• Economic and Environmental Geology
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• v.51 no.2
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• pp.87-97
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• 2018

The cesium (Cs) removal from the contaminated water system has been considered to be difficult because the cesium likes to exist as soluble phases such as ion and complexes than the solid in water system. Many researches have focused on developing the breakthrough adsorbent to increase the cesium removal efficiency in water. In this study, the laboratory scale experiments were performed to investigate the feasibility of the adsorption process using the bamboo charcoal for the Cs contaminated water system. The Cs removal efficiency of the bamboo charcoal were measured and the optimal adsorption conditions were determined by the adsorption batch experiments. Total 5 types of commercialized bamboo charcoals in Korea were used to identify their surface properties from SEM-EDS and XRD analyses and 3 types of bamboo charcoals having large specific surface areas were used for the adsorption batch experiment. The batch experiments to calculate the Cs removal efficiency were performed at conditions of various Cs concentration (0.01 - 10 mg/L), pH (3 - 11), temperature ($5-30^{\circ}C$), and adsorption time (10 - 120 min.). Experimental results were fitted to the Langmuir adsorption isotherm curve and their adsorption constants were determined to understand the adsorption properties of bamboo charcoal for Cs contaminated water system. From results of SEM-EDS analyses, the surfaces of bamboo charcoal particles were composed of typical fiber structures having various pores and dense lamella structures in supporting major adsorption spaces for Cs. From results of adsorption batch experiments, the Cs-133 removal efficiency of C type bamboo charcoal was the highest among those of 3 bamboo charcoal types and it was higher than 75 % (maximum of 82 %) even when the initial Cs concentration in water was lower than 1.0 mg/L, suggesting that the adsorption process using the bamboo charcoal has a great potential to remove Cs from the genuine Cs contaminated water, of which Cs concentration is low (< 1.0 mg/L) in general. The high Cs removal efficiency of bamboo charcoal was maintained in a relatively wide range of temperatures and pHs, supporting that the usage of the bamboo charcoal is feasible for various types of water. Experimental results were similar to the Langmuir adsorption model and the maximum amount of Cs adsorption (qm:mg/g) was 63.4 mg/g, which was higher than those of commercialized adsorbents used in previous studies. The surface coverage (${\theta}$) of bamboo charcoal was also maintained in low when the Cs concentration in water was < 1.0 mg/L, investigating that the Cs contaminated water can be remediated up with a small amount of bamboo charcoal.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.833-842
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• 2013

The quantile mapping is utilized to reproduce reliable GCM(Global Climate Model) data by correct systematic biases included in the original data set. This scheme, in general, projects the Cumulative Distribution Function (CDF) of the underlying data set into the target CDF assuming that parameters of target distribution function is stationary. Therefore, the application of stationary quantile mapping for nonstationary long-term time series data of future precipitation scenario computed by GCM can show biased projection. In this research the Nonstationary Quantile Mapping (NSQM) scheme was suggested for bias correction of nonstationary long-term time series data. The proposed scheme uses the statistical parameters with nonstationary long-term trends. The Gamma distribution was assumed for the object and target probability distribution. As the climate change scenario, the 20C3M(baseline scenario) and SRES A2 scenario (projection scenario) of CGCM3.1/T63 model from CCCma (Canadian Centre for Climate modeling and analysis) were utilized. The precipitation data were collected from 10 rain gauge stations in the Han-river basin. In order to consider seasonal characteristics, the study was performed separately for the flood (June~October) and nonflood (November~May) seasons. The periods for baseline and projection scenario were set as 1973~2000 and 2011~2100, respectively. This study evaluated the performance of NSQM by experimenting various ways of setting parameters of target distribution. The projection scenarios were shown for 3 different periods of FF scenario (Foreseeable Future Scenario, 2011~2040 yr), MF scenario (Mid-term Future Scenario, 2041~2070 yr), LF scenario (Long-term Future Scenario, 2071~2100 yr). The trend test for the annual precipitation projection using NSQM shows 330.1 mm (25.2%), 564.5 mm (43.1%), and 634.3 mm (48.5%) increase for FF, MF, and LF scenarios, respectively. The application of stationary scheme shows overestimated projection for FF scenario and underestimated projection for LF scenario. This problem could be improved by applying nonstationary quantile mapping.