Measuring bed shear stress along vegetated river beds using FST-hemispheres

The measurement of the bed shear stress along vegetated river beds is essential for accurately predicting the water level, velocity and solute and sediment transport fluxes in computational hydroenvironmental models. Details are given herein of an experimental and theoretical study to determine the bed boundary shear stress along vegetated river beds introducing a novel field measuring method, namely the FliessWasserStammtisch (FST)-hemispheres. Although investigations have been conducted previously for sedimentary channels using the FST-hemispheres, this preliminary study is thought to be the first time that such hemispheres have been used to investigate the bed shear stresses in vegetated channels. FST-hemispheres were first developed by Statzner and Müller [1989. Standard hemispheres as indicators of flow characteristics in lotic benthos research. Freshwater Biology 21, 445-459] to act as an integrated indicator of the gross hydrodynamic stresses present near the bed. Test and validation data were found to be at least of the same order of magnitude for the stresses predicted from literature for sedimentary channels, with this study establishing the commencement of a database of calibrated FST-hemisphere laboratory data for vegetated channel beds. In a series of experiments, depths ranging from 0.1 to 0.28m were considered, equating directly to comparable conditions in small rivers or streams. The results of this study provide a basis for enabling the FST-hemispheres to be used to evaluate the boundary shear stress for a wider range of applications in the future, including vegetated river beds.     

Hunting Increases Dispersal Limitation in the Tree Carapa procera, a Nontimber Forest Product

Abstract:  The sustainability of seed extraction from natural populations has been questioned recently. Increased recruitment failure under intense seed harvesting suggests that seed extraction intensifies source limitation. Nevertheless, areas where more seeds are collected tend to also have more intense hunting of seed-dispersing animals. We studied whether such hunting, by limiting disperser activity, could cause quantitative dispersal limitation, especially for large crops and for crops in years of high seed abundance. In each of four Carapa procera (Meliaceae) populations in French Guiana and Surinam, two with hunting and two without, we compared seed fate for individual trees varying in crop size in years of high and low population-level seed abundance. Carapa seeds are a nontimber forest product and depend on dispersal by scatter-hoarding rodents for survival and seedling establishment. Hunting negatively affected the proportion of seeds dispersed and caused greater numbers of seeds to germinate or be infested by moths below parent trees, where they would likely die. Hunting of seed-dispersing animals disproportionally affected large seed crops, but we found no additional effect of population-level seed abundance on dispersal rates. Consistently lower rates of seed dispersal, especially for large seed crops, may translate to lower levels of seedling recruitment under hunting. Our results therefore suggest that the subsistence hunting that usually accompanies seed collection is at the cost of seed dispersal and may contribute to recruitment failure of these nontimber forest products. Seed extraction from natural populations may affect seedling recruitment less if accompanied by measures adequately incorporating and protecting seed dispersers.     

Biological Activity in a Heavily Organohalogen-Contaminated River Sediment (8 pp)

