A panel study on the relationship between biofuels production and sustainable development

Biomass is one of the renewable energy sources on which policy makers are greatly dependent on since it is a flexible feedstock capable of conversion into electricity, transport liquid fuels and heat by chemical and biological processes on demand. Though numerous publications have examined the relationship of economic growth with renewable energy and other parameters, biomass energy has never been included in these studies. Then, this study examines the causal relationship within a multivariate panel cointegration/error correction framework which combines the cross-section and time series data while allowing for heterogeneity across different provinces. After employing panel data regression model ranging from 2003 through 2012 to examine the relationships of biofuels production with sustainable development in China, the paper concludes that the development of biofuel energy production integrated with the consideration of the improvement of income per capita, and the attraction of more capital investment, does make a significant contribution to economic growth. However, some negative side effects including the increase of greenhouse emissions and the decrease of marginal land still coexist with the economic development. Of course, the importance of these findings lies on their implications and their adoption on strategic policies.     

Current status and historical variations of phthalate ester (PAE) contamination in the sediments from a large Chinese lake (Lake Chaohu)

The residual levels of phthalate esters (PAEs) in the surface and two core sediments from Lake Chaohu were measured with a gas chromatograph–mass spectrometer (GC–MS). The temporal–spatial distributions, compositions of PAEs, and their effecting factors were investigated. The results indicated that di-n-butyl phthalate (DnBP), diisobutyl phthalate (DIBP), and di(2-ethylhexyl) phthalate (DEHP) were three dominant PAE components in both the surface and core sediments. The residual level of total detected PAEs (∑PAEs) in the surface sediments (2.146?±?2.255 μg/g dw) was lower than that in the western core sediments (10.615?±?9.733 μg/g) and in the eastern core sediments (5.109?±?4.741 μg/g). The average content of ∑PAEs in the surface sediments from the inflow rivers (4.128?±?1.738 μg/g dw) was an order of magnitude higher than those from the lake (0.323?±?0.093 μg/g dw), and there were similar PAE compositions between the lake and inflow rivers. This finding means that there were important effects of PAE input from the inflow rivers on the compositions and distributions of PAEs in the surface sediments. An increasing trend was found for the residual levels of ΣPAEs, DnBP, and DIBP from the bottom to the surface in both the western and eastern core sediments. Increasing PAE usage with the population growth, urbanization, and industrial and agricultural development in Lake Chaohu watershed would result in the increasing production of PAEs and their resulting presence in the sediments. The significant positive relationships were also found between the PAE contents and the percentage of sand particles, as well as TOC contents in the sediment cores.     

Toward better understanding and feasibility of controlling greenhouse gas emissions from treatment of industrial wastewater with activated sludge

Wastewater treatment plants (WWTPs) have been recognized as important sources for anthropogenic greenhouse gas (GHG) emission. The objective of the study was to thoroughly investigate a typical industrial WWTP in southern Taiwan in winter and summer which possesses the emission factors close to those reported values, with the analyses of emission factors, mass fluxes, fugacity, lab-scale in situ experiments, and impact assessment. The activated sludge was the important source in winter and summer, and nitrous oxide (N₂O) was the main contributor (e.g., 57 to 91 % of total GHG emission in a unit of kg carbon dioxide-equivalent/kg chemical oxygen demand). Albeit important for the GHGs in the atmosphere, the fractional contribution of the GHG emission to the carbon or nitrogen removal in wastewater treatment was negligible (e.g., less than 1.5 %). In comparison with the sludge concentration or retention time, adjusting the aeration rate was more effective to diminish the GHG emission in the activated sludge without significantly affecting the treated water quality. When the aeration rate in the activated sludge simulation was reduced by 75 %, the mass flux of N₂O could be diminished by up to 53 % (from 9.6 to 4.5 mg/m²-day). The total emission in the WWTP (including carbon dioxide, methane, and N₂O) would decrease by 46 % (from 0.67 to 0.36 kg CO₂-equiv/kg COD). However, the more important benefit of changing the aeration rate was lowering the energy consumption in operation of the WWTP, as the fractional contribution of pumping to the total emission from the WWTP ranged from 46 to 93 % within the range of the aeration rate tested. Under the circumstance in which reducing the burden of climate change is a global campaign, the findings provide insight regarding the GHG emission from treatment of industrial wastewater and the associated impact on the treatment performance and possible mitigation strategies by operational modifications.

