微生物絮凝剂产生菌的筛选及其絮凝凝除浊性能

采用某污水处理厂活性污泥富集的菌种，通过筛选获得了１０株絮除浊性能较强的菌株。以其中絮凝性能最优的菌株ＧＳ７为研究对象，探讨了影响其絮凝作用的主要因素。结果表明，ＧＳ７的最适生长条件为：培养基ＰＨ６、培养时间１８ｈ、培养温度３０℃。     

高效絮凝剂产生菌的筛选及培养条件

从活性污泥中分离出36株菌株,培养、筛选得到具有絮凝活性的菌株9株,其中有一株絮凝活性较高,命名为B-8,同时考察了培养时间、温度、pH和碳源对絮凝效果的影响。结果表明,培养时间72h,最佳温度为37℃,pH为7.0,葡萄糖为最佳碳源时,该微生物所产生的絮凝剂的絮凝效果最好,絮凝率可达95.75%。     

高分子重金属絮凝剂的制备及含铜废水处理

以聚乙烯亚胺和二硫化碳制备聚乙烯亚胺基黄原酸钠(PEX),研究了PEX对废水中浊度及铜的去除效果.实验证明,在含铜废水中投加PEX,铜和浊度均可被去除,显示了其除浊与除重金属的双重功能.PEX与投加氧化钙的化学沉淀法相比:适用的废水pH值在5.0-6.0之间,低于化学沉淀法所必需的pH值;出水中残余铜远低于化学沉淀法;生成的矾花较大,易于沉降分离,化学沉淀法生成的沉淀细小,难于分离.研究还表明,PEX为两性聚合电解质,在较低的pH值下,分子上的氨基带正电荷,发挥电中和作用达到絮凝除浊效果;在较高的pH值下,分子上的黄原酸基和氨基均可与重金属离子发生配位及螯合反应,从而达到去除重金属离子的效果.     

聚合氯化铝铁(PAFC)絮凝剂污水除磷的研究

用聚合氯化铝铁(PAFC)絮凝剂对城镇生活污水除磷处理效果进行研究,研究内容包括:PAFC用量对模拟污水和实际生活污水除磷效果的影响,污水经PAFC处理后pH值的变化情况,污水酸度对除磷效果的影响,PAFC用量对模拟污水和实际生活污水除浊效果的影响.结果表明:铝铁复合絮凝剂有良好的除磷效果,除磷处理后磷含量可低于0.5mg·l-1,能达到生活污水国家一级排放标准;污水处理后pH值较稳定;除浊效果好,除浊率可达到95%以上.     

霉菌利用酒精废水产絮凝剂

研究2株霉菌利用酒精废水产絮凝剂的絮凝效果以及菌株的生长情况.结果表明:在酒精废水中添加少量营养物质可作为廉价培养基使用;确定了廉价培养基的组成,絮凝剂产量分别达到淀粉培养基产量的94.5%和92.2%;廉价培养基培养HHE-P7和HHE-A26的培养液对高岭土悬浊液、猪场废水、建材废水、餐饮废水、城市生活污水都具有良好的絮凝效果,达到淀粉培养基的92.6%～97.8%.图4表3参19     

利用酱油酿造废水生产微生物絮凝剂及其性能研究

土壤杆菌(Agrobacteriumsp.)LG5-1能够利用酱油酿造废水作为替代培养基生产微生物絮凝剂.在温度为30℃的条件下,研究了碳源、氮源、初始pH及培养时间等条件对絮凝剂的产量及絮凝剂活性的影响,得到了以酱油酿造废水为培养基生产微生物絮凝剂GIL-1的最佳条件:100mL酱油酿造废水中加入2mL乙醇(75%)作为补充碳源,不需添加氮源,调节pH值至8.0左右,培养时间约为48h.获得的絮凝剂GIL-1具有较高的絮凝率和较好的稳定性能,低温储存200d,絮凝率仍可达76.3%.图5参19     

铝铁改性淀粉复合絮凝剂对甲基紫的絮凝机理

以模拟甲基紫染料废水为处理对象,通过测定絮凝R值、Zeta电位、脱色率与CODCr去除率,研究了自配铝铁改性淀粉复合絮凝剂(CAFS)的絮凝特性,初步探讨了其絮凝机理.结果表明,该复合絮凝剂为阳离子型高分子絮凝剂,絮凝初期作用机理趋于"吸附电中和",絮凝后期作用机理以"絮凝架桥"和"卷扫网捕"为主,絮凝性能受pH值影响显著.在pH=11.0,投加量为0.330 mg·L-1时,甲基紫处理效果最优,CODCr去除率达41.0%,色度去除率高达98.0%,其絮体形态密实、含水率低.     

微生物絮凝剂生产菌T1的鉴定及其对生活污水絮凝特性

从活性污泥中分离筛选絮凝剂产生菌,得到一株对高岭土悬浊液具有较高絮凝活性的微生物絮凝菌T1.根据菌株的生理生化鉴定以及16S r DNA基因序列分析,确定该菌株为芽孢杆菌(Bacillus sp),该菌的絮凝活性物质分布在发酵液上清液中.通过正交实验确定最佳生活污水絮凝条件为絮凝剂投加量5%,污水p H值7.0,助凝剂Ca Cl2投加量为2%,在此条件下,T1发酵上清液对生活污水的絮凝率为83.85%.     

