DGGE、T-RFLP 、LH-PCR对两种活性污泥的微生物种群多样性分析的比较

采用基于PCR扩增的分子生态技术DGGE、T-RFLP和LH-PCR,对2种典型污水处理系统中的活性污泥（生活污泥、焦化污泥）进行微生物种群多样性分析;并以此比较3种技术的优劣,提出不同应用条件下研究方法的选择依据。根据实验结果：DGGE得到的条带较多,但误差来源也最多;T-RFLP技术较为灵敏,但需要选择适当的内切酶,严格控制酶切条件,并且文库比对误差较大;而LH-PCR操作简单,结果稳定性较高。虽然目前尚无法判断3种方法的准确性,但LH-PCR在活性污泥的微生物种群多样性分析中已显示出潜在优势。     

干法腈纶生产废水脱氨氮研究

采用本实验室富集驯化的高效硝化细菌对生化处理后的干法腈纶生产废水进行了脱NH3-N研究,在连续运行装置中考察了DO和MLSS的变化特征,并研究了废水NH3-N负荷和COD负荷对脱NH3-N效果的影响.实验结果表明:该菌能适应干法腈纶生产废水中的难生物降解物质并有效去除废水中的NH3-N,启动期DO呈现"高-低-高"的变化,运行期污泥增长速率呈现"S"型变化;进水NH3-N负荷达到0.405 kg/(m3·d)时仍能维持出水P(NH3-N)在5 mg/L以下,进水COD负荷由0.126 kg/(m3·d)提高到0.975 kg/(m3·d)的过程中,NH3-N去除率始终高于96%.     

活性碳纤维吸附处理对硝基苯酚生产废水

采用活性碳纤维(ACF)吸附处理对硝基苯酚(PNP)生产废水,考察了动态吸附和脱附过程.实验结果表明,在298 K、pH 4时,最佳进水流量为0.25 L/h,ACF的有效吸附量为439.3 mg/g,PNP去除率大于99.95%,TOC去除率大于99.5%,脱附率大于99.5%,ACF的吸附--脱附性能稳定.ACF吸附和电解相结合的工艺能有效去除废水中的有害物质,回收利用PNP和氯化钠,实现了PNP的清洁生产.     

Mathematical analysis of the whole core injection method accuracy for measuring phenanthrene biodegradation rates in undisturbed marine sediments

Rates of 14C-phenanthrene mineralization in contaminated, undisturbed marine sediments were measured using the whole core injection method to assess microbial natural attenuation activity as a function of sediment depth. Submerged sediments were sampled from Eagle Harbor, a marine superfund site in Puget Sound. Experiments show significant biodegradation activities (0.0012-0.0036 day(-1)) in the sediment horizons from 0 to 10 cm. The purpose and scope of this paper is to evaluate the range of experimental conditions giving valid results; a mathematical simulation described competing contaminant 14C-phenanthrene diffusion and simultaneous biodegradation (Monod kinetics), both retarded by sorption. The effect of aging was examined with two sorption models in presumed pseudo-homogenous sediments having effective properties. The simulation predictions provide quantitative guidelines for the successful use of the whole core injection method. (1) The effective Monod constant KS' in sediment is increased by a large partition coefficient KP between sediment and water and makes the apparent 14C-phenanthrene biodegradation approach first-order kinetics. (2) When KS'>1 mg(-1) l(-1), the measured 14C-phenanthrene biodegradation extent is biased by inadequately distributed injected tracer only when less than 7% of the sediment horizon is initially probed and mixed with injected tracer. (3) A short incubation time (<20 days) is necessary when a mobile indicator, e.g., gaseous 14CO2, is used. For longer incubation times, predictions show that a 14CO2 indicator diffuses to adjacent horizons, thus smearing the depth profile of biodegradation. (4) This method employing a radiolabeled tracer provides accurate biodegradation rates for freshly contaminated sediments, and represents an upper limit to the natural phenanthrene biodegradation extents if the contaminant is aged over 50 days.     

Challenges in quantifying biosphere-atmosphere exchange of nitrogen species

Recent research in nitrogen exchange with the atmosphere has separated research communities according to N form. The integrated perspective needed to quantify the net effect of N on greenhouse-gas balance is being addressed by the NitroEurope Integrated Project (NEU). Recent advances have depended on improved methodologies, while ongoing challenges include gas-aerosol interactions, organic nitrogen and N(2) fluxes. The NEU strategy applies a 3-tier Flux Network together with a Manipulation Network of global-change experiments, linked by common protocols to facilitate model application. Substantial progress has been made in modelling N fluxes, especially for N(2)O, NO and bi-directional NH(3) exchange. Landscape analysis represents an emerging challenge to address the spatial interactions between farms, fields, ecosystems, catchments and air dispersion/deposition. European up-scaling of N fluxes is highly uncertain and a key priority is for better data on agricultural practices. Finally, attention is needed to develop N flux verification procedures to assess compliance with international protocols.     