Background, Aims and Scope Sediments of the Spittelwasser creek are highly polluted with organic compounds and heavy metals due to the discharge of untreated waste waters from the industrial region of Bitterfeld-Wolfen, Germany over the course of more than one century. However, relatively few data have been published about the chloroorganic contamination of the sediment. This paper reports on the content of different (chloro)organic compounds with special emphasis on polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F), and chlorobenzenes. Existing concepts for the remediation of Spittelwasser sediment include the investigation of natural attenuation processes, which largely depend on the presence of an intact microbial food web. In order to gain more insight in terms of biological activity, we analyzed the capacity of sediment microflora to degrade organic matter by measuring the activities of extracellular hydrolytic enzymes involved in the biogeochemical cycling of carbon, nitrogen, phosphorus and sulfur. Furthermore, the detection of physiologically active bacteria in the sediment, particularly of those known for their capability to reductively dehalogenate organochlorine compounds, illustrates the potential for intrinsic bioremediation processes. Methods PCDD/F and chlorobenzenes were analyzed by gas chromatography(GC)/mass spectrometry and GC/flame ionization detection, respectively. The activities of hydrolytic enzymes were determined from freshly sampled sediment layers using 4-methylumbelliferyl (MUF) or 7-amino-4-methylcoumarin-conjugated model compounds and kinetic fluorescence measurements. Physiologically active bacteria from different sediment layers were microscopically visualized by fluorescence in situ hybridization (FISH). Specific bacteria were identified by 16S rRNA gene amplification and sequencing. Results and Discussion The PCDD/F congener profile was dominated by dibenzofurans. In addition, the presence of specific tetra and pentachlorinated dibenzofurans supported the assumption that extensive magnesium production was one possible source for the high contamination. A range of other chloroorganic compounds, including several isomers of chlorobenzenes, hexachlorocyclohexane and 1,1,1-trichloro-2,2-bis (p-chloro-phenyl)ethane (DDT), was present in the sediment. Activities of extracellular hydrolytic enzymes showed a strong decrease in those sediment layers that were characterized by high contents of absorbable organic halogen (AOX), indicating disturbed organic matter decay. Interestingly, an abnormal increase of cellulolytic enzyme activities below the organochlorine-rich layers was observed, possibly caused by residual cellulose from discharges of sulfite pulping wastes. FISH revealed physiologically active bacteria in most sediment layers from the surface down to the depth of about 60 cm, including members of Desulfitobacterium (D.) and Sulfurospirillum. The presence of D. dehalogenans was confirmed by its partial 16S rRNA gene sequence. Conclusions Results of chemical sediment analyses demonstrated high loads of organochlorine compounds, particularly of PCDD/F. Several years after stopping the waste water discharge to Spittelwasser creek, this sediment remains a main source for pollution of the downstream river system by way of the ongoing mobilization of sediment during high floods. As indicated by our enzyme activity measurements, the decomposition potential for organic matter is low in organochlorine-rich sediment layers. In contrast, the comparably higher enzyme activities in less organochlorine-polluted sediment layers as well as the presence of physiologically active bacteria suggest a considerable potential for natural attenuation. Recommendations and Perspectives From our data we strongly recommend to explore the degradative capacity of sediment microorganisms and the limits for in situ activity towards specific sediment pollutants in more detail. This will give a sound basis for the integration of bioremediation approaches into general concepts to reduce the risk that permanently radiates from this highly contaminated sediment. Submission Editor: Dr. Henner Hollert (Henner.Hollert@urz.uniheidelberg.de)     

人工神经网络在预测深基坑周边地表沉降变形中的应用研究

深基坑开挖引起的周边地表变形预测是一个复杂非线性问题,引起地表沉降的影响因素很多,各因素之间呈高度的非线性关系。传统的基坑用边地表沉降变形预测方法存在着一定的局限性,其预测精度有待提高,而人工神经网络是一种多元非线性动力学系统,可以灵活方便地对多成因的复杂未知系统进行高度建模,实现全面考虑各种主要影响因素的深基坑周边地表沉降变形预测。本文介绍了误差反向传播(BP)网络模型的结构、学习过程及其算法的改进,径向基函数(RBF)网络模型的结构及其学习过程;分析了影响深基坑开挖周边土体沉降变形的主要影响因素;以25个基坑工程的地表沉降实测资料为训练样本,建立了11个输入影响因素的BP神经网络模型和RBF神经网络模型,通过对样本的学习训练过程及对5个检验样本的预测精度,说明了人工神经网络用于预测基坑周边地表沉降的可行性和准确性。     

用海泡石处理采油废水

用海泡石吸附法处理采油废水，考察了处理时间、海泡石加入量和采油废水pH对采油废水COD去除率的影响，并通过正交实验优化了采油废水处理工艺条件。通过正交实验得到的采油废水处理最佳工艺条件为：处理时间6h，粒径为150μm的海泡石加入量200g／L，采油废水pH9。在该条件下处理采油废水，COD去除率达到91％，处理后出水的COD为34．71mg／L，小于GB8978-1996（（污水综合排放标准》中的一级标准（60mg／L）。     

Source characterization of ambient fine particles at multiple sites in the Seattle area

To identify major PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) sources with a particular emphasis on the ship engine emissions from a major port, integrated 24 h PM_2.5 speciation data collected between 2000 and 2005 at five United State Environmental Protection Agency's Speciation Trends Network monitoring sites in Seattle, WA were analyzed. Seven to ten PM_2.5 sources were identified through the application of positive matrix factorization (PMF). Secondary particles (12–26% for secondary nitrate; 17–20% for secondary sulfate) and gasoline vehicle emissions (13–31%) made the largest contributions to the PM_2.5 mass concentrations at all of the monitoring sites except for the residential Lake Forest site, where wood smoke contributed the most PM_2.5 mass (31%). Other identified sources include diesel vehicle emissions, airborne soil, residual oil combustion, sea salt, aged sea salt, metal processing, and cement kiln. Residual oil combustion sources identified at multiple monitoring sites point clearly to the Port of Seattle suggesting ship emissions as the source of oil combustion particles. In addition, the relationship between sulfate concentrations and the oil combustion emissions indicated contributions of ship emissions to the local sulfate concentrations. The analysis of spatial variability of PM_2.5 sources shows that the spatial distributions of several PM_2.5 sources were heterogeneous within a given air shed.     

