基于多目标遗传算法的层级生态节点识别与优化——以常州市金坛区为例

有效生态网络构建可保障区域生态安全,是实现区域可持续发展的重要路径。基于生态节点的内涵解析,建立了“资源型战略点-结构型战略点-结构型薄弱点”的多层级生态节点识别体系,利用金坛区2015年的遥感影像、土地利用数据、POI数据等,通过多目标遗传算法、最小阻力模型等方法,构建了金坛区层级生态网络,并采用表征网络拓扑结构和节点效用性等指标,定量评价生态节点优化前后的生态网络性能,取得以下主要研究结论：（1）多层级生态网络在节点效用、网络整体性能上显著优于一般网络,且在复杂生态水网区域具有较强适用性;（2）金坛区现状生态网络分布不均匀,生态节点布局亟待优化,经优化节点覆盖率提升了17.70%,节点分布均匀度降低45.45%,平均聚类系数提升了87.36%;（3）多层级生态节点体系具有实践应用性,应针对不同类型生态节点采取差别化管理策略。     

Prediction of the concentration of chlorophyll-α for Liuhai urban lakes in Beijing City

Introduction The eutrophication of fresh w ater has becom e a m ain w ater environm ental problem in the w orld. The m ain negative im pacts of fresh w ater eutrophication are w ater quality deterioration and the decrease of hydrophytes and aquatic specie…     

中国废旧农用塑料薄膜的回收与利用

介绍了中国废旧农用塑料薄膜回收利用的现状 ;重点阐述了中国废旧农膜再生造粒技术 ,从工艺、成本利益、再生粒料的应用市场等各方面对该技术进行了详细的介绍和剖析 ,在论述其优越性的同时 ,指出了再生造粒技术存在的问题 ,为研究人员今后的研究工作指明了方向 ;最后对废旧农膜再生造粒技术的应用前景进行了展望。     

武汉市城市生活垃圾资源化处理的经济对策分析

对城市生活垃圾进行资源化处理,不仅有利于解决城市生活垃圾所带来的城市生态环境问题,而且能够获取部分城市发展所需的资源,是发展循环经济的重要环节之一。从武汉市城市生活垃圾资源化处理的现状入手,对武汉市城市生活垃圾在回收再利用、焚烧发电、堆肥等方面的资源化处理措施予以分析,指出武汉市应优先开展回收再利用,适度发展焚烧发电和堆肥的规模,并在此基础上借鉴日本、美国和德国等国城市生活垃圾资源化处理的先进经验,提出要提高城市生活垃圾回收的比率和效率,推进城市生活垃圾资源化处理的产业化发展,以促进武汉市城市生活垃圾的资源化处理进程。     

废旧家电产品回收性能评价指标体系的建立

针对家电产品回收工艺流程决策评价的多阶段、多目标的特点,以及不同类型家电产品和不同回收工艺过程的动态特性,采用层次分类方法建立了以经济指标和环境指标为主要内容的回收工艺流程评价指标体系,对指标体系的组成进行了较为详细的分析,讨论了应该考虑的因素及有关计算方法。以电冰箱为例,用改进的模糊层次分析法与遗传算法相结合,介绍了回收工艺流程的优化决策方法。建立的指标体系不仅可以根据评价对象进行动态修改,而且具有较强的可操作性。     

我国污水资源化存在问题及其对策

我国是缺水国家,水资源短缺严重制约着发展前景,水污染问题又使缺水状况更加突出.实施污水资源化战略可提高水的利用率,缓解部分水资源紧张问题.     

Reasons for non-participation in a kerbside recycling scheme

In this article potential barriers to recycling are explored within a UK community of approximately 8000 households that are piloting a kerbside paper recycling scheme. All 1690 non-users of the scheme were surveyed for their decisions not to participate. A 43% response rate was generated from the survey and the reasons given by householders were coded into 12 categories. The main reasons for non-participation included insufficient paper and lack of space in which to locate the recycling bin. 62% of the non-participants reported that they were recycling paper using other facilities such as local bring schemes and charity collections. Strategies to increase participation in the kerbside scheme are suggested.     

紫外分光光度法在处理废水中的应用

根据二沉池出水特点，指出了以260nm紫外光(F260)评价处理二沉池出水的理论依据，然后确定了E260与COD的关系式为：E260=0．008COD 0．083。最后以E260为评价指标，比较了3种无饥混凝剂在不同pH值条件下对城市二沉池出水的COD去除效果，确定了最佳混凝剂为三氯化铁(FC)及混凝条件：pH4．5，投药量Fe^3 为32mg/L。     

垃圾填埋场渗滤液回流技术研究进展

垃圾填埋场渗滤液回流能有效降低渗滤液中污染物的浓度，提高填埋场稳定化程度，具有广泛的应用前景。本文综述了国内外学者对该技术的研究情况，包括技术特点及相关机理、渗滤液特性研究及回流效果的影响因素，指出了该技术现存的不足之处和尚待解决的理论和技术问题。     

AN EVALUATION OF THE AVAILABLE TECHNIQUES FOR ESTIMATING MISSING FECAL COLIFORM DATA1

ABSTRACT: This paper presents the findings of a study aimed at evaluating the available techniques for estimating missing fecal coliform (FC) data on a temporal basis. The techniques investigated include: linear and nonlinear regression analysis and interpolation functions, and the use of artificial neural networks (ANNs). In all, seven interpolation, two regression, and one ANN model structures were investigated. This paper also investigates the validity of a hypothesis that estimating missing FC data by developing different models using different data corresponding to different dynamics associated with different trends in the FC data may result in a better model performance. The FC data (counts/100 ml) derived from the North Fork of the Kentucky River in Kentucky were employed to calibrate and validate various models. The performance of various models was evaluated using a wide variety of standard statistical measures. The results obtained in this study are able to demonstrate that the ANNs can be preferred over the conventional techniques in estimating missing FC data in a watershed. The regression technique was not found suitable in estimating missing FC data on a temporal basis. Further, it has been found that it is possible to achieve a better model performance by first decomposing the whole data set into different categories corresponding to different dynamics and then developing separate models for separate categories rather than developing a single model for the composite data set.