A new approach to characterize biodegradation of organics by molecular mass distribution in landfill leachate

This study provides biodegradability of organics in leachate according to their molecular mass distributions (<0.5, 0.5 to 1, 1 to 3, 10, and >10 KDa). Organics with molecular mass values lower than 0.5 KDa were the predominant species in the raw leachate filtrate, and the aerated lagoon process was very effective in treating these highly biodegradable organics; the Fenton's oxidation process was very effective in treating not-so-biodegradable organics with molecular mass values higher than 0.5 KDa, but a portion of these organics were converted into organics <0.5 KDa after Fenton's oxidation. An oxygen uptake measurement using a respirometer was more sensitive than a conventional biochemical oxygen demand measurement to evaluate bioactivities, especially when bioactivities were low.     

Remediation of petroleum-contaminated soils by fluidized thermal desorption

A novel type of fluidized bed desorber was developed for the remediation of petroleum-contaminated soils at low temperature with high efficiency. Cahn balance® was utilized to investigate the thermal desorption behavior of soils contaminated by various hydrocarbons. The performance of the fluidized-bed desorber was investigated at different operating modes. Batch operation of the fluidized-bed desorber exhibited 99.9% desorption efficiency at temperatures of ca. 300°C within a half hour. Continuous operation of the fluidized-bed indicated that Q/F (the ratio of the mass flow rate of fluidizing gas to feeding rate of contaminated soils) is less important at higher temperature (>300°C), if proper fluidization is ensured. The periodic operation of the fluidized bed desorber shows the possibility to reduce off-gas volume significantly.     

Compartment model of aerobic and anaerobic biodegradation in a municipal solid waste landfill

The mathematical formulations in a one-dimensional compartment model of the biodegradation of organic landfill components are described. The model is designed to switch between anaerobic and aerobic conditions, depending on the local oxygen concentration. The model also includes the effect of environmental factors, such as moisture content, pH, and temperature, on reaction rates. The model includes not only biodegradation processes for carbon compounds (acetate, CO2, CH4), but also for nitrogen compounds involved in nitrification and denitrification due to their significance in landfills. Two example runs to simulate anaerobic and aerobic waste were conducted for a single landfill unit cell by changing the organic content and diffusion coefficient.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

The politics of employment liability

Employment liability is an important check and balance against employee harm at work. In practice, however, an inadvertent consequence of employment liability is a potential shift in power from organizations to employees that affects subsequent managerial decision making. In this Incubator, we discuss behavioral and attitudinal ramifications of employment liability at work. Copyright © 2014 John Wiley & Sons, Ltd.     

A Modeling System to Assess Land Cover Land Use Change Effects on SAV Habitat in the Mobile Bay Estuary

Estuarine ecosystems are largely influenced by watersheds directly connected to them. In the Mobile Bay, Alabama watersheds we examined the effect of land cover and land use (LCLU) changes on discharge rate, water properties, and submerged aquatic vegetation, including freshwater macrophytes and seagrasses, throughout the estuary. LCLU scenarios from 1948, 1992, 2001, and 2030 were used to influence watershed and hydrodynamic models and evaluate the impact of LCLU change on shallow aquatic ecosystems. Overall, our modeling results found that LCLU changes increased freshwater flows into Mobile Bay altering temperature, salinity, and total suspended sediments (TSS). Increased urban land uses coupled with decreased agricultural/pasture lands reduced TSS in the water column. However, increased urbanization or agricultural/pasture land coupled with decreased forest land resulted in higher TSS concentrations. Higher sediment loads were usually strongly correlated with higher TSS levels, except in areas where a large extent of wetlands retained sediment discharged during rainfall events. The modeling results indicated improved water clarity in the shallow aquatic regions of Mississippi Sound and degraded water clarity in the Wolf Bay estuary. This integrated modeling approach will provide new knowledge and tools for coastal resource managers to manage shallow aquatic habitats that provide critical ecosystem services.     

Mini-review on river eutrophication and bottom improvement techniques,with special emphasis on the Nakdong River

Water quality in rivers is vital to humans and to maintenance of biotic and ecological integrity. During the Four Major Rivers restoration of South Korea, remarkable attempts have been made to decrease external nutrient loads and moveable weirs were designed to discharge silt that may deposit in pools. However, recently eutrophication of the Nakdong River, which was limited to the lower reaches, is seen to be spreading upstream. The reduction of external nutrient loads to rivers is a long-term goal that is unlikely to lead to reductions in algal blooms for many years because of the time required to implement effective land management strategies. It would therefore be desirable to implement complementary strategies. Regulating the amount of water released is effective at preventing algae blooms in weir pools; so, the relationship between discharge, stratification and bloom formation should be understood in this regard. However, pollutants are likely to accumulate in the riverbed upstream from release points. Thus, to control phosphorus levels, total phosphorus density should be lowered by applying in-river techniques as well. As many ecosystem properties are controlled by multiple processes, simultaneous river bottom improvement techniques, such as combined dissolved oxygen supply and nutrient inactivation, are likely to be effective. The purpose of this review is to present a series of technological approaches that can be used to improve the river bottom area and hence sediment nutrient release, and to illustrate the application of these techniques to the Nakdong River.     

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.