Biodegradation of Poly(Aspartic Acid-Itaconic Acid) Copolymers by Miscellaneous Microbes from Natural Water

Poly(aspartic acid-itaconic acid) copolymers (PAI) is a new scale inhibitor for water treatment. Thus, it is necessary to investigate its biodegradability. The biodegradability of PAI was investigated through CO₂ evolution tests under different conditions based on determination of carbon dioxide production. The investigation results showed that the degradation rate of PAI on day 10 and day 28 were respectively 38.7 and 79.5%, indicating that PAI was one kind of easily biodegradable scale inhibitors. With the increase in the content of itaconic acid in copolymerization process, the biodegradability of PAI was significantly reduced. In addition, the high biodegradability might be attributed to the existence of C–N bone-structure and more –COO–. Finally, Cu²⁺ could decrease the degradation percentage and the enzyme inhibition effect of Cu²⁺ was not the linear effect, but the “low-dosage effect”.     

Polychlorinated biphenyls (PCBs) and halogenated flame retardants (HFRs) in multi-matrices from an electronic waste (e-waste) recycling site in Northern China

The present study investigated the occurrence of polychlorinated biphenyls (PCBs) and halogenated flame retardants (HFRs) in soil, water, reed, air and dust samples collected from the e-waste recycling region in Ziya Town, Northern China. The results showed that the concentrations of PCBs reached relative high level in environmental matrices in the study area. HFRs including polybrominated diphenyl ethers (PBDEs), dechlorane plus (DP), allyl 2,4,6-tribromophenyl ether (ATE), tetrabromoethylcyclohexane (TBECH), pentabromotoluene (PBT), pentabromoethylbenzene (PBEB), 2,3-dibromopropyl 2,4,6-tribromophenyl ether (DPTE), 1,2-bis-(2,4,6-tribromophenoxy) ethane (BTBPE) and so on were also widely detected in multi-matrices. Long-range atmospheric transport (LRAT) potentials of non-BDE HFRs were assessed to address the LRAR abilities of these compounds. Analysis of soil–air exchange of PCBs and HFRs showed that soil acted as a secondary source to the atmosphere only for a few low molecular weight compounds, while the direction of the flux of most detected chemicals was from air to soil.     

Performance improvement of airfoils for wind blade with the groove

This study was a basic one to explore how much the aerodynamic characteristics of wind blade improve. The extent of improvement according to the shapes of groove placed on the surface of airfoil (NACA0015) was analyzed through computational analysis. A commercial computational fluid dynamics (CFD) code, the ANSYS Fluent 13, was used in this study. In this study, regarding with the positions and shapes of groove, the end of groove was placed at a certain distance (length, l) from both the front and back of separation starting point, the depth and the width were designated as h and d respectively. Analysis was conducted at the 7° angle of attack under the following conditions; the thickness (δ) of boundary layer to the depth (h) of groove ratio (h/δ) 0.6–1.0, the depth (h) of groove to the width (d) of groove ratio (h/d) 0.1–1.4, and the length (l) between the end of groove and separation point to the thickness (δ) of boundary layer ratio (l/δ) ?0.5–0.5. Among these conditions, the best improvement of lift to drag ratio, standing at 15.3%, was under h/δ = 1.0, h/d = 0.12, and l/δ = –0.5 (7° AOA, Re = 360k). In addition, throughout the range of angle of attack, 2–14°, lift to drag ratio improved by 0.8%, 5.1%, 3.2%, and 1.8% each when Reynolds numbers were 280k, 360k, 450k, and 530k. It is also confirmed that the shape of groove contributed to recovering velocity around airfoil wall and the lift to drag ratio improvements by groove were maintained at the given range of Reynolds number and around the angle of attack, 7°.     