城市污水处理脱磷滤料的研究

生物法除磷投资省、成本低,但稳定性较差,当废水中磷酸盐浓度较高时,出水很难达标排放.化学法除磷工艺简单,运行可靠,能达标排放,但运行费用高,产生大量污泥,导致二次污染.文章进行了第三种除磷方法即滤料除磷的研究,并探讨了污水pH及水力停留时间对滤料除磷效果的影响.试验结果表明:煤渣和改性煤渣滤料对城市污水总磷的去除率可以达到54%和71%,但处理后的出水不能达标排放;碳酸钙滤料对城市污水总磷的去除率可以达到83%,能达标排放,但处理效果与城市污水pH值密切相关.城市污水pH值在8～9的范围,出水不能达标排放;pH值在5～8的范围,处理后的出水则可以达标排放,而且污水pH越低,总磷的去除率越高,其最佳水力停留时间为10 min.该方法具有处理效果好、处理成本低、无污染、投资省、占地面积小的特点.     

阳离子化克雷伯氏菌絮凝剂CMBF-NⅢ2在生活污水中的应用

微生物絮凝剂具有无毒性,绿色生产等优点,能够安全地用于给水处理及污废水处理.本文通过阳离子改性和与非生物絮凝剂复配的方法,提高MBF-NIII2的絮凝能力.以MBF-NIII2为原料,利用3-氯-2-羟丙基三甲基氯化铵(CHTAC)对其修饰,合成新型阳离子化的微生物絮凝剂(CMBF-NIII2)以CMBF-NIII2为研究主要对象,对校园生活污水进行处理.通过改变投加量、pH值、沉淀时间与温度,探究CMBF-NIII2絮凝能力的变化规律.将改性前的MBF-NIII2与改性后的CMBF-NIII2分别用于校园生活废水的处理,对比发现当CMBF-NIII2投加量为1.3 mL,pH 4.6,温度为60℃,沉降时间为40 min时,絮凝率达到91.5%,且COD去除率为87.8%,絮凝能力明显优于MBF-NIII2(絮凝率为47.61%),能更高效地絮凝生活污水.以MBF-NIII2与三氯化铁复配处理生活污水,结果表明MBF-NIII2和FeCl_3的投加量分别为10 mg·L~(-1)和15 mg·L~(-1)时,絮凝率可达88.06%,不仅比单独使用MBF-NIII2的处理效果好,还相对减少了絮凝剂的投加量.     

Discounting for personal and social payments: Patience for others,impatience for ourselves

The market rate of return on private investment is often used as the discount rate when conducting cost-benefit analysis (CBA) of public projects. I argue that the decision to invest pits current consumption against future consumption, both of which accumulate to the private decision maker. Public projects, on the other hand, provide benefits that accrue to society in general. To examine the appropriateness of discount rates based on returns to private investment, this paper considers lab experiments designed to test whether individuals discount personal and social benefits at different rates. Personal benefits are captured through personal monetary payments, while social benefits are captured through anonymous donations to charitable organizations. I jointly elicit time and risk preferences and find that subjects discount charitable contributions at significantly lower rates than personal payments.     

Discounting for personal and social payments: Patience for others,impatience for ourselves

The market rate of return on private investment is often used as the discount rate when conducting cost-benefit analysis (CBA) of public projects. I argue that the decision to invest pits current consumption against future consumption, both of which accumulate to the private decision maker. Public projects, on the other hand, provide benefits that accrue to society in general. To examine the appropriateness of discount rates based on returns to private investment, this paper considers lab experiments designed to test whether individuals discount personal and social benefits at different rates. Personal benefits are captured through personal monetary payments, while social benefits are captured through anonymous donations to charitable organizations. I jointly elicit time and risk preferences and find that subjects discount charitable contributions at significantly lower rates than personal payments.     

Parameter estimation for fractional dispersion model for rivers

The fractional dispersion model for natural rivers, extended by including a first order reaction term, contains four parameters. In order to estimate these parameters a fractional Laplace transform-based method is developed in this paper. Based on 76 dye test data measured in natural streams, the new parameter estimation method shows that the fractional dispersion operator parameter F is the controlling parameter causing the non-Fickian dispersion and F does not take on an integer constant of 2 but instead varies in the range of 1.4–2.0. The adequacy of the fractional Laplace transform-based parameter estimation method is determined by computing dispersion characteristics of the extended fractional dispersion model and these characteristics are compared with those observed from 12 dye tests conducted on the US rivers, including Mississippi, Red, and Monocacy. The agreement between computed and observed dispersion characteristics is found to be good. When combined with the fractional Laplace transform-based parameter estimation method, the extended fractional dispersion model is capable of accurately simulating the non-Fickian dispersion process in natural streams.     

