一株抗Cd~（2＋）菌株的筛选及其吸附性能

Cd2＋为一种毒性金属元素,为了实际解决污水中低浓度重金属污染,实现污水达标排放,通过12C6＋重离子束辐照诱变技术筛选到一株耐受Cd2＋的菌株C2,研究其对Cd2＋的抗性和低浓度Cd2＋的吸附性能表明,Cd2＋浓度≤100 mg/L时,C2菌株可以生长繁殖,但随Cd2＋浓度升高受到抑制;SEM分析表明,受到Cd2＋胁迫时,C2产生大量胞外产物与Cd2＋形成络合物;吸附过程中菌粉表面空隙得到填充,形成凸起;红外光谱分析表明,吸附过程中的主要作用基团为醇羟基O—H键、氨基和酰胺基团;C2菌粉和固定化菌球都对Cd2＋有较好的吸附能力,菌粉吸附效果比固定化菌球稍好;菌粉和固定化吸附剂的最佳吸附初始pH值为5~6.0,最佳投加量分别为1.0 g/L和10 g/L（实际含菌量为1.0 g/L）;Cd2＋浓度在2~20mg/L时,在最佳吸附条件下,菌粉和固定化吸附剂对Cd2＋的吸附率均在90%左右;2种吸附剂吸附过程与Langmuir等温模型和拟二级动力学模型拟合最佳;热力学研究表明吸附反应均能自发进行。以上研究结果表明,C2菌粉和固定化吸附剂均可用于污水中低浓度Cd2＋的去除。     

电渗透脱水污泥干燥特性曲线及干燥模型

通过电渗透脱水技术及自然风干技术两种预处理方式降低污泥含水率,当污泥初始含水率相近时,研究经两种方式处理后污泥的干燥特性曲线,并对电渗透脱水污泥干燥特性曲线的优势情况进行探讨。在40—120℃的低温条件下,研究电渗透脱水污泥（泥饼厚度为3．5mm）的干燥特性曲线并分析其干燥特性。通过所得电渗透脱水污泥的干燥特性曲线,引人薄层污泥干燥模型进行数值分析。结果表明,在实验条件下,电渗透脱水污泥的干燥速率要优于自然风干污泥的干燥速率。随着温度的升高,电渗透脱水污泥的干燥速率随之升高,干燥到所需含水率的时间则随之减少。Logarithmic模型比其他模型更适合描述薄层电渗透脱水污泥在低温条件下的干燥特性。     

Ce-PbO_2/C电极的制备及其去除水中酸性红B

采用电沉积法制备铈修饰的PbO2/C电极,通过SEM、XRD、XPS及循环伏安对PbO2/C、Ce-PbO2/C电极进行表征,结果表明,Ce-PbO2/C电极比PbO2/C颗粒细小,表面均匀致密,电化学氧化能力较强,修饰电极中Ce以CeO2的形态存在。以Ce-PbO2/C为工作电极,电解浓度为1 000 mg/L的高盐酸性红B模拟活性染料废水,考察了电压、pH、电解质浓度、极间距对脱色率、氨氮去除率及COD去除率的影响。确定适宜工艺条件为：初始酸性红B溶液浓度为1 000 mg/L,pH值为6,电压10 V,电解时间1 h,电极间距1.5 cm,该条件下脱色率、氨氮去除率和COD去除率分别为99.98%、97.23%和90.17%。通过UV-Vis及GC-MS初步分析了降解过程可能存在的中间产物及降解途径。     

基于智能优化算法的河流水质模型参数的优化简

为探索河流水质模型参数新的求解方法,根据有限的实测数据,分别应用免疫进化优化算法和免疫进化优选的捕食搜索算法,对河流水质模型计算公式中的多参数进行优化。将优化得到的计算公式用于国内外若干河流的河段中DO浓度值的拟合,并与实测结果进行了比较。结果表明,将免疫进化优化算法或免疫进化优选的捕食搜索算法优化得到的水质模型参数精度不仅较高,而且相对稳定,从而为河流水质模型参数的优化提供了一种新方法。     

