Remobilization of polycyclic aromatic hydrocarbons during the resuspension of Yangtze River sediments using a particle entrainment simulator

Remobilization of 16 polycyclic aromatic hydrocarbons (PAHs) during sediment resuspension was investigated using a particle entrainment simulator at shear stress from 0.2 to 0.5Nm(-2), typical of the energy levels present in many tidally driven aquatic environments. The results suggested that desorption from the entrained particles was the primary source of dissolved PAHs. summation operatorPAHs concentrations in particles on volume normalization increased about four times. However, on mass weight basis, summation operatorPAHs concentrations decreased from 6039.74+/-138.28microgkg(-1) to 1665.39+/-112.26microgkg(-1). The same trend was observed for individual PAHs. Dissolved concentrations of PAHs demonstrated significant differences depending on molecular weight and applied shear. The distribution of PAHs between particle and water phase suggested that for three-ring PAHs, the amounts of PAHs in particles were higher than the predicted values during our experiments. This might be due to presence of another active sorbent.     

Catalytic hydrogenation of polyaromatic hydrocarbon (PAH) compounds in supercritical carbon dioxide over supported palladium

A series of supported palladium catalysts were evaluated for their ability to mediate the complete hydrogenation of polycyclic aromatic hydrocarbon (PAH) compounds. Benzo[a]pyrene (B[a]P) or phenanthrene (Phe) in hexane was merged with a hydrogen-carbon dioxide [5% (w/w) H(2)/CO(2)] stream and transferred to a flow through mini-reactor (capacity ca. 1 g) that was maintained at 90 degrees C under a back-pressure of 20.68 MPa. Effluent from the reactor trapped in hexane was monitored/quantified by gas chromatography-mass spectrometry. Catalyst formulations supported on iron powder, high density polyethylene (HDPE) or gamma-alumina were prepared and compared in terms of hydrogenation activity as measured by the quantity of substrate per unit time that could be perhydrogenated to toxicologically innocuous products. Both of the Pd preparations supported on gamma-alumina were more efficient than a commercial Pd(0) (5% w/w) on gamma-Al(2)O(3) formulation or preparations supported on HDPE or the iron powder. Bimetallic mixtures with Pd increased the hydrogenation activity when co-deposited with Cu or Ni but not with Ag or Co. However, increases in hydrogenation activity by increasing the loading of Pd (or bimetallic mixture) on this surface were limited. Despite using supercritical carbon dioxide (scCO(2)) to swell the surfaces of the polymer, the deposition of nanoparticles within the polyethylene formulation was appreciably less active than either the oxidic or the Fe(0) formulations.     

Distribution of elements in needles of Pinus massoniana (Lamb.) was uneven and affected by needle age

Macronutrients (P, S, K, Na, Mg, Ca), heavy metals (Fe, Zn, Mn, Cu, Pb, Cr, Ni, Cd,) and Al concentrations as well as values of Ca/Al in the tip, middle and base sections, and sheaths of current year and previous year needles of Pinus massoniana from Xiqiao Mountain were analyzed and the distribution patterns of those elements were compared. The results indicated that many elements were unevenly distributed among the different components of needles. Possible deficiency of P, K, Ca, Mn and Al toxicity occurred in needles under air pollution. Heavy metals may threaten the health of Masson pine. Needle sheaths were good places to look for particulate pollutants, in this case including Fe, Cu, Zn, Pb, Cr, Cd and Al.     

Heavy metals in bark of <Emphasis Type="Italic">Pinus massoniana</Emphasis> (Lamb.) as an indicator of atmospheric deposition near a smeltery at Qujiang,China