基于社会科学统计程序(SPSS)回归性分析的尾矿库事故预测模型

基于社会科学统计程序(SPSS)软件的分析功能,对调查研究中获取的尾矿库案例进行数据提炼和分类编码,找出相关因子并进行回归性分析。最终目的是找出尾矿库各个因素的内在联系,建立简单的尾矿库事故模型,从而可以初步预测尾矿库事故发生的可能性。该预测模型为尾矿库事故的研究提出了新方法,对于防灾减灾以及保护人民生命财产安全起到了积极作用。     

Mapping afforestation and deforestation from 1974 to 2012 using Landsat time-series stacks in Yulin District, a key region of the Three-North Shelter region, China

The Three-North Shelter Forest Program is the largest afforestation reconstruction project in the world. Remote sensing is a crucial tool to map land use and land cover change, but it is still challenging to accurately quantify the change in forest extent from time-series satellite images. In this paper, 30 Landsat MSS/TM/ETM+ epochs from 1974 to 2012 were collected, and the high-quality ground surface reflectance (GSR) time-series images were processed by integrating the 6S atmosphere transfer model and a relative reflectance normalization algorithm. Subsequently, we developed a vegetation change tracking method to reconstruct the forest change history (afforestation and deforestation) from the time-series Landsat GSR images based on the integrated forest z-score (IFZ) model by Huang et al. (2009a), which was improved by multi-phenological IFZ models and the smoothing processing of IFZ data for afforestation mapping. The mapping result showed a large increase in the extent of forest, from 380,394 ha (14.8 % of total district area) in 1974 to 1,128,380 ha (43.9 %) in 2010. Finally, the land cover and forest change map was validated with an overall accuracy of 89.1 % and a kappa coefficient of 0.858. The forest change time was also successfully retrieved, with 22.2 % and 86.5 % of the change pixels attributed to the correct epoch and within three epochs, respectively. The results confirmed a great achievement of the ecological revegetation projects in Yulin district over the last 40 years and also illustrated the potential of the time-series of Landsat images for detecting forest changes and estimating tree age for the artificial forest in a semi-arid zone strongly influenced by human activities.     

Development of a multi-criteria index ranking system for urban runoff best management practices (BMPs) selection

Low impact development best management practices (LID-BMPs) are considered to be cost-effective measures for mitigating the water quantity and quality impact of urban runoff. Currently, there are many types of LID-BMPs, and each type has its own intrinsic technical and/or economical characteristics and limitations for implementation. The selection of the most appropriate BMP type(s) for a specific installation site is therefore a very important planning step. In the present study, a multi-criteria selection index system (MCIS) for LID-BMP planning was developed. The selection indexes include 12 first-level indices and 26 second-level indices which reflect the specific installation site characteristics pertaining to site suitability, runoff control performance, and economics of implementation. A mechanism for ranking the BMPs was devised. First, each individual second-level index was assigned a numeric value that was based on site characteristics and information on LID-BMPs. The quantified indices were normalized and then integrated to obtain the score for each of the first-level index. The final evaluation scores of each LID-BMP were then calculated based on the scores for the first-level indices. Finally, the appropriate BMP types for a specific installation site were determined according to the rank of the final evaluation scores. In order to facilitate the application of the MCIS BMP ranking system, the computational process has been coded into a software program, BMPSELEC. A case study demonstrating the MCIS methodology, using an LID-BMP implementation planning at a college campus in Foshan, Guangdong Province, is presented.     

Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations

We introduce robust procedures for analyzing water quality data collected over time. One challenging task in analyzing such data is how to achieve robustness in presence of outliers while maintaining high estimation efficiency so that we can draw valid conclusions and provide useful advices in water management. The robust approach requires specification of a loss function such as the Huber, Tukey’s bisquare and the exponential loss function, and an associated tuning parameter determining the extent of robustness needed. High robustness is at the cost of efficiency loss in parameter loss. To this end, we propose a data-driven method which leads to more efficient parameter estimation. This data-dependent approach allows us to choose a regularization (tuning) parameter that depends on the proportion of “outliers” in the data so that estimation efficiency is maximized. We illustrate the proposed methods using a study on ammonium nitrogen concentrations from two sites in the Huaihe River in China, where the interest is in quantifying the trend in the most recent years while accounting for possible temporal correlations and “irregular” observations in earlier years.     

Microbial community composition and electricity generation in cattle manure slurry treatment using microbial fuel cells: effects of inoculum addition

Environmental Science and Pollution Research - Microbial fuel cell (MFC) is a sustainable technology to treat cattle manure slurry (CMS) for converting chemical energy to bioelectricity. In this...