Need for multiscale planning for conservation of urban bats

For over a century there have been continual efforts to incorporate nature into urban planning. These efforts (i.e., urban reconciliation) aim to manage and create habitats that support biodiversity within cities. Given that species select habitat at different spatial scales, understanding the scale at which urban species respond to their environment is critical to the success of urban reconciliation efforts. We assessed species–habitat relationships for common bat species at 50‐m, 500‐m, and 1 km spatial scales in the Chicago (U.S.A.) metropolitan area and predicted bat activity across the greater Chicago region. Habitat characteristics across all measured scales were important predictors of silver‐haired bat (Lasionycteris noctivagans) and eastern red bat (Lasiurus borealis) activity, and big brown bat (Eptesicus fuscus) activity was significantly lower at urban sites relative to rural sites. Open vegetation had a negative effect on silver‐haired bat activity at the 50‐m scale but a positive effect at the 500‐m scale, indicating potential shifts in the relative importance of some habitat characteristics at different scales. These results demonstrate that localized effects may be constrained by broader spatial patterns. Our findings highlight the importance of considering scale in urban reconciliation efforts and our landscape predictions provide information that can help prioritize urban conservation work.     

Accounting for Complementarity to Maximize Monitoring Power for Species Management

To choose among conservation actions that may benefit many species, managers need to monitor the consequences of those actions. Decisions about which species to monitor from a suite of different species being managed are hindered by natural variability in populations and uncertainty in several factors: the ability of the monitoring to detect a change, the likelihood of the management action being successful for a species, and how representative species are of one another. However, the literature provides little guidance about how to account for these uncertainties when deciding which species to monitor to determine whether the management actions are delivering outcomes. We devised an approach that applies decision science and selects the best complementary suite of species to monitor to meet specific conservation objectives. We created an index for indicator selection that accounts for the likelihood of successfully detecting a real trend due to a management action and whether that signal provides information about other species. We illustrated the benefit of our approach by analyzing a monitoring program for invasive predator management aimed at recovering 14 native Australian mammals of conservation concern. Our method selected the species that provided more monitoring power at lower cost relative to the current strategy and traditional approaches that consider only a subset of the important considerations. Our benefit function accounted for natural variability in species growth rates, uncertainty in the responses of species to the prescribed action, and how well species represent others. Monitoring programs that ignore uncertainty, likelihood of detecting change, and complementarity between species will be more costly and less efficient and may waste funding that could otherwise be used for management. Contabilización de la Complementariedad para Maximizar el Poder de Monitoreo para el Manejo de Especies     

建立我国生态环境标准体系的初步构想

我国的生态环境标准极其薄弱,建立生态环境标准体系是我国生态环境保护的迫切需要。借鉴我国目前针对污染防治的环境标准体系,提出了我国生态环境标准体系的基本框架,以及生态环境标准制订的原则和重点。     

Microtargeting for conservation

Widespread human action and behavior change is needed to achieve many conservation goals. Doing so at the requisite scale and pace will require the efficient delivery of outreach campaigns. Conservation gains will be greatest when efforts are directed toward places of high conservation value (or need) and tailored to critical actors. Recent strategic conservation planning has relied primarily on spatial assessments of biophysical attributes, largely ignoring the human dimensions. Elsewhere, marketers, political campaigns, and others use microtargeting—predictive analytics of big data—to identify people most likely to respond positively to particular messages or interventions. Conservationists have not yet widely capitalized on these techniques. To investigate the effectiveness of microtargeting to improve conservation, we developed a propensity model to predict restoration behavior among 203,645 private landowners in a 5,200,000 ha study area in the Chesapeake Bay Watershed (U.S.A.). To isolate the additional value microtargeting may offer beyond geospatial prioritization, we analyzed a new high-resolution land-cover data set and cadastral data to identify private owners of riparian areas needing restoration. Subsequently, we developed and evaluated a restoration propensity model based on a database of landowners who had conducted restoration in the past and those who had not (n = 4978). Model validation in a parallel database (n = 4989) showed owners with the highest scorers for propensity to conduct restoration (i.e., top decile) were over twice as likely as average landowners to have conducted restoration (135%). These results demonstrate that microtargeting techniques can dramatically increase the efficiency and efficacy of conservation programs, above and beyond the advances offered by biophysical prioritizations alone, as well as facilitate more robust research of many social–ecological systems.     

A foundation for developing a methodology for social network sampling

Researchers are increasingly turning to network theory to understand the social nature of animal populations. We present a computational framework that is the first step in a series of works that will allow us to develop a quantitative methodology of social network sampling to aid ecologists in their social network data collection. To develop our methodology, we need to be able to generate networks from which to sample. Ideally, we need to perform a systematic study of sampling protocols on different known network structures, as network structure might affect the robustness of any particular sampling methodology. Thus, we present a computational tool for generating network structures that have user-defined distributions for network properties and for key measures of interest to ecologists. The user defines the values of these measures and the tool will generate appropriate network randomizations with those properties. This tool will be used as a framework for developing a sampling methodology, although we do not present a full methodology here. We describe the method used by the tool, demonstrate its effectiveness, and discuss how the tool can now be utilized. We provide a proof-of-concept example (using the assortativity measure) of how such networks can be used, along with a simulated egocentric sampling regime, to test the level of equivalence of the sampled network to the actual network. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James).