Nitrogen mass balance in a constructed wetland treating piggery wastewater effluent

The nitrogen changes and the nitrogen mass balance in a free water surface flow constructed wetland （CW） using the four-year monitoring data from 2008 to 2012 were estimated. The CW was composed of six cells in series that include the first settling basin （Cell 1）, aeration pond （Cell 2）, deep marsh （Cell 3）, shallow marsh （Cell 4）, deep marsh （Cell 5） and final settling basin （Cell 6）. Analysis revealed that the NH4＋-N concentration decreased because of ammonification which was then followed by nitrification. The NO4＋-N and NO4＋-N were also further reduced by means of microbial activities and plant uptake during photosynthesis. The average nitrogen concentration at the influent was 37,819 kg/year and approximately 45% of that amount exited the CW in the effluent. The denitrification amounted to 34% of the net nitrogen input, whereas the accretion of sediment was only 7%. The biomass uptake of plants was able to retain only 1% of total nitrogen load. In order to improve the nutrient removal by plant uptake, plant coverage in four cells （i.e., Cells 1, 3, 4 and 5） could be increased.     

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(R~2), 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.     

Risk assessment of particulate matter by considering time-activity-pattern and major microenvironments for preschool children living in Seoul,South Korea

Environmental Science and Pollution Research - Preschool children aged 3–6 years are vulnerable to exposed to particulate matter (“PM10” and “PM2.5”). It is required...     

PCDDs, PCDFs, and PCBs concentrations in breast milk from two areas in Korea: body burden of mothers and implications for feeding infants

We determined breast milk concentration of polychlorinated dibenzo-p-dioxins (PCDDs)/polychlorinated dibenzofurans (PCDFs) and polychlorinated biphenyls (PCBs) in 24 mothers living in Korea, and assessed the maternal body burden based on PCDDs/PCDFs and PCBs concentrations in breast milk and an infant intake rate through breast-feeding based on their concentration in breast milk. PCDDs/PCDFs and PCBs levels in breast milk from primipara mothers were found to be higher than those from multipara mothers. For total PCDDs/PCDFs TEQ level, 2,3,4,7,8-PeCDD was the predominant congener, and the proportion of 2,3,7,8-TCDD was less than 3% of total PCDDs/PCDFs TEQ level. For PCBs TEQ level, PCB-126 was the predominant congener. Maternal body burden levels of PCDDs/PCDFs and PCBs based on their concentrations in breast milk were 268-622 TEQ ng. The daily dioxin intakes of mothers were predicted to be 0.78-2.18 TEQ pg/kg/day for PCDDs/PCDFs and 0.34-0.66 TEQ pg/kg/ day for PCBs. For the first year, the body burden of an infant was predicted to be 212 TEQ ng and the daily intake of an infant was predicted to be 85 TEQ pg/kg/day, assuming the mean dioxin-related compounds concentration (27.54 TEQ pg/g fat).     

Assessment of Human Health Risk of Dioxin in Korea

This study assessed the daily exposure to dioxin using comparable approach methods in order to predict the human health risks of dioxin on the general adult population and incinerator workers in Korea. The health risk assessment of dioxin involved four scenarios (General-Direct, General-Breast Milk, General-Blood, and Worker-Blood). The risks were described in terms of the excess cancer risk and the MOEs (Margin-Of-Exposure) for cancer, reproductive dysfunctions, endometriosis, and neurobehavioral effects. The adult daily intake (General-Direct) of dioxin was estimated to be 0.50 pg-TEQ/kg-day based on the dioxin concentrations in the environmental media and foods in Korea. The average dioxin concentrations in General-Breast Milk, General-Blood, and Worker-Blood were 15.13 pg-TEQ/g fat, 14.57 pg-TEQ/g lipid, and 22.90 pg-TEQ/g lipid, respectively. The lifetime average daily doses (LADDs) of dioxin for General-Breast Milk, General-Blood, and Worker-Blood were estimated to be 1.35 pg-TEQ/kg day, 0.99 pg-TEQ/kg day, and 1.15 pg-TEQ/kg day, respectively. Although the estimated risks of cancer and reproductive dysfunctions were not unusually high in any of the four scenarios, the MOE values for the neurobehavioral effects on infants were not sufficiently high to guarantee the safety against this endpoint.     

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.     