     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

Hydroponic root mats for wastewater treatment—a review

Hydroponic root mats (HRMs) are ecotechnological wastewater treatment systems where aquatic vegetation forms buoyant filters by their dense interwoven roots and rhizomes, sometimes supported by rafts or other floating materials. A preferential hydraulic flow is created in the water zone between the plant root mat and the bottom of the treatment system. When the mat touches the bottom of the water body, such systems can also function as HRM filter; i.e. the hydraulic flow passes directly through the root zone. HRMs have been used for the treatment of various types of polluted water, including domestic wastewater; agricultural effluents; and polluted river, lake, stormwater and groundwater and even acid mine drainage. This article provides an overview on the concept of applying floating HRM and non-floating HRM filters for wastewater treatment. Exemplary performance data are presented, and the advantages and disadvantages of this technology are discussed in comparison to those of ponds, free-floating plant and soil-based constructed wetlands. Finally, suggestions are provided on the preferred scope of application of HRMs.     

Efficient degradation of rhodamine B using modified graphite felt gas diffusion electrode by electro-Fenton process

The electro-Fenton (EF) process treatment of 0.1-M (rhodamine B) RhB solution was studied with different graphite cathode materials, and graphite felt (GF) was selected as a promising material in further investigation. Then, the degradation performances of gas diffusion electrode (GDE) and graphite felt (GF) were compared, and GDE was confirmed to be more efficient in RhB removal. The operational parameters such as Fe²⁺ dosage and current density were optimized, and comparison among different modified methods—polytetrafluoroethylene-carbon black (PTFE-CB), polytetrafluoroethylene-carbon nanotube (PTFE-CNT), electrodeposition-CB, and electrodeposition-CNT—showed 98.49 % RhB removal by PTFE-CB-modified cathode in 0.05 M Na₂SO₄ at a current density of 50 A/m² and an air flow rate of 1 L/min after 20 min. Meanwhile, after cathode modified by PTFE-CB, the mineralization efficiency and mineralization current efficiency performed absolutely better than the pristine one. Cyclic voltammograms, SEM images, contact angles, and BET surface area were carried out to demonstrate stronger current responses and higher hydrophilicity of GF after modified. The value of biochemical oxygen demand/chemical oxygen demand (BOD₅/COD) increased from 0.049 to 0.331 after 90-min treatment, suggesting the solution was biodegradable, and the modified cathode was confirmed to be stable after ten circle runs. Finally, a proposed degradation pathway of RhB was put forward.     

Exposure,epidemiology, and mechanism of the environmental toxicant manganese

It has become increasingly apparent that global manganese (Mn) pollution to air and water is a significant threat to human health. Despite this recognition, research is only beginning to comprehend the detrimental effects of exposure. Mn, while essential, is particularly harmful to the central nervous system, and overexposure is symptomatic of several neurological disorders. At-risk populations have been identified, but it is still unclear whether typical exposure levels have any long-term consequences. Those at an elevated risk have diminished intellectual function, learning and memory, and mental development. While the overall mechanism of toxicity is undetermined, Mn has been found to induce oxidative stress, exacerbate mitochondrial dysfunction, dysregulate autophagy, and promote apoptosis, ultimately enhancing neurodegeneration. Extrapolation of this in vitro and in vivo data to humans is difficult. There is a definite need to correlate epidemiological studies with causative effects. It is imperative that research efforts endure, so threats are appropriately identified and exposure properly regulated.