Screening of Cucumis sativus as a new arsenic-accumulating plant and its arsenic accumulation in hydroponic culture

Phytoextraction is a remediation technology with a promising application for removing arsenic (As) from soils and waters. Several plant species were evaluated for their As accumulation capacity in hydroponic culture amended with As. Cucumis sativus (cucumber) displayed the highest tolerance against As among 4 plants tested in this study (corn, wheat, sorghum and cucumber). The germination ratio of Cucumis sativus was more than 50% at the high concentration of 5,000 mg-As/l. In Cucumis sativus grown in a solution contaminated with 25 mg-As/l, the accumulated As concentrations in the shoot and root were 675.5 ± 11.5 and 312.0 ± 163.4 mg/kg, respectively, and the corresponding values of the translocation and bioaccumulation factors for As were 1.9 ± 0.9 and 21.1 ± 8.4, respectively. These results indicate Cucumis sativus is to be a candidate plant for phytoextraction of As from soils and water.     

Potential application of sludge produced from coal mine drainage treatment for removing Zn(II) in an aqueous phase

Various analyses of physico-chemical characteristics and batch tests were conducted with the sludge obtained from a full-scale electrolysis facility for treating coal mine drainage in order to find the applicability of sludge as a material for removing Zn(II) in an aqueous phase. The physico-chemical analysis results indicated that coal mine drainage sludge (CMDS) had a high specific surface area and also satisfied the standard of toxicity characteristic leaching procedure (TCLP) because the extracted concentrations of certain toxic elements such as Pb, Cu, As, Hg, Zn, and Ni were much less than their regulatory limits. The results of X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) showed that the CMDS mainly consists of goethite (70%) and calcite (30%) as a weight basis. However, the zeta potential analysis represented that the CMDS had a lower isoelectric point of pH (pH_IEP) than that of goethite or calcite. This might have been caused by the complexation of negatively charged anions, especially sulfate, which usually exists with a high concentration in coal mine drainage. The results of Fourier transform infrared (FT-IR) spectrometry analysis revealed that Zn(II) was dominantly removed as a form of precipitation by calcite, such as smithsonite [ZnCO₃] or hydrozincite [Zn₅(CO₃)₂(OH)₆]. Recycling sludge, originally a waste material, for the removal process of Zn(II), as well as other heavy metals, could be beneficial due to its high and speedy removal capability and low economic costs.     

Commercial landscape of noninvasive prenatal testing in the United States

Cell-free fetal DNA-based noninvasive prenatal testing (NIPT) could significantly change the paradigm of prenatal testing and screening. Intellectual property (IP) and commercialization promise to be important components of the emerging debate about clinical implementation of these technologies. We have assembled information about types of testing, prices, turnaround times, and reimbursement of recently launched commercial tests in the United States from the trade press, news articles, and scientific, legal, and business publications. We also describe the patenting and licensing landscape of technologies underlying these tests and ongoing patent litigation in the United States. Finally, we discuss how IP issues may affect clinical translation of NIPT and their potential implications for stakeholders. Fetal medicine professionals (clinicians and researchers), genetic counselors, insurers, regulators, test developers, and patients may be able to use this information to make informed decisions about clinical implementation of current and emerging noninvasive prenatal tests. © 2013 John Wiley & Sons, Ltd.     

Comparisons of human risk assessment models for heavy metal contamination within abandoned metal mine areas in Korea

Environmental Geochemistry and Health - This study was initiated to develop a model specialized to conduct human risk assessments (HRAs) of abandoned metal mine areas in Korea. The Korean guideline...     

Effects of a dynamic membrane formed with polyethylene glycol on the ultrafiltration of natural organic matter

The formation of a dynamic membrane (DM) was investigated using polyethylene glycol (PEG) (molecular weight of 35000 g/mol, concentration of 1 g/L). Two natural organic matters (NOM), Dongbok Lake NOM (DLNOM) and Suwannee River NOM (SRNOM) were used in the ultrafiltration experiments along with PEG. To evaluate the effects of the DM with PEG on ultrafiltration, various transport experiments were conducted, and the analyses of the NOM in the membrane feed and permeate were performed using high performance size exclusion chromatography, and the effective pore size distribution (effective PSD) and effective molecular weight cut off (effective MWCO) were determined. The advantages of DM formed with PEG can be summarized as follows: (1) PEG interferes with NOM transmission through the ultrafiltration membrane pores by increasing the retention coefficient of NOM in UF membranes, and (2) low removal of NOM by the DM is affected by external factors, such as pressure increases during UF membrane filtration, which decreases the effective PSD and effective MWCO of UF membranes. However, a disadvantage of the DM with PEG was severe flux decline; thus, one must be mindful of both the positive and negative influences of the DM when optimizing the UF performance of the membrane.     

Evolutionary governance choice for corporate social responsibility: a forestry campaign case in South Korea

Firms select different governance types (e.g. internalisation, outsourcing or collaboration) for corporate social responsibility (CSR) activities. This paper examined how the choice of CSR governance type is influenced by social factors and organisational experiences and how changes in these factors affect the governance choice. These questions are especially meaningful in the context of developing countries with weak institutional environments and recent social change. With the unique phenomenon that is not readily explainable in terms of the extant theoretical groundwork on CSR governance choice, a case study of a South Korean forestry campaign by Yuhan-Kimberly was analysed. It was found that social circumstances triggered Yuhan-Kimberly's initiation of forestry projects, but the absence of social infrastructure limited the choice of governance type early on. Lack of experience also constrained the governance choice. Over time, environmental change and the accumulation of organisational knowledge caused the choice of governance to evolve. This study contributes to the CSR governance choice literature by exploring social and organisational determinants other than economic efficiency and by applying a dynamic perspective.     

Sorption and biodegradation of vapor-phase organic compounds with wastewater sludge and food waste compost

To test the possible use of composted food waste and wastewater sludge as biofilters to treat gas-phase volatile organic compounds (VOCs), batch experiments were conducted with an isolated strain that could degrade aromatic compounds under aerobic conditions. A benzene and trichloroethylene (TCE) mixture was used as the gas-phase pollutant in experiments with composted food waste, sludge, and soil. Under aerobic conditions, benzene was degraded as a primary substrate and TCE was degraded cometabolically, with water contents varying from 6 to 60% (volume of water added/volume of solid). Optimal water content for VOC removal was 12% for the soil, 36% for the composted food waste, and 48% for the sludge. The extent of VOC sorption and biodegradation at the optimal water content was different for each material. With the same initial VOC concentration, more VOCs were removed by sorption onto the composted food waste and the sludge, while less VOCs were biodegraded in comparison with the results using soil. The reason the biodegradation in the soil was greater may be partly attributed to the fact that, due to less sorption, the aqueous-phase concentration of VOCs, which microorganisms could utilize as a carbon source or cometabolize, was higher. We also speculate that the distribution of microorganisms in each medium affects the rate of biodegradation. A large number of microorganisms were attached to the composted food waste and sludge. Mass transfer of VOCs and oxygen to these microorganisms, which appear to have been heterogeneously distributed in clusters, may have been limited, resulting in hindered biodegradation.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen (T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks (ANNs) and support vector machines (SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination (R²), Nash–Sutcliff efficiency (NSE), relative efficiency criteria (d_rel). Additionally, Latin-Hypercube one-factor-at-a-time (LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage. However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.