混凝-超滤短流程工艺处理北方水库原水简

采用混凝-超滤膜短流程工艺对大伙房水库原水进行处理,考察其除污染性能和膜污染情况,并对该短流程工艺参数进行优化。结果表明,当利用超滤膜直接过滤原水时,膜污染较重,并且对污染物质的去除率较低;而采用混凝-超滤短流程工艺时,膜污染得到一定程度上的缓解;当絮凝剂投加量为7 mg/L、膜清洗周期为30 min时,对浊度、COD_Mn和UV₂₅₄的去除率分别为95.61%、40.42%和37.12%,出水水质能够满足生活饮用水卫生标准。     

污泥膜覆盖好氧发酵堆体流场模拟及应用研究简

运用Gambit软件建立了污泥好氧发酵堆体的多孔介质模型,通过自行设计的实验装置获得了堆体的通风粘性阻力系数和惯性阻力系数以及功能膜压差与透气量之间的关系,用Fluent软件分析了堆体不同截面形状及底部通风管数量对堆体通风均匀性的影响,为确定合理的通风管数量及截面几何形状提供理论依据。对上海奉贤区城镇污水厂污泥处理工程发酵仓进行堆体流场模拟,确定堆体采用小拱形截面形状,堆高2 m,宽8 m,底部设置4条通风管,实际运行效果良好。     

Decline of traditional rice farming constrains the recovery of the endangered Asian crested ibis (Nipponia nippon)

Traditional agriculture benefits a rich diversity of plants and animals. The winter-flooded rice fields in the Qinling Mountains, China, are the last refuge for the endangered Asian crested ibis (Nipponia nippon), and intensive efforts have been made to protect this anthropogenic habitat. Analyses of multi-temporal satellite data indicate that winter-flooded rice fields have been continuously reduced across the current range of crested ibis during the past two decades. The rate of loss of these fields in the core-protected areas has unexpectedly increased to a higher level than that in non-protected areas in the past decade. The best fit (R² = 0.87) numerical response model of the crested ibis population shows that a reduction of winter-flooded rice fields decreases population growth and predicts that the population growth will be constrained by the decline of traditional winter-flooded rice fields in the coming decades. Our findings suggest that the decline of traditional rice farming is likely to continue to pose a threat to the long-term survival and recovery of the crested ibis population in China.

Electronic supplementary material

The online version of this article (doi:10.1007/s13280-015-0649-5) contains supplementary material, which is available to authorized users.     

Ecosystem services in coupled social–ecological systems: Closing the cycle of service provision and societal feedback

Both the ‘cascade model’ of ecosystem service provision and the Driver-Pressure-State-Impact-Response framework individually contribute to the understanding of human–nature interactions in social–ecological systems (SES). Yet, as several points of criticism show, they are limited analytical tools when it comes to reproducing complex cause–effect relationships in such systems. However, in this paper, we point out that by merging the two models, they can mutually enhance their comprehensiveness and overcome their individual conceptual deficits. Therefore we closed a cycle of ecosystem service provision and societal feedback by rethinking and reassembling the core elements of both models. That way, we established a causal sequence apt to describe the causes of change to SES, their effects and their consequences. Finally, to illustrate its functioning we exemplified and discussed our approach based on a case study conducted in the Alpujarra de la Sierra in southern Spain.

Electronic supplementary material

The online version of this article (doi:10.1007/s13280-015-0651-y) contains supplementary material, which is available to authorized users.     

中国工业水污染及其成因的de Bruyn模型分析

选取废水、挥发酚、氰化物、COD、石油类和氨氮为中国工业水污染指标,利用分解分析方法将2004—2010年间的污染变化分解为规模效应、结构效应、污染治理效应、清洁技术效应和广义技术效应。结果显示,这5类效应的平均作用强度分别为2.08%、3.04%、15.61%、17.37%和32.88%,其中规模效应和广义技术效应是影响工业水污染的主导效应。各类效应对不同污染物的作用方向并不完全一致,规模效应促进污染物排放量的增加;结构效应以加重污染为主,污染治理效应和清洁技术效应以减轻污染为主;广义技术效应的平均作用强度和负向作用概率均最大,是现阶段中国工业水污染控制最为有效的手段。     

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.