• Resources Recycling
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• v.23 no.2
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• pp.17-25
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• 2014

The thermal effect on the compositional change of the $SO_2$ absorption process product was investigated compared with the composition of raw material when dolomite is employed in place of lime in the scrubbing process based on thermodynamic estimation. It was considered that the equilibrium reactions which directly related with the formation of $CaSO_4$ and $MgSO_4$, the absorption process products, are those between $Ca^{2+}$ and $Ca(OH)_2$, $Mg^{2+}$ and $Mg(OH)_2$, and the secondary dissociation reaction of $H_2SO_4$. It was thought to be necessary to examine the enthalpy change for the formation reactions of $CaSO_4$ and $MgSO_4$ along with the thermal feature of the relative reactions to figure out the influence of temperature on the compositional change of absorption process products. The stable regions for $Ca(OH)_2$ and $Mg(OH)_2$ in Pourbaix diagram were found to be increased as temperature rises and the equilibrium reaction between $Ca^{2+}$ and $Ca(OH)_2$ was investigated to be more strongly influence by temperature change compared with the equilibrium reaction between $Mg^{2+}$ and $Mg(OH)_2$. The amounts of $CaSO_4$ and $MgSO_4$ were anticipated to be decreased with temperature considering the thermal characteristics for the equilibrium reactions regarding calcium, magnesium, and $H_2SO_4$. It was understood that the formation ratio between $CaSO_4$ and $MgSO_4$ is greater than the composition ratio between calcium and magnesium contained in dolomite at specific temperature and the decrease of the formation ratio of $CaSO_4$ and $MgSO_4$ with temperature was estimated to be diminished as the content of calcium in dolomite is increased. In addition, the extent of the change in the compositional ratio between absorption process products was examined to be reduced compared with the composition of raw material as the calcium content in dolomite is raised.

• 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.

• Tuberculosis and Respiratory Diseases
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• v.55 no.6
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• pp.560-569
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• 2003

Background : A large number of pollutants such as sulfur dioxide, nitric oxide, carbon monoxide, particulate matter, and ozone influence on the body. These pollutants put a burden on the lung and the sequelae resulting from the oxidative stress are thought to contribute to the development of fibrotic lung disease, emphysema, chronic bronchitis and lung cancer. Also, carbon monoxide generated from the incomplete combustion of carbon-containing compounds is an important component of air pollution caused by traffic exhaust fumes and has the toxic effect of tissue hypoxia and produce various systemic and neurologic complications. The objective of this study is to compare the difference of pulmonary function and serum carboxyhemoglobin(CO-Hb) level between the traffic policemen and clerk policemen. Method : Three hundred and twenty-nine of traffic policemen, and one hundred and thirty clerk policemen were included between 2001 May and 2002 August. The policemen who took part in this study were asked to fill out a questionnaire which included questions on age, smoking, drinking, years of working, work-related symptoms and past medical history. The serum CO-Hb level was measured by using carboxyoximeter. Pulmonary function test was done by using automated spirometer. Additional tests, such as elecrocardiogram, urinalysis, chest radiography, blood chemistry, and CBC, were also done. Results : $FEV_1(%)$ was $97.1{\pm}0.85%$, and $105.7{\pm}1.21%$(p<0.05). FVC(%) was $94.6{\pm}0.67%$, and $102.1{\pm}1.09%$, respectively(p<0.05). Serum CO-Hb level was $2.4{\pm}0.06%$, and $1.8{\pm}0.08%$(p<0.05). After correction of confounding factors (age, smoking), significant variables were FVC(%), $FEV_1(%)$ and serum CO-Hb level(%)(p<0.05). Conclusion : Long exposure to air pollution may influence the pulmonary function and serum CO-Hb level. But, further prospective cohort study will be needed to elucidate detailed influences of specific pollutants on pulmonary function and serum carboxyhemoglobin level.

• 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.