Research and application of method of oxygen isotope of inorganic phosphate in Beijing agricultural soils

Phosphorus (P) in agricultural ecosystems is an essential and limited element for plants and microorganisms. However, environmental problems caused by P accumulation as well as by P loss have become more and more serious. Oxygen isotopes of phosphate can trace the sources, migration, and transformation of P in agricultural soils. In order to use the isotopes of phosphate oxygen, appropriate extraction and purification methods for inorganic phosphate from soils are necessary. Here, we combined two different methods to analyze the oxygen isotopic composition of inorganic phosphate (δ¹⁸O_P) from chemical fertilizers and different fractions (Milli-Q water, 0.5 mol L^?1 NaHCO₃ (pH = 8.5), 0.1 mol L^?1 NaOH and 1 mol L^?1 HCl) of agricultural soils from the Beijing area. The δ¹⁸O_P results of the water extracts and NaHCO₃ extracts in most samples were close to the calculated equilibrium value. These phenomena can be explained by rapid P cycling in soils and the influence of chemical fertilizers. The δ¹⁸O_P value of the water extracts and NaHCO₃ extracts in some soil samples below the equilibrium value may be caused by the hydrolysis of organic P fractions mediated by extracellular enzymes. The δ¹⁸O_P values of the NaOH extracts were above the calculated equilibrium value reflecting the balance state between microbial uptake of phosphate and the release of intracellular phosphate back to the soil. The HCl extracts with the lowest δ¹⁸O_P values and highest phosphate concentrations indicated that the HCl fraction was affected by microbial activity. Hence, these δ¹⁸O_p values likely reflected the oxygen isotopic values of the parent materials. The results suggested that phosphate oxygen isotope analyses could be an effective tool in order to trace phosphate sources, transformation processes, and its utilization by microorganisms in agricultural soils.     

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.     

Benchmarking Optical/Thermal Satellite Imagery for Estimating Evapotranspiration and Soil Moisture in Decision Support Tools

Generally, one expects evapotranspiration (ET) maps derived from optical/thermal Landsat and MODIS satellite imagery to improve decision support tools and lead to superior decisions regarding water resources management. However, there is lack of supportive evidence to accept or reject this expectation. We “benchmark” three existing hydrologic decision support tools with the following benchmarks: annual ET for the ET Toolbox developed by the United States Bureau of Reclamation, predicted rainfall‐runoff hydrographs for the Gridded Surface/Subsurface Hydrologic Analysis model developed by the U.S. Army Corps of Engineers, and the average annual groundwater recharge for the Distributed Parameter Watershed Model used by Daniel B. Stephens & Associates. The conclusion of this benchmark study is that the use of NASA/USGS optical/thermal satellite imagery can considerably improve hydrologic decision support tools compared to their traditional implementations. The benefits of improved decision making, resulting from more accurate results of hydrologic support systems using optical/thermal satellite imagery, should substantially exceed the costs for acquiring such imagery and implementing the remote sensing algorithms. In fact, the value of reduced error in estimating average annual groundwater recharge in the San Gabriel Mountains, California alone, in terms of value of water, may be as large as $1 billion, more than sufficient to pay for one new Landsat satellite.     

三峡库区重庆段土壤保持服务时空分布格局研究

土壤保持是生态系统服务与功能的重要组成,在防止土壤侵蚀、减少径流泥沙与农业面源污染等方面具有至关重要的作用。以对国家生态安全具有重要作用的土壤保持生态功能区——三峡库区重庆段为研究区域,研究得到了2000~2013年时间序列区域土壤保持服务"流量"结果,结果表明:(1)三峡库区重庆段多年平均土壤保持量为604.39 t/hm~2·a,沿长江干流自西向东逐渐增强,区域差异显著;(2)三峡库区的土壤保持服务存在明显的垂直分异特征,随着高程的增加,以300 m与900 m为节点,出现递减-递增-递减的分段规律,与人类活动存在明显的相关关系;(3)增加森林覆被面积是改善区域土壤保持、减少水土流失的重要举措。同时,在三峡库区开展坡改梯工程,减少坡耕地的数量能够有效控制区域水土流失;