基于改进遗传算法的河流水污染源反演方法

针对河流水污染应急响应过程中污染源排放历史迟知、未知的问题,结合多种群遗传算法和自适应遗传算法,利用一维河流水质模型和水质监测数据,研究建立了基于改进遗传算法的河流水污染定量源反演方法,实现了对河流污染源排放历史的识别与重构.将该方法应用于美国特拉基河流的3个不同流量下的示踪剂实验中,对示踪剂排放历史进行定量源反演分析.结果表明：IGA算法对高、中、低不同流量下的3次示踪剂实验均可以很好的重构和识别示踪剂排放历史,对于实际河流水污染源反演分析的误差均在可接受范围内.IGA算法在河流水污染源反演分析中具有一定的可靠性和稳定性,可为河流水污染精准溯源与治理提供科学的技术支撑.     

岩溶地下河污染物运移模型对比研究

两区模型（TRM）和暂时存储模型（TSM）为两种常用的岩溶地下河（管道）污染物运移模型,为了更好表征岩溶地下河污染物运移过程,开展模型对比研究.采用两种模型拟合室内管道示踪实验穿透曲线,从概念模型框架、模型优缺点、参数校正过程和物理意义、参数敏感性等方面对比分析两种模型的异同.结果表明,OTIS软件提供TSM的数值解,而CXTFIT软件提供TRM的解析解,但两者均能通过下游不同位置的穿透曲线分段表征地下河不同阶段的污染物运移过程.两种模型的物理意义相近,均可近似描述岩溶地下河中溶质的暂时性存储现象,较好地拟合溶质运移曲线.采用TSM可校正得到暂时存储区的横截面积,但采用TRM只能获得非流动区所占的比例.两种模型中弥散系数和传质/交换系数的敏感度较低,并且弥散系数的变化对穿透曲线拖尾无显著影响.研究结果为岩溶地下河污染物运移模型的选取与应用提供了依据.     

北部湾南流江流域土地覆盖及生物多样性模拟

以北部湾独流入海河流南流江流域为研究对象,基于研究区2000年和2015年遥感数据解译的土地利用数据以及社会经济等数据,采用CLUE-S模型对未来2030年生态保护情景、自然增长情景以及粮食安全情景的土地利用格局进行了模拟预测,在此基础上采用InVEST模型对流域过去和未来不同情景的生物多样性进行了评估,探讨了流域生物多样性的生境质量和生境退化程度.结果表明：2000~2015年南流江流域建设用地、园地、水域和未利用地呈现出增加趋势,其中建设用地的增幅最大,而耕地和林地减幅最大.流域土地系统中共存在着34种土地网络转移流关系,上游存在24种,中游20种,下游28种,耕地与建设用地、耕地与林地以及林地与园地之间的转换占到流域总土地利用变化的70.74%.CLUE-S模型模拟未来土地利用的Kappa系数达到0.86,表明模型模拟未来情景的土地利用精度满足要求.2000年、2015年、2030年生态保护情景、2030年自然增长情景以及2030年粮食安全情景流域生境质量总得分和平均得分分别为866630,900357,921055,876231,865370和0.7457,0.7747,0.7925,0.7539,0.7466.2030年3种情景的中上游和下游地区生物多样性都呈现出不同程度的改善趋势,而中游地区则表现出退化趋势.     

Phosphate recovery with granular acid-activated neutralized red mud: Fixed-bed column performance and breakthrough curve modelling

Granular acid-activated neutralized red mud (AaN-RM) has been successfully prepared with good chemical stability and physical strength. However, its potential for industrial application remains unknown. Therefore, the performance of granular AaN-RM for phosphate recovery in a fixed-bed column was investigated. The results demonstrated that the phosphate adsorption performance of granular AaN-RM in a fixed-bed column was affected by various operational parameters, such as the bed depth, flow rate, initial solution pH and initial phosphate concentration. With the optimal empty-bed contact time (EBCT) of 24.27 min, the number of processed bed volumes and the phosphate adsorption capacity reached 496.95 and 84.80 mg/g, respectively. Then, the saturated fixed-bed column could be effectively regenerated with a 0.5 mol/L HCl solution. The desorption efficiency remained as high as 83.45% with a low weight loss of 3.57% in the fifth regeneration cycle. In addition, breakthrough curve modelling showed that a 5-9-1 feed-forward artificial neural network (ANN) could be effectively applied for the optimization of the fixed-bed adsorption system; the coefficient of determination (R²) and the root mean square error (RMSE) evaluated on the validation-testing data were 0.9987 and 0.0183, respectively. Therefore, granular AaN-RM fixed-bed adsorption exhibits promising potential for phosphate removal and recovery from polluted water.