Goal, Scope and Background Rapid urbanization and the expansion of industrial activities in the past several decades have led to large increases in emissions of pollutants in the Pearl River Delta of south China. Recent reports have suggested that industrial emission is a major factor contributing to the damages in current natural ecosystem in the Delta area. Tree barks have been used successfully to monitor the levels of atmospheric metal deposition in many areas, but rarely in China. This study aimed at determining whether atmospheric heavy metal deposition from a Pb-Zn smeltery at Qujiang, Guangdong province, could be accurately reflected both in the inner bark and the outer bark of Masson pine (Pinus massoniana L.). The impact of the emission from smeltery on the soils beneath the trees and the relationships of the concentrations between the soils and the barks were also analyzed. Methods Barks around the bole of Pinus massoniana from a pine forest near a Pb-Zn smeltery at Qujiang and a reference forest at Dinghushan natural reserve were sampled with a stainless knife at an average height of 1.5 m above the ground. Mosses and lichens on the surface barks were cleaned prior to sampling. The samples were carefully divided into the inner bark (living part) and the outer bark (dead part) in the laboratory, and dried and ground, respectively. After being dry-ashed, the powder of the barks was dissolved in HNO₃. The solutions were analyzed for iron (Fe), manganese (Mn), copper (Cu), zinc (Zn), chromium (Cr), nickel (Ni) and cobalt (Co) by inductively coupled plasmas emission spectrometry (ICP, PS-1000AT, USA) and Cadmium (Cd) and lead (Pb) by graphite furnace atomic absorption spectrometry (GFAAS, ZEENIT 60, Germany). Surface soils (0–10 cm) beneath the sample trees were also collected and analyzed for the selected metals. Results and Discussion Concentrations of the selected metals in soils at Qujiang were far above their environmental background values in the area, except for Fe and Mn, whilst at Dinghushan, they were far below their background values, except for Cd and Co. Levels of the metals, in particular Pb and Zn, in the soils beneath the sample trees at Qujiang were higher than those at Dinghushan with statistical significance. The result suggested that the pine forest soils at Qujiang had a great input of heavy metals from wet and dry atmospheric deposition, with the Pb-Zn smeltery most probably being the source. Levels of Cu, Fe, Mn, Zn, Ni and Pb at Qujiang, both in the inner and the outer bark, were statistically higher than those at Dinghushan. Higher concentrations of Pb, Fe, Zn and Cu may come from the stem-flow of elements leached from the canopy, soil splash on the 1.5 m height and sorption of metals in the mosses and lichens growing on the bark, which were direct or indirect results from the atmospheric deposition. Levels of heavy metals in the outer barks were associated well with the metal concentrations in the soil, reflecting the close relationships between the metal atmospheric deposition and their accumulation in the outer bark of Masson pine. The significant (p<0.01) correlations of Fe-Cu, Fe-Cr, Fe-Pb, Fe-Ni, Pb-Ni, and Pb-Zn in the outer barks at Qujiang again suggested a common source for the metals. The correlation only occurred between Pb and Ni, Cd and Co in the outer barks at Dinghushan, which suggested that those metals must possibly have other uncommon sources. Conclusions Atmospheric deposition of the selected metals was great at Qujiang, based on the levels in the bark of Pinus massoniana and on the concentrations in the soils beneath the trees compared with that at Dinghushan. Bark of Pinus massoniana, especially the outer bark, was an indicator of metal loading at least at the time of sampling. Recommendations and Perspectives The results from this study and the techniques employed constituted a new contribution to the development of biogeochemical methods for environmental monitoring particularly in areas with high frequency of pollution in China. The method would be of value for follow up studies aimed at the assessment of industrial pollution in other areas similar with the Pearl River Delta.     

RETRACTED ARTICLE: Environmental Impact Assessment of Industrial Structure Change in a Rural Region of China

As the embodiment of human activities, the change of regional industrial structure is an essential driving factor of global environmental change. Consequently, the research on the change of regional industrial structure and associated effects on the environment is one of the key issues of researches on sustainable development, human–environment relationship, and regional response to global environment change. However, compared to the flourish of researches on environmental impact assessment of industrial departments, few studies have been conducted to assess the environmental impact of regional industrial structure. In this study, based on a synthetic analysis of environmental disturbances of different industrial departments, the environmental impact coefficient of industrial department associated with the index of environmental impact of industrial structure was constructed, so as to make a quantitative assessment of environmental impact of the change of regional industrial structure. And the results of the case study in Lijiang City, a rural region of China, have showed that there are two obvious changes of industrial structure in the study area from 1992 to 2003, associated with a continuous decreasing of the index of environmental impact of industrial structure, which indicated a positive environmental effects of the change of regional industrial structure.     

苯酚、苯胺及其衍生物对斜生栅列藻的急性毒性及QSAR研究

本文应用ＯＥＣＤ标准方法测定了１８种酚类胺类化合物对斜生栅列藻的４８ｈ－ＥＣ６０值,选择参数ＥＬＵＭＯ和ｌｇＫｏｗ进行了ＱＳＡＲ分析,得到下列回归方程：－１ｇＥＣ５０＝２．５４１－０．６４９ＥＬＵＭＯ＋０．２９９ＬｇＫｏｗ,ｎ＝１８,ｒ＝０．９０３,ＳＥ＝０．３１４应用所得方程计算了化合物的毒性,探讨了毒机理。     

湖库泥沙资源化多目标优化配置研究综述

合理利用泥沙的资源特性,实现泥沙资源优化配置是湖库生态环境治理迫切需要解决的关键问题之一。该文旨在对泥沙资源化及多目标优化配置方法的研究进行综述,总结泥沙资源化的研究现状和分析其发展趋势。首先对泥沙资源优化配置框架的相关研究进行了综述,主要集中于泥沙资源优化配置、泥沙资源化的途径、泥沙管理生态化及优化配置方法等方面,然后分析了目前对于泥沙资源优化配置的理论认识、模型构建及应用、方案评价及决策存在的主要问题,最后探讨了泥沙资源优化配置相关研究未来发展方向。     

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.

     

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.