合流制排水系统降雨径流污染物的特性及来源

在昆明市典型合流制排水小区对降雨径流进出水水量、水质进行了研究,旨在揭示城市区域合流制排水系统中降雨径漉不同来源的污染物特性及各个污染源的比倒.分别监测了合流制排水系统日常污水以及4场降雨期间小区出口断面、街道、屋顶、庭院降雨径流的水量、水质.结果表明,人为干扰是影响城市径流污染物输出强度的主要因素,城市下垫面降雨径流污染物输出浓度顺序为:道路>庭院>屋顶,道路是城市面源污染的关键源区;道路次降雨径流量约25%,却产出了40%～80%的污染物,而屋顶次降雨径流量约50%,却仅有4%～30%的污染物负荷.合流制排水系统中管道沉积物在降雨期间的迁移是重要的污染源,4场降雨中管道沉积物的TN、TP、SS、COD和BOD5的污染贡献率在30%以上.降雨强度是影响管道沉积物输出的重要因素,在高强度降雨下,管道沉积物污染贡献率高50%以上.在不同的降雨特性条件下,合流制排水系统主导污染源有所不同.     

人工神经网络在水环境质量评价中的应用

为了将人工神经网络应用于水环境质量评价,应用了人工神经网络Ｂ—Ｐ算法,构造了水环境质量评价模型,该模型应用于实例评价结果表明,人工神经网络用于环境质量评价具有客观性,通用性和实用性。     

基于B-P神经网络的环境质量评价方法

提出可将环境质量评价的无论是定量指标还是定性参数转化成"二进制"的"1"或"0",进而将这种二进制数引入B-P网络.通过实例探讨,这种新的B-P网络既适用于定量指标的水质参数又适用于定性指标的水质参数.     

环境监测站INTRANET建设研究

研究探讨了环境监测站内部网络 (Intranet)建设方案、Web数据库开发应用方案、网站的规划方法、结构与内容 ,对其经济性、技术合理性、适用性作了阐述。     

空气质量监测网络发展现状与趋势分析

回顾和分析了国外发达国家和地区空气质量监测网络建设的历程、现状、发展趋势以及存在的不足,针对我国空气质量监测网络的现状和面临的挑战,探讨了我国空气质量监测网络建设的发展思路,并提出构建区域性空气质量监测网络的建议。     

层级化环境质量数据网络直报业务系统的设计和应用

分析了目前环境监测部门的数据传输现状,设计了基于VPN互联网络的环境质量数据层级化直报业务系统,介绍了网络直报系统的业务模型、数据审核和存储策略、系统功能设计及其应用等。     

B-P网络应用于地下水水质监测点优选

以地下水水质评价分级标准作为训练样本,构造B-P网络模型对其进行训练,用训练好的B-P网络对某地的地下水水质监测点进行评判、优选。并与其它方法的结果进行比较,结果表明,B-P网络用于环境测点优选不仅原理直观,而且具有较好的客观性和实用性。     

人工神经网络在化学和环境科学中应用的若干问题

讨论了人工神经网络中最常用的多层前馈网络 ( BP网络 )及误差反向传播算法应用于化学和环境科学时要考虑的几个问题 :网络的输入与数据的归一化 ;隐含层数、隐含层节数和学习速率 ;训练集与监控集 ;网络误差 ;初始权重     

Direct protein-protein interaction network for insecticide resistance based on subcellular localization analysis in Drosophila melanogaster

Abstract

In present study, we constructed the direct protein-protein interaction network of insecticide resistance based on subcellular localization analysis. Totally 177 of 528 resistance proteins were identified and they were located in 11 subcellular localizations. We further analyzed topological properties of the network and the biological characteristics of resistance proteins, such as k-core, neighborhood connectivity, instability index and aliphatic index. They can be used to predict the key proteins and potential mechanisms from macro-perspective. The problem of resistance has not been solved fundamentally, because the development of new insecticides can’t keep pace with the development speed of resistance, and the lack of understanding of molecular mechanism of resistance. As the further analysis to reduce data noise, we constructed the direct protein-protein interaction network of insecticide resistance based on subcellular localization analysis. The interaction between proteins located at the same subcellular location belongs to direct interactions, thus eliminating indirect interaction. Totally 177 of 528 resistance proteins were identified and they were located in 11 subcellular localizations. We further analyzed topological properties of the network and the biological characteristics of resistance proteins, such as k-core, neighborhood connectivity, instability index and aliphatic index. They can be used to predict the hub proteins and potential mechanisms from macro-perspective. This is the first study to explore the insecticide resistance molecular mechanism of Drosophila melanogaster based on subcellular localization analysis. It can provide the bioinformatics foundation for further understanding the mechanisms of insecticide resistance. It also provides a reference for the study of molecular mechanism of insecticide resistance of other insects.     

Identifying key needs for the integration of social–ecological outcomes in arctic wildlife monitoring

For effective monitoring in social–ecological systems to meet needs for biodiversity, science, and humans, desired outcomes must be clearly defined and routes from direct to derived outcomes understood. The Arctic is undergoing rapid climatic, ecological, social, and economic changes and requires effective wildlife monitoring to meet diverse stakeholder needs. To identify stakeholder priorities concerning desired outcomes of arctic wildlife monitoring, we conducted in-depth interviews with 29 arctic scientists, policy and decision makers, and representatives of indigenous organizations and nongovernmental organizations. Using qualitative content analysis, we identified and defined desired outcomes and documented links between outcomes. Using network analysis, we investigated the structure of perceived links between desired outcomes. We identified 18 desired outcomes from monitoring and classified them as either driven by monitoring information, monitoring process, or a combination of both. Highly cited outcomes were make decisions, conserve, detect change, disseminate, and secure food. These reflect key foci of arctic monitoring. Infrequently cited outcomes (e.g., govern) were emerging themes. Three modules comprised our outcome network. The modularity highlighted the low strength of perceived links between outcomes that were primarily information driven or more derived (e.g., detect change, make decisions, conserve, or secure food) and outcomes that were primarily process driven or more derived (e.g., cooperate, learn, educate). The outcomes expand monitoring community and disseminate created connections between these modules. Key desired outcomes are widely applicable to social–ecological systems within and outside the Arctic, particularly those with wildlife subsistence economies. Attributes and motivations associated with outcomes can guide development of integrated monitoring goals for biodiversity conservation and human needs. Our results demonstrated the disconnect between information- and process-driven goals and how expansion of the monitoring community and improved integration of monitoring stakeholders will help connect information- and process-derived outcomes for effective ecosystem stewardship.