for n=216). This accordance level could be improved by appropriate expansion of training parameters. Thus it may be concluded that the NNMs can be successfully used to predict short-term ground level concentrations averaged over time less than 24 hours even in complex terrain. The prediction performance of ANN models tends to improve as the prediction lagging time approaches the concentration averaging time, but to become worse as the lagging time departs from the averaging time.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.

• Environmental Engineering Research
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• v.14 no.2
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• pp.111-119
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• 2009

A prototype GIS-based decision support system (DSS) was developed by using a database management system (DBMS), a model management system (MMS), a knowledge-based system (KBS), a graphical user interface (GUI), and a geographical information system (GIS). The method of selecting a dispersion model or a modeling scheme, originally devised by Park and Seok, was developed using our GIS-based DSS. The performances of candidate models or modeling schemes were evaluated by using a single index(statistical score) derived by applying fuzzy inference to statistical measures between the measured and predicted concentrations. The fumigation dispersion model performed better than the models such as industrial source complex short term model(ISCST) and atmospheric dispersion model system(ADMS) for the prediction of the ground level $SO_2$ (1 hr) concentration in a coastal area. However, its coincidence level between actual and calculated values was poor. The neural network models were found to improve the accuracy of predicted ground level $SO_2$ concentration significantly, compared to the fumigation models. The GIS-based DSS may serve as a useful tool for selecting the best prediction model, even for complex terrains.

• Journal of Korean Society for Atmospheric Environment
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• v.6 no.1
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• pp.51-56
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• 1990

This Study shows the effect of the district heating on the $SO_2$ concentration reduction. In order to analyze the effect of the district heating, three alternatives were set up as follows; Alternative I represented present central heating system, and Alternative II and Alternative III represented district heating system of which the scale were different from. The concludions of this study are as follows; In case of the Alternative II and III, annual average $SO_2$ concentration are reduced by 9.0% and 14.6% respectively, and winter season $SO_2$ concentrations are reduced by 12.2% and 15.8% respectively, at the highest points. The average reduction rates of $SO_2$ concentration in the district heating area are about the same as the reduction rates at the highest points. Also, it was found that using the district heating system, the ground level $SO_2$ concentrations could be reduced within the area of 5 to 10 km radius.

• International Union of Geodesy and Geophysics Korean Journal of Geophysical Research
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• v.24 no.1
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• pp.29-45
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• 1996

Before (March 26, 1994) or after the occurrence of a downslope windstorm (March 29), the NO, $NO_2$, and $SO_2$ at the ground level of Kangnung city were monitored with high concentrations in the afternoon, due to a large amount of gases emitted from combustion of motor vehicle and heating apparatus, especially near 1600-1800 LST and 2000-2100 LST, but at night, they had low concentrations, resulting from small consumptions of vehicle and heating fuels. When both moderate westerly synoptic-scale winds flow over Mt. Taegwallyang and easterly meso-scale sea breeze during the day, atmospheric pollutants should be trapped by two different wind systems, resulting in higher concentration at Kangnung city in the afternoon. At night, the association of westerly synoptic wind and land breeze can produce relatively strong winds and the dissipation by the winds cause these low concentrations to lower and lower, as nightime goes on. From March 27 through 28, an enforced localized windstorm could be produced along the lee side of the mountain near Kangnung, generating westerly internal gravity waves with hydraulic jump motions. Sea breeze toward inland appartantly confines to the bottom of the eastern side of the mountain, due to the interruption of eastward violent internal gravity waves. As the windstorm moves down toward the ground, an encountering point of two opposite winds approaches Kangnung, and a great amount of NO and $NO_2$ were removed by the strong surface winds. Thus, their maximum concentrations are found to be near 18 and 20 LST, 17 and 21 LST. In the nighttime, the more developed storm should produce very strong surface winds and the NO and $NO_2$ could be easily dissipated into other place. The $SO_2$ concentration had no maximum value, that is, almost constant one all day long, due to its removal by the strong surface winds. Especially, the CO concentrations were slightly lower during the strom period than both before or after the strom, but they were nearly constant without much changes during the during the daytime and nighttime.

