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.     

Inhalation of concentrated ambient particulate matter near a heavily trafficked road stimulates antigen-induced airway responses in mice

The goal of this study was to test the following hypotheses: (1) exposure to mobile emissions from mobile sources close to a heavily trafficked roadway will exacerbate airway inflammation and allergic airway responses in a sensitized mouse model, and (2) the magnitude of allergic airway disease responses will decrease with increasing distance from the roadway. A particle concentrator and a mobile exposure facility were used to expose ovalbumin (OVA)-sensitized BALB/c mice to purified air and concentrated fine and concentrated ultrafine ambient particles at 50 m and 150 m downwind from a roadway that was heavily impacted by emissions from heavy duty diesel-powered vehicles. After exposure, we assessed interleukin (IL)-5, IL-13, OVA-specific immunoglobulin E, OVA-specific immunoglobulin G1, and eosinophil influx as biomarkers of allergic responses and numbers of polymorphonuclear leukocytes as a marker of inflammation. The study was performed over a two-year period, and there were differences in the concentrations and compositions of ambient particulate matter across those years that could have influenced our results. However, averaged over the two-year period, exposure to concentrated ambient particles (CAPs) increased the biomarkers associated with airway allergies (IL-5, immunoglobulin E, immunoglobulin G1 and eosinophils). In addition, mice exposed to CAPs 50 m downwind of the roadway had, on the average, greater allergic responses and showed greater indications of inflammation than did mice exposed to CAPs 150 m downwind. This study is consistent with the hypothesis that exposure to CAPs close to a heavily trafficked roadway influenced allergic airway responses.     

Control of bacterial growth in water using synthesized inorganic disinfectant

Chlorine is a popular method for controlling bacterial growth in cooling towers. However, there are several drawbacks such as the difficulties in maintaining the disinfection efficacy particularly at high temperature and pH. In order to overcome these difficulties, an inorganic disinfectant based on silver and copper, which is called EEKO-BALL (commercial name), was recently developed. EEKO-BALL is made from specific ceramics and coating materials. This study was performed to evaluate the efficacy of EEKO-BALL and compare it with that of silver (Ag+) and copper (Cu2+) ions. In addition, a field study was undertaken to investigate the control of bacterial growth in several cooling tower systems. The results showed that the contact time required for inactivating 99% of E. coli was 15 min in the EEKO-BALL stock solution at 25 degrees C, pH 7.3, with 0.05 mgl(-1) Ag and 0.05 mgl(-1) Cu. EEKO-BALL was approximately four times more effective than silver and copper ions in inactivating E. coli at 25 degrees C, pH 7.3. The control of bacterial growth in the cooling towers was found to be effective, lasting more than two months after a one-time installation of the EEKO-BALL. Overall, this study suggests that EEKO-BALL can effectively work as an inorganic disinfectant for bacterial growth control.     

Controlling various contaminants in wastewater effluent through membranes and engineered wetland

For effective wastewater reclamation and water recovery, the treatment of natural and effluent organic matters (NOM and EfOM), toxic anions, and micropollutants was considered in this work. Two different NOM (humic acid of the Suwannee River, and NOM of US and Youngsan River, Korea), and one EfOM from the Damyang wastewater treatment plant, Korea, were selected for investigating the removal efficiencies of tight nanofiltration (NF) and ultrafiltration (UF) membranes with different properties. Nitrate, bromate, and perchlorate were selected as target toxic anions due to their well known high toxicities. Tri-(2-chloroethyl)-phosphate (TCEP), oxybenzone, and caffeine, due to their different K _ow and pK _a values, were selected as target micropollutants. As expected, the NF membranes provided high removal efficiencies in terms of all the tested contaminants, and the UF membrane provided fairly high removal efficiencies for anions (except for nitrate) and the relatively hydrophobic micropollutant, oxybenzon. Through the wetlands, nitrate was successfully removed. Therefore, a fair process of combining membranes with an engineered wetland could be proposed for sustainable wastewater reclamation and optimum control of contaminats.     

Surface-enhanced Raman spectroscopy for emerging contaminant analysis in drinking water

● Definition of emerging contaminants in drinking water is introduced. ● SERS and standard methods for emerging contaminant analysis are compared. ● Enhancement factor and accessibility of SERS hot spots are equally important. ● SERS sensors should be tailored according to emerging contaminant properties. ● Challenges to meet drinking water regulatory guidelines are discussed. Emerging contaminants (ECs) in drinking water pose threats to public health due to their environmental prevalence and potential toxicity. The occurrence of ECs in our drinking water supplies depends on their physicochemical properties, discharging rate, and susceptibility to removal by water treatment processes. Uncertain health effects of long-term exposure to ECs justify their regular monitoring in drinking water supplies. In this review article, we will summarize the current status and future opportunities of surface-enhanced Raman spectroscopy (SERS) for EC analysis in drinking water. Working principles of SERS are first introduced and a comparison of SERS and liquid chromatography-tandem mass spectrometry in terms of cost, time, sensitivity, and availability is made. Subsequently, we discuss the strategies for designing effective SERS sensors for EC analysis based on five categories—per- and polyfluoroalkyl substances, novel pesticides, pharmaceuticals, endocrine-disrupting chemicals, and microplastics. In addition to maximizing the intrinsic enhancement factors of SERS substrates, strategies to improve hot spot accessibilities to the targeting ECs are equally important. This is a review article focusing on SERS analysis of ECs in drinking water. The discussions are not only guided by numerous endeavors to advance SERS technology but also by the drinking water regulatory policy.