Molecular diversity of arbuscular mycorrhizal fungi at a large-scale antimony mining area in southern China

Arbuscular mycorrhizal fungi (AMF) have great potential for assisting heavy metal hyperaccumulators in the remediation of contaminated soils. However, little information is available about the community composition of AMF under natural conditions in soils contaminated by antimony (Sb). The objective of this study was to investigate the characteristics of AMF molecular diversity, and to explore the effects of Sb content and soil properties on the AMF community structure in an Sb mining area. Four Sb mine spoils and one adjacent reference area were selected from around the Xikuangshan mine in southern China. The association of AMF molecular diversity and community composition with the rhizosphere soils of the dominant plant species was studied by Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis (PCR-DGGE). Results from all five studied sites showed that the diversity of AMF decreased with increasing Sb concentration. Principal component analysis (PCA) indicated that the AMF community structure was markedly different among these groups. Further redundancy analysis (RDA) showed that Sb contaminationwas the dominating factor influencing the AMF community structure in the Sb mine area. However, the multivariate analysis showed that, apart from the soil Sb content, extractable nitrogen content and organic matter content also attributed to AMF sequence distribution type. Some AMF sequences were only found in the highly contaminated area and these might be ideal candidates for improving phytoremediation efficiency in Sb mining regions. Gene sequencing analysis revealed that most species were affiliated with Glomus, suggesting that Glomus was the dominant AMF genus in the studied Sb mining area.     

Microcystis aeruginosa/Pseudomonas pseudoalcaligenes interaction effects on off-flavors in algae/bacteria co-culture system under different temperatures

We conducted an experiment to study the interaction effects of Microcystis aeruginosa and Pseudomonas pseudoalcaligenes on off-flavors in an algae/bacteria co-culture system at three temperatures (24, 28 and 32°C). Gas chromatography–mass spectrometry was applied to measure off-flavor compounds dimethyl sulfide (DMS), dimethyl trisulfide (DMTS), 2-methylisoborneol, geosmin (GEO) and β-cyclocitral. During the lag phase of co-cultured M. aeruginosa (first 15 days), P. pseudoalcaligenes significantly increased the production of DMS, DMTS and β-cyclocitral at all three temperatures. In the exponential phase of co-cultured M. aeruginosa (after 15 days), M. aeruginosa became the main factor on off-flavors in the co-culture system, and β-cyclocitral turned to the highest off-flavor compound. These results also indicated that DMS, DMTS and β-cyclocitral were the main off-flavor compounds in our M. aeruginosa/P. pseudoalcaligenes co-culture system. Univariate analysis was applied to investigate the effects of M. aeruginosa and P. pseudoalcaligenes on the production of off-flavors. The results demonstrated that both M. aeruginosa and P. pseudoalcaligenes could increase the production of DMS and DMTS, while β-cyclocitral was mainly determined by M. aeruginosa. Our results also provide some insights into understanding the relationship between cyanobacteria and heterotrophic bacteria.     

Microcystis aeruginosa/Pseudomonas pseudoalcaligenes interaction effects on off-flavors in algae/bacteria co-culture system under different temperatures

We conducted an experiment to study the interaction effects of Microcystis aeruginosa and Pseudomonas pseudoalcaligenes on off-flavors in an algae/bacteria co-culture system at three temperatures(24, 28 and 32℃). Gas chromatography–mass spectrometry was applied to measure off-flavor compounds dimethyl sulfide(DMS), dimethyl trisulfide(DMTS),2-methylisoborneol, geosmin(GEO) and β-cyclocitral. During the lag phase of co-cultured M. aeruginosa(first 15 days), P. pseudoalcaligenes significantly increased the production of DMS, DMTS and β-cyclocitral at all three temperatures. In the exponential phase of co-cultured M. aeruginosa(after 15 days), M. aeruginosa became the main factor on off-flavors in the co-culture system, and β-cyclocitral turned to the highest off-flavor compound. These results also indicated that DMS, DMTS and β-cyclocitral were the main off-flavor compounds in our M. aeruginosa/P. pseudoalcaligenes co-culture system. Univariate analysis was applied to investigate the effects of M. aeruginosa and P. pseudoalcaligenes on the production of off-flavors. The results demonstrated that both M. aeruginosa and P. pseudoalcaligenes could increase the production of DMS and DMTS, while β-cyclocitral was mainly determined by M. aeruginosa. Our results also provide some insights into understanding the relationship between cyanobacteria and heterotrophic bacteria.     

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.     

Remarkable promotion effect of trace sulfation on OMS-2 nanorod catalysts for the catalytic combustion of ethanol

OMS-2 nanorod catalysts were synthesized by a hydrothermal redox reaction method using MnSO₄ (OMS-2-SO₄) and Mn(CH₃COO)₂ (OMS-2-AC) as precursors. SO₄^2 −-doped OMS-2-AC catalysts with different SO₄^2 − concentrations were prepared next by adding (NH₄)₂SO₄ solution into OMS-2-AC samples to investigate the effect of the anion SO₄^2 − on the OMS-2-AC catalyst. All catalysts were then tested for the catalytic oxidation of ethanol. The OMS-2-SO₄ catalyst synthesized demonstrated much better activity than OMS-2-AC. The SO₄^2 − doping greatly influenced the activity of the OMS-2-AC catalyst, with a dramatic promotion of activity for suitable concentration of SO₄^2 − (SO₄/catalyst = 0.5% W/W). The samples were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), inductively coupled plasma optical emission spectroscopy (ICP-OES), NH₃-TPD and H₂-TPR techniques. The results showed that the presence of a suitable amount of SO₄^2 − species in the OMS-2-AC catalyst could decrease the Mn–O bond strength and also enhance the lattice oxygen and acid site concentrations, which then effectively promoted the catalytic activity of OMS-2-AC toward ethanol oxidation. Thus it was confirmed that the better catalytic performance of OMS-2-SO₄ compared to OMS-2-AC is due to the presence of some residual SO₄^2 − species in OMS-2-SO₄ samples.     

