• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.262-273
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• 2020

Amending biopolymers such as β-glucan (BG) and Xanthan gum (XG) generally enhances soil strength by ionic and hydrogen bonds between soil particles. Thus, biopolymers have been studied as eco-friendly construction materials in levees. However, physiological responses of plants grown on soil amended with biopolymers are not fully understood. This study focuses on the effects of biopolymers on the growth of Camelina sativa L. (Camelina) under excess zinc (Zn) stress. The optimal concentrations of BG and XG were confirmed to have a 0.5% ratio in soil depending on the physiological parameters of Camelina under excess Zn stress. The Zn binding capacity of biopolymers was investigated using 1,5-diphenylthiocarbazone (DTZ). The reduction of Zn damage in Camelina was evaluated by analyzing the Zn content and expression of heavy metal ATPase (HMA) genes under excess Zn stress. Amendments of BG and XG improved Camelina growth under excess Zn stress. In DTZ staining and ICP-OES analysis, Camelina grown on BG and XG soil showed less Zn uptake than normal soil under excess Zn stress. The Zn-inducible CsHMA3 gene was not stimulated by either BG or XG amendment under excess Zn stress. Moreover, both BG and XG amendments in soil exhibit Zn-stress mitigation similar to that of Zn-tolerant CsHMA3 overexpres sed Camelina. These results indicate that biopolymer-amended soils may influence the prevention of Zn absorption in Camelina under excess Zn stress. Thus, BG and XG are proven to be suitable materials for levee construction and can protect plants from soil contamination by Zn.

• Korean Journal of Ecology and Environment
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• v.54 no.1
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• pp.24-38
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• 2021

Microhabitat In the upper stream is created by various environment variables such as the bottom substrate and the physicochemical factors, and may influence the distribution of benthic macroinvertebrates. We investigated the bottom substrate and environmental variables influencing the distribution of benthic macroinvertebrate in 26 stream-type waterways established at upper reaches of Geum River. During study period, total 85 families, 160 species, 9305 individuals of benthic macroinvertebrates were recorded. The stream-type waterways, where the bottom substrates consist mainly of pebble (16~64 mm) and cobble (64~256 mm) or with rapid water velocity (more than 0.2 m/s) and high dissolved oxygen (more than 120%), were supported by high species diversity of benthic macroinvertebrate. Hierological cluster analysis and the nonparametric multidimensional scale (NMDS) divided 26 stream-type waterways into a total of three clusters. In Cluster 1, the invertebrate species, such as Branchiura sowerbyi, Cloeon dipterum, Ischnura asiatica, Paracercion calamorum, and Radix auricularia, closely related to aquatic macrophytes, and Chironomidae spp., Limnodrilus gotoi, and Tanypodinae sp. were abundant in waterways, with high coverage of silt and clay as well as high turbidity and total nitrogen. The benthic macroinvertebrate species (Cheumatopsyche brevilineata, Drunella ishiyamana, Dugesia japonica, Ephemera orientalis, Gumaga KUa, Macrostemum radiatum, Potamanthus formosus, Semisulcospira libertine, Stenelmis vulgaris, and Teloganopsis punctisetae) included in Cluster 2 were dominated in sites with high cover rates of pebble and gravel. Cluster 3 was predominantly covered by the Cobbles, was supported by Simulium sp. Such a clear distinction in the study sites means that each stream-type waterways is governed by a clear habitat environment. In the case of some sites with low species diversity, improvement measures are required to restore nature, such as improving the function of inflows and outflows, creating meandering channel, and inducing the settlement of littoral vegetation.

• Economic and Environmental Geology
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• v.54 no.6
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• pp.689-698
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• 2021

Since the mine reclamation scheme was implemented from 2007 in Korea, various remediation programs have been decontaminated the pollution associated with mining and 254 mines were managed to reclamation from 2011 to 2015. However, as the total amount of contaminated mine drainage has been increased due to the discovery of potential hazards and contaminated zone, more efficient and economical treatment technology is required. Therefore, in this study, the adsorption properties of arsenic was evaluated according to the adsorbents which were derived from water treatment sludge(Alum based adsorbent, ABA-500) and granular ferric hydroxide(GFH), already commercialized. The alum sludge and GFH adsorbents consisted of aluminum, silica materials and amorphous iron hydroxide, respectively. The point of zero charge of ABA-500 and GFH were 5.27 and 6.72, respectively. The result of the analysis of BET revealed that the specific surface area of GFH(257 m²·g^-1) was larger than ABA-500(126~136 m²·g^-1) and all the adsorbents were mesoporous materials inferred from N₂ adsorption-desorption isotherm. The adsorption capacity of adsorbents was compared with the batch experiments that were performed at different reaction times, pH, temperature and initial concentrations of arsenic. As a result of kinetic study, it was confirmed that arsenic was adsorbed rapidly in the order of GFH, ABA-500(granule) and ABA-500(3mm). The adsorption kinetics were fitted to the pseudo-second-order kinetic model for all three adsorbents. The amount of adsorbed arsenic was increased with low pH and high temperature regardless of adsorbents. When the adsorbents reacted at different initial concentrations of arsenic in an hour, ABA-500(granule) and GFH could remove the arsenic below the standard of drinking water if the concentration was below 0.2 mg·g^-1 and 1 mg·g^-1, respectively. The results suggested that the ABA-500(granule), a low-cost adsorbent, had the potential to field application at low contaminated mine drainage.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.543-551
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• 2023

Snow is an essential climate factor that affects the climate system and surface energy balance, and it also has a crucial role in water balance by providing solid water stored during the winter for spring runoff and groundwater recharge. In this study, statistical analysis of Local Data Assimilation and Prediction System (LDAPS), Modern.-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and ERA5-Land snow depth data were used to evaluate the applicability in South Korea. The statistical analysis between the Automated Synoptic Observing System (ASOS) ground observation data provided by the Korea Meteorological Administration (KMA) and the reanalysis data showed that LDAPS and ERA5-Land were highly correlated with a correlation coefficient of more than 0.69, but LDAPS showed a large error with an RMSE of 0.79 m. In the case of MERRA-2, the correlation coefficient was lower at 0.17 because the constant value was estimated continuously for some periods, which did not adequately simulate the increase and decrease trend between data. The statistical analysis of LDAPS and ASOS showed high and low performance in the nearby Gangwon Province, where the average snowfall is relatively high, and in the southern region, where the average snowfall is low, respectively. Finally, the error variance between the four independent snow depth data used in this study was calculated through triple collocation (TC), and a merged snow depth data was produced through weighting factors. The reanalyzed data showed the highest error variance in the order of LDAPS, MERRA-2, and ERA5-Land, and LDAPS was given a lower weighting factor due to its higher error variance. In addition, the spatial distribution of ERA5-Land snow depth data showed less variability, so the TC-merged snow depth data showed a similar spatial distribution to MERRA-2, which has a low spatial resolution. Considering the correlation, error, and uncertainty of the data, the ERA5-Land data is suitable for snow-related analysis in South Korea. In addition, it is expected that LDAPS data, which is highly correlated with other data but tends to be overestimated, can be actively utilized for high-resolution representation of regional and climatic diversity if appropriate corrections are performed.