• Advances in environmental research
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• v.9 no.2
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• pp.123-133
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• 2020

A time series dataset was conducted to ascertain the effect of water table on the variability in and emission of CH₄ and CO₂ concentrations at a closed landfill site. An in-situ data of methane/carbon dioxide concentrations and environmental parameters were collected by means of an in-borehole gas monitor, the Gasclam (Ion Science, UK). Linear regression analysis was used to determine the strength of the correlation between ground-gas concentration and water table. The result shows CH₄ and CO₂ concentrations to be variable with strong negative correlations of approximately 0.5 each with water table over the entire monitoring period. The R² was slightly improved by considering their concentration over single periods of increasing and decreasing water table, single periods of increasing water table, and single periods of decreasing water table; their correlations increased significantly at 95% confidence level. The result revealed that fluctuations in groundwater level is the key driving force on the emission of and variability in groundgas concentration and neither barometric pressure nor temperature. This finding further validates the earlier finding that atmospheric pressure - the acclaimed major control on the variability/migration of CH₄ and CO₂ concentrations on contaminated sites, is not always so.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.69-72
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• 2003

The meteorological model, CALMET, and its plume dispersion model, CALPUFF, were used in order to simulate the dispersion of $SO_2$ emitted from Yatagan Power Plant and its effect on Yatagan district in the episodic event on December 2 and 3, 2000. It is found that south westerly and light winds and the nighttime surface inversion layers lead to accumulation of pollutants over Yatagan district. The results are compared with the measurements done by Local Environmental Authorities of Mu la. The simulation results indicate that the maximum ground level concentrations were found northeast from the source, which agrees with experimental measurement. On the other hand, the magnitude of results obtained with the model shows some differences compared with experimental measurements.

• Journal of Korean Society for Atmospheric Environment
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• v.10 no.4
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• pp.252-259
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• 1994

A ground station has been established at Kosan, Cheju Island, since January of 1992 for the monitoring of background air Pollutant levels in Korea. Anthropogenic pollutant sources and meteorological conditions of Kosan were surveyed. Concentrations of SO$_2$, NO, NO$_{y}$ and $O_3$, were measured and analyzed for the period of February through December, 1992. The annual means of NO and SO$_2$, levels were very low in comparison to other urban's levels and similiar to other country's background levels. The annual mean of $O_3$, level was higher than urban's but comparable to other coastal region's. The NO concentration showed a distinct seasonal and diurnal variations. Summer peak was detected in the monthly means of NO and smooth peak around noon was found in the annual means of hourly data. Diurnal variation of the SO$_2$ concentration was barely detected but a slice increase in winter was detected. The $O_3$, concentration data, however, showed seasonal and diurnal variations similar to the urban's.an's.

• Journal of Korean Society for Atmospheric Environment
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• v.7 no.3
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• pp.197-202
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• 1991

It is observed that the outbreak of dust storms (yellow sand) from Northern China and Mongolia occurs a few times in April 1988 and 1990. It is found that a dust storm initiated with strong gusty winds after the passage of a cold front, particularly after defrost of the ground surface of a source region in the early spring. According to meteorological chart, satellite images and trajectory analyses, dust clouds invaded Korea in April 1988 and 1990 were landing in the sink area after 2 $\sim$ 4 days travelling for 2,000 $\sim$ 3,000 km from a source region. It was also observed that in the west coast total suspended particulated (TSP) were 100 $\sim$ 200 $\mug m^{-3}$ and sulphates $(SO_4=)$ were 3 $\sim$ 10 $\mug m^{-3}$. These values clearly exceed the concentrations of a background level measured in the Arctic and Atlantic Ocean. Trajectory analyses and meteorological analyses suggest that the high values occurred with prevailing westerly flows coming from anthropogenic sources in China. High concentrations of air pollutants occurred in the backside of an anticyclone and in the area "col".col".uot;.