Analysis of the compensation system at the Environmental-Adverse-Effect Zone of a large-scale waste landfill site

Sudokwon landfill is the largest sanitary landfill in South Korea. The Environmental-Adverse-Effect Zone (EAEZ) from the boundary of a Sudokwon landfill site was set up to give the community compensation by a landfill developer to support special financial favors to every household resident. One group of residents in the EAEZ would continually receive the community compensation after 2010 and the other group would not because of changing the landfill site in 2009. These situations in Sudokwon landfill raise the question of whether the cash payment to residents for the community compensation may increase the acceptance of the waste landfill site and whether ceasing community compensation may create new Not in My Backyard (NIMBY) syndrome concerning landfill operations. Answers from questionnaires showed that 279 respondents (Group A) had experienced Resident Supportive Projects (RSP) and the other (86 respondents, Group B) had not. Most of the respondents (more than 80?%) in Group A who had received compensation answered that the sort of RSP was the direct cash payment. 37?% of respondents in Group A and 29?% of all respondents reported RSP as helpful. The 24?% of respondents who lived in the area released from the EAEZ in 2009 were ‘very reduced’ or ‘reduced’, while the corresponding results were 38?% in the continued EAEZ area. These different responses were statistically significant. And many respondents (70.6?%) recognized that the level of RSP (especially the monetary payment) was not enough. This ratio is unusual compared with a previous result (Lee PhD thesis, 2001), which was 6.2?% in 2000. The relative ratio of respondents living in the continued EAEZ area answered ‘very high’ or ‘high’ on the questions of knowledge regarding the sort of RSP, RSP cost, satisfaction of public participation, the process to decide the RSP, and the helpfulness of the RSP, which are statistically significant. In addition, respondents wanted the projects for welfare and resident convenience facilities with the same ratio (29.7?%) among several projects. These results suggest that the effect of the direct cash payment on the response of residents is similar to North America and Europe. Additionally, long periods of support of the public projects to develop resident communities may be required in order to improve resident acceptance of sitting landfill.     

Simplified LCA and matrix methods in identifying the environmental aspects of a product system

In order to effectively integrate environmental attributes into the product design and development processes, it is crucial to identify the significant environmental aspects related to a product system within a relatively short period of time. In this study, the usefulness of life cycle assessment (LCA) and a matrix method as tools for identifying the key environmental issues of a product system were examined. For this, a simplified LCA (SLCA) method that can be applied to Electrical and Electronic Equipment (EEE) was developed to efficiently identify their significant environmental aspects for eco-design, since a full scale LCA study is usually very detailed, expensive and time-consuming. The environmentally responsible product assessment (ERPA) method, which is one of the matrix methods, was also analyzed. Then, the usefulness of each method in eco-design processes was evaluated and compared using the case studies of the cellular phone and vacuum cleaner systems. It was found that the SLCA and the ERPA methods provided different information but they complemented each other to some extent. The SLCA method generated more information on the inherent environmental characteristics of a product system so that it might be useful for new design/eco-innovation when developing a completely new product or method where environmental considerations play a major role from the beginning. On the other hand, the ERPA method gave more information on the potential for improving a product so that it could be effectively used in eco-redesign which intends to alleviate environmental impacts of an existing product or process.     

Flow and mass balance analysis of eco-bio infiltration system

A structured stormwater infiltration system was developed and constructed at a university campus and monitoring of storm events was performed during a one-year operation period. The flow and pollutant mass balances were analyzed and the overall efficiency of the system was assessed. While significant positive correlations were observed among rainfall, runoff and discharge volume (R²= 0.93-0.99; p<0.05), there was no significant correlations existed between rainfall, runoff, discharge volume and pollutant load. The system was more effective in reducing the runoff volume by more than 50% for small storm events but the difference between the runoff and discharge volume was significant even with rainfall greater than 10 mm. Results showed that the pollutant reduction rates were higher compared to the runoff volume reduction. Average pollutant reduction rates were in the range of 72% to 90% with coefficient of variation between 0.10 and 0.46. Comparable with runoff reduction, the system was more effective in reducing the pollutant load for small storm events, in the range of 80% to 100% for rainfall between 0 and 10 mm; while 65% to 80% for rainfall between 10 and 20 mm. Among the pollutant parameters, particulate matters was highly reduced by the system achieving only a maximum of 25% discharge load even after the entire runoff was completely discharged. The findings have proven the capability of the system as a tool in stormwater management achieving both flow reduction and water quality improvement.