• Journal of Climate Change Research
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• v.5 no.1
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• pp.83-94
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• 2014

Gangnam-gu, where the survey of residents' consciousness has been made in this study, is the district shows the highest rate of the energy consumption and greenhouse gas emission per unit area except some industrial districts such as Gwangyang, Ulsan, and Pohang. The greenhouse gas emission amount of Gangnam-gu is 4,863,765 $tCO_2$ which accounts for 10 % of the total discharging amount of Seoul, 50,330,356 $tCO_2$, which is ranked the top greenhouse gas emission rate in the commercial category and the 2nd place in the household category. The average recognition rate for the 5 subjects of the global warming phenomenons has indicated to be 83.58%. A survey questioning about the main agent to reduce the greenhouse gas, in all age groups except 20s have replied that it should be done by themselves, the residents of Gangnam-gu. For the question of the role of local government to respond to the climate change, the necessity of establishing infrastructure which is suitable for walking and biking. For the other question about the educational facilities to cope with the climate change, many answered the relevant education should be processed from the middle and high schools. For the practical activities in daily life to respond to the climate change, many replies have shown that the energy and resource conservation has been practiced pretty well broadly, but the ecomileage (former carbon mileage) has not been practiced well. Also, many replies have pointed that there were no benefits or rewards for the people who practiced the eco-mileage in their daily lives, which indicates that a kind of incentive is necessary for the efforts to respond to the climate change from the local government to execute the policy substantially and effectively. This study has the purpose to search the political countermeasures to improve the potentiality to reduce the green house gas emission rate through the residents conscious survey about climate change and the political solution by the local government to improve the certain items which showed the lower awareness rate.

• Applied Chemistry for Engineering
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• v.30 no.2
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• pp.226-232
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• 2019

The biological wastewater treatment is more eco-friendly and can be used effectively in wastewater for a variety of purposes than that of the conventional treatment. In particular, the wastewater treatment using microalgae in biological treatment processes has attracted great attention due to its ability to remove economically nutrients from wastewater and have many advantages as a renewable energy source. This study was investigated to establish the optimal growth conditions for microalga Scenedesmus obliquus. Additionally, the removal efficiencies of nutrients (N, P) and heavy metals (Cu, Zn) from the synthetic wastewater were evaluated. As a results, the optimal growth conditions were established at $28^{\circ}C$, pH 7, and light and dark cycle of 14 : 10 h. In the evaluation of nutrient removal efficiencies at each concentrations of 500, 1,000, 5,000, and 10,000 mg/L, the removal rates were 17.6~70% N and 8.4~34% P in the single treatment and 12.0~58.0% N and 3.0~40.3% P in the binary mixture treatment. In addition, the evaluation of heavy metal removal efficiencies at each concentrations of 10, 30 and 50 mg/L, the removal rates were 13.7~40.3% Cu and 10.0~30.0% Zn in the single treatment and 16.0~40.0% Cu and 12.0~20.0% Zn in the binary mixture treatment. Based on the results of the study, it appears that Scenedesmus obliquus can be used for the removal of nutrients and heavy metals from the swine wastewater.

• Korean Journal of Ecology and Environment
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• v.55 no.1
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• pp.60-75
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• 2022

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.