The relationship between light intensity and nutrient uptake kinetics in six freshwater diatoms

In order to find effective measures to control diatom blooms, a better understanding of the physiological characteristics of nutrient uptake in diatoms is needed. A study of P and Si-uptake kinetics for diatom species from two light regimes was conducted at low (LL), moderate (ML) and high light intensities (HL) (2, 25 and 80 μmol photons/(m²·sec)), respectively. The results showed that P uptake of diatoms was heavily influenced by historic light regimes. P affinity changed with growth and photosynthetic activity. The lowest half saturation constant for P uptake (K_m(P)) was under HL for high-light adapted diatoms while the lowest half-saturation constant for low-light adapted diatoms was observed under LL. The Si half-saturation constant (K_m(Si)) increased with increasing light intensities for pennate diatoms but decreased for centric diatoms. Diatom volumes were correlated with the maximum Si uptake rates (V_m(Si)) at HL and K_m(Si) at ML and HL for six diatom species. Our results imply that when we assess the development of diatom blooms we should consider light intensity and cell volume in addition to ambient Si or P concentration. The relationship between light intensity and P-uptake suggests that we can find suitable methods to control diatom blooms on the basis of reducing phytoplankton activity of P-uptake and photosynthesis simultaneously.     

基于QSAR和ICE模型的磺胺类药物的水质基准研究

药物水环境污染是一个严重的环境问题,引起了人们越来越多的关注。磺胺类药物是一类广泛使用的药物。文章应用水质毒性快速检测仪测定一系列磺胺类药物的发光菌毒性,建立发光菌毒性QSAR模型,从而解决ICE模型的基准物种毒性数据缺失的问题。基于发光菌毒性测试和QSAR、ICE毒性估算方法获得更多毒性数据,初步推导了保护水生生物的磺胺类化合物的水质基准,并为建立更加具有生态统计学意义的物种敏感性分布(SSD)模型提供一定的数据支撑。同时,结合国内外水环境中磺胺类药物的暴露水平,对我国磺胺类药物的风险水平做了初步评估,其中磺胺甲恶唑、磺胺嘧啶和磺胺喹恶林的风险较高。     

膨润土负载纳米铁的改性及对水中As(Ⅴ)的吸附

用十六烷基三甲基溴化铵(CTMAB)对膨润土负载纳米零价铁(Bent-n ZVI)进行表面改性,得到表面改性膨润土负载纳米铁(Bent-n ZVI)/CTMAB。通过SEM、Zeta电位和中位径d(50)分析,与膨润土(Bent)、改性膨润土(CTMAB-Bent)、Bent-n ZVI进行了对比,结果表明:相比Bent-n ZVI,(Bent-n ZVI)/CTMAB中的纳米铁颗粒更为分散且呈链状分布,d(50)仅为5.21μm,并且Zeta电位为+0.65 m V,更易静电吸引和吸附阴离子As O43-。4种材料的除砷结果亦表明其中(Bent-n ZVI)/CTMAB去除率最高,达到了93.9%。光电子能谱(XPS)分析表明(Bent-n ZVI)/CTMAB的除砷产物中只有As(Ⅴ),并不存在其还原态As(Ⅲ),说明(Bent-n ZVI)/CTMAB是通过吸附作用去除As(Ⅴ)。     

基尼系数法在全球碳排放权分配中的应用研究

文章剖析了现有碳排放权分配方法在分配指标的选取、适用性、可行性、减排责任分担等方面存在的不足。在前期研究中提出的"生存权平等,发展权有别"的思想和多角度衡量CO2排放公平性的评价指标体系的基础上,提出了滚动规划的分配理念和基于基尼系数法的全球CO2排放权优化分配方法及相应的数学模型。在VC平台上开发了求解该优化分配模型的计算机算法和软件。通过全球各国CO2排放配额的实例计算,结果显示:基于基尼系数法的CO2排放权分配方法可以更好地提高碳减排分配方案的公平性,滚动规划理念可以更好地兼容未来的各种变化和不确定性,从而提高分配方案的可行性。     

闸控河段水质浓度影响关键因子识别

由于闸门调度对河道内的水环境要素具有强烈的扰动作用,故闸控河段的水质变化过程较常规河道更为复杂。为了探析引起闸控河段水质浓度变化的主要制约因素,以槐店闸为研究对象,结合笔者进行的2次实验和其他时期的监测数据,通过偏相关分析和主成分分析的方法识别影响闸控河段水质浓度变化的关键因子,并利用多元线性回归方法分析得到闸控河段水质浓度变化与其关键因子的多元线性回归方程。经过分析初步得到,与高锰酸盐指数浓度变化率有关的关键因子为闸门开度、开启个数、高锰酸盐指数来水浓度、溶解氧含量、流速和水深;与氨氮浓度变化率有关的关键因子为闸门开度、开启个数、氨氮来水浓度、溶解氧含量和p H值。