Effects of Compounded Curing Agents on Properties and Performance of Urea Formaldehyde Resin

The effects of three compounded curing agents on the properties and performance of the urea-formaldehyde (UF) resin were investigated in this study. The compounded curing agents were prepared by mixing ammonium chloride with hexamethylenetetramine, citric acid, and oxalic acid respectively at a ratio of 1:1, named N-H, N–CA, and N–OA, respectively. The curing process, crystallinity, and physical properties were measured, and the three-ply plywood was fabricated to measure its prepress strength, wet shear strength, and formaldehyde emission. Results showed that the compounded curing agents N–CA and N–OA enhanced the initial viscosity, crosslinking density and thermal stability of UF resin. Additionally, the prepress strength of the plywood bonded by UF resin with N–CA and N–OA increased by 82 and 111% respectively compared to the UF resin with NH₄Cl, and the wet shear strength increased by 14 and 16%, the formaldehyde emission decreased by 19 and 42% respectively. However, owing to the short pot-life of these curing agent limited their storage time, the curing agents N–CA and N–OA should be applied to fabricate plywood in winter for obtaining a better bond strength and a lower formaldehyde emission. While the UF resin with N–HT showed a suitable pot-life, so it could be applied to fabricate plywood in summer for long time storage and avoiding procuring problem.     

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.     

Increasing the recovery of heavy metal ions using two microbial fuel cells operating in parallel with no power output

The present study aimed to improve the performance of microbial fuel cells (MFCs) by using an intermittent connection period without power output. Connecting two MFCs in parallel improved the voltage output of both MFCs until the voltage stabilized. Electric energy was accumulated in two MFCs containing heavy metal ions copper, zinc, and cadmium as electron acceptors by connection in parallel for several hours. The system was then switched to discharge mode with single MFCs with a 1000-Ω resistor connected between anode and cathode. This method successfully achieved highly efficient removal of heavy metal ions. Even when the anolyte was run in sequencing batch mode, the insufficient voltage and power needed to recover heavy metals from the cathode of MFCs can be complemented by the developed method. The average removal ratio of heavy metal ions in sequencing batch mode was 67 % after 10 h. When the discharge time was 20 h, the removal ratios of zinc, copper, and cadmium were 91.5, 86.7, and 83.57 %, respectively; the average removal ratio of these ions after 20 h was only 52.1 % for the control group. Therefore, the average removal efficiency of heavy metal ions increased by 1.75 times using the electrons stored from the bacteria under the open-circuit conditions in parallel mode. Electrochemical impedance data showed that the anode had lower solution resistance and polarization resistance in the parallel stage than as a single MFC, and capacitance increased with the length of time in parallel.

     

Efficacy of Si fertilization to modulate the heavy metals absorption by barley (Hordeum vulgare L.) and pea (Pisum sativum L.)

Silicon-based fertilizers and soil amendments can have direct and indirect positive influences on cultivated plants. The solid forms of Si-based substances, the most widespread in use, are efficient only at high application rates due to their low level of solubility. Several types of Si-based substances such as fumed silica, slags from the iron and steel industry, modified slags, and a Si-rich product were tested using barley and pea as silicon accumulative and non-accumulative plants, respectively, at two application rates. The plants were grown under toxic concentrations of heavy metals in a greenhouse. Si-rich materials high in water-soluble Si had a positive effect at both the low and high application rates, and for both plant species. This type of substance can be regarded as Si fertilizer, demonstrating greater efficiency at a low application rate and lessened efficiency at a high application rate for protection of the cultivated plants against accumulation of the heavy metals.

     

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.     

The fractionation and geochemical characteristics of rare earth elements measured in ambient size-resolved PM in an integrated iron and steelmaking industry zone

Environmental Science and Pollution Research - Improved understanding of the fractionation and geochemical characteristic of rare earth elements (REEs) from steel plant emissions is important due...