Dongwon Ki

Bacterial biofilm-community selection during autohydrogenotrophic reduction of nitrate and perchlorate in ion-exchange brine

Three hydrogen-based membrane biofilm reactors (MBfR) biologically reduced nitrate and perchlorate in a synthetic ion-exchange (IX) brine. Inocula from different natural saline environments were employed to initiate the three MBfRs. Under high-salinity (3%) conditions, bioreduction of nitrate and perchlorate occurred simultaneously, and the three MBfRs from the different inocula exhibited similar removal fluxes for nitrate and perchlorate. Clone libraries were generated from samples of the biofilms in the three MBfRs and compared to those of their inocula. When H2 was the sole exogenous electron donor under high-salinity conditions, MBfR-driven community shifts were observed with a similar pattern regardless of inoculum. The following 16S rRNA gene phylogenetic analysis showed the presence of novel perchlorate-reducing bacteria in the salt-tolerant mBfR communities. These findings suggest that autohydrogenotrophic and high-salinity conditions provided strong selective pressure for novel perchlorate-reducing populations in the mBfRs.

Microbial diversity and population dynamics of activated sludge microbial communities participating in electricity generation in microbial fuel cells

In this study, we performed microbial community analysis to examine microbial diversity and community structure in microbial fuel cells (MFCs) seeded with activated sludge from a municipal wastewater treatment plant in South Korea. Because anode-attached biofilm populations are particularly important in electricity transfer, the ecological characteristics of anode-attached biofilm microbes were explored and compared with those of microbes grown in suspension in an anode chamber. 16S rDNA-based community analysis showed that the degree of diversity in anode-attached biofilms was greater than that of the originally seeded activated sludge as well as that of the suspension-grown microbes in the anode bottle. In addition, Bacteroidetes and Clostridia grew preferentially during MFC electricity generation. Further phylogenetic analysis revealed that the anode biofilm populations described in this work are phylogenetically distant from previously characterized MFC anode biofilm microbes. These findings suggest that a phylogenetically diverse set of microbes can be involved in the electricity generation of MFC anode compartments, and that increased microbial diversity in anode biofilms may help to stabilize electricity production in the MFC.

Using decision tree to develop a soil ecological quality assessment system for planning sustainable construction

Research highlights? This study proposes a soil ecological quality assessment system. ? The system uses forward and backward DT models under GIS-based spatial analysis. ? The system may examine conservation and development areas strictly. ? The prediction results can be used for planning mega-construction projects. Soil ecology is the foundation of the entire biosphere and plays a significant role in global ecosystems. Soil ecology is important in the decision-making aspects of mega-construction projects. Despite its significance, soil ecological quality is not normally included in environmental impact assessments for sustainable development. This study develops and presents a new expert system to assess soil microbial diversity as an indicator of soil ecology quality using decision tree (DT) algorithms and GIS (geographic information system)-based spatial analysis. Our modeling results show that forward and backward DT models provide development-oriented and conservation-oriented information maps. To resolve potential conflicts by the different model predictions, a new mapping approach was developed for identifying strict conservation and potential development areas. These results suggest that the newly developed soil ecological quality assessment system can be used for planning mega-construction projects.

Decision-Tree-based data mining and rule induction for predicting and mapping soil bacterial diversity

Soilmicrobial ecology plays a significant role in global ecosystems. Nevertheless, methods of model prediction and mapping have yet to be established for soil microbial ecology. The present study was undertaken to develop an artificial-intelligence- and geographical information system (GIS)-integrated framework for predicting and mapping soil bacterial diversity using pre-existing environmental geospatial database information, and to further evaluate the applicability of soil bacterial diversity mapping for planning construction of eco-friendly roads. Using a stratified random sampling, soil bacterial diversity was measured in 196 soil samples in a forest area where construction of an eco-friendly road was planned. Model accuracy, coherence analyses, and tree analysis were systematically performed, and four-class discretized decision tree (DT) with ordinary pair-wise partitioning (OPP) was selected as the optimal model among tested five DT model variants. GIS-based simulations of the optimal DT model with varying weights assigned to soil ecological quality showed that the inclusion of soil ecology in environmental components, which are considered in environmental impact assessment, significantly affects the spatial distributions of overall environmental quality values as well as the determination of an environmentally optimized road route. This work suggests a guideline to use systematic accuracy, coherence, and tree analyses in selecting an optimal DT model from multiple candidate model variants, and demonstrates the applicability of the OPP-improved DT integrated with GIS in rule induction for mapping bacterial diversity. These findings also provide implication on the significance of soil microbial ecology in environmental impact assessment and eco-friendly construction planning.

Effects of Operation Parameters on Pollutants Removal in a Lab-Scale Multi-Layered Soil Filtration System

In this study we investigated the effects of operational parameters of a multi-layered soil filtration (filter depth, filtration velocity, and continuous/intermittent operation) on removal of pollutants in river water. As filter depth increased removal of all the pollutants (COD, TP, TN, and NH4-N) was increased because the increase in filter depth increased in contact time between media and pollutants. The removal of TP and NH4-N more increased with the increase in filter depth, comparing to the biological COD removal which was performed only in the top layer, since the removal mechanism of TP and NH4-N was physicochemical process occurring throughout the whole layers. However, the reduction in filtration velocity resulted in decrease of removal all the pollutants removal due to shorter retention time. Biological COD removal was more influenced with the reduction in filtration velocity (longer retention time), than the removal of TP and NH4-N. Because biological process was occurred only in the top layer which has relatively shorter retention time, comparing with physicochemical process occurred throughout whole media. Therefore, it is desirable that the operation parameters be controlled toward increasing retention time, in order to achieve efficient pollutants removal. The change in operation mode (continuos vs. intermittent operations) did not provide significant effects on the pollutant treatment efficiency by the multi-layered soil filtration system. Our findings suggest that for stable long-term operation it should be considered keeping conditions for biological activity and accelerating clogging.