基于小波神经网络的芦苇潜流人工湿地水质预测

人工湿地系统对污水的处理效果好,工艺简单,投资运行费用低,但影响其出水水质的因素很多,并且往往是非线性的,因此目前很难将这些影响因素模型化并用于水质预测. 已有的预测方法不是过于复杂就是预测精度不高. 神经网络是一种具有较强预测能力的新方法,适用于各种非线性模型的预测. 在小试研究的基础上,使用3种不同的、经过训练的小波神经网络,对芦苇潜流人工湿地沿程各采样口的水温,ρ(DO),pH,Eh和ρ(COD_Cr)等水质指标进行了预测. 结果显示,各指标的平均相对误差分别为:水温≤4.21%,pH≤1.36%,ρ(DO)≤9.77%,Eh≤6.50%,ρ(COD_Cr)≤17.76%,表明小波神经网络模型适用于人工湿地模型的预测.      

Modelling spatial distribution of hard bottom benthic communities and their functional response to environmental parameters

In this study we analyzed and modelled spatial distribution of hard bottom benthic communities in the Lagoon of Venice, and used the model to derive functional response of these communities to changing environmental conditions.     

Analysis of the relationship between ambient levels of O3, NO2 and NO as a function of NOx in the UK

Monitoring data from the UK Automatic Urban and Rural Network are used to investigate the relationships between ambient levels of ozone (O₃), nitric oxide (NO) and nitrogen dioxide (NO₂) as a function of NO_x, for levels ranging from those typical of UK rural sites to those observed at polluted urban kerbside sites. Particular emphasis is placed on establishing how the level of ‘oxidant’, OX (taken to be the sum of O₃ and NO₂) varies with the level of NO_x, and therefore to gain some insight into the atmospheric sources of OX, particularly at polluted urban locations. The analyses indicate that the level of OX at a given location is made up of NO_x-independent and NO_x-dependent contributions. The former is effectively a regional contribution which equates to the regional background O₃ level, whereas the latter is effectively a local contribution which correlates with the level of primary pollution. The local oxidant source has probable contributions from (i) direct NO₂ emissions, (ii) the thermal reaction of NO with O₂ at high NO_x, and (iii) common-source emission of species which promote NO to NO₂ conversion. The final category may include nitrous acid (HONO), which appears to be emitted directly in vehicle exhaust, and is potentially photolysed to generate HO_x radicals on a short timescale throughout the year at southern UK latitudes. The analyses also show that the local oxidant source has significant site-to-site variations, and possible reasons for these variations are discussed. Relationships between OX and NO_x, based on annual mean data, and fitted functions describing the relative contributions to OX made by NO₂ and O₃, are used to define expressions which describe the likely variation of annual mean NO₂ as a function of NO_x at 14 urban and suburban sites, and which can take account of possible changes in the regional background of O₃.     

环氧化合物分子结构与致变活性的关系

由化学物质毒性效应登录(RTECS)1998年光盘系统检索获得86种环氧化合物对鼠沙门氏菌致变活性数据.应用主成分分析法从11种分子描述符中选择出5种对致变活性有明显影响的描述符:碳原子数,苯环数,氢原子数,烷基数和氧原子数.通过样本学习集训练并优化神经网络结构,建模分类预报:对于86个样本,低和高2类活性,正确分类率达到92%.结果表明:3～4个骈接苯环的环氧化物的特殊结构具有很强的致变活性.     

HCl emission characteristics and BP neural networks prediction in MSW/coal co-fired fluidized beds

IntroductionWiththedepletionofspaceforlandfillingofmunicipalsolidwasteandtherisingpricesforrawmaterials ,moreandmorefluidizedbedincineratorshavebeenusedtotreatMSWduetotheprimaryadvantagesofhygieniccontrol,volumereductionandenergyrecovery .BecausetheMSWhas…     

正矩阵因子分解法评价北京市PM_(10)和SO_2监测网络

为辨识北京市大气污染物变化规律相同的区域,筛选监测网络中表征冗余信息的监测点,使用正矩阵因子分解法分别对北京市PM10和SO2监测网络进行分析与评价.分析获得北京市PM10和SO2明显的季节变化特征:PM10浓度春季最高,夏季较低;SO2浓度冬季最高,夏季较低.采用正矩阵分解法对PM10监测网络解析出3个因子,对应北京市PM10污染变化特征相同的3个区域,分别为区域1代表监测点为车公庄与石景山古城,区域2代表监测点为位于城东的前门、天坛、农展馆与奥体中心,区域3代表监测点定陵.结果表明区域2监测点密度较大,存在表征冗余信息的监测点,可以考虑撤销或迁移部分监测点.对SO2监测网络解析出6个因子,分别对应北京市SO2污染变化特征相同的6个区域,代表监测点位分别为定陵、古城、东四、奥体中心、农展馆与天坛和前门与车公庄.评价结果表明北京市PM10和SO2监测网络都存在冗余信息的监测点,可以根据分区结果考虑撤销或者迁移部分监测点,优化监测网络.     

Social Networks and Community-Based Natural Resource Management

We conducted case studies of three successful examples of collaborative, community-based natural resource conservation and development. Our purpose was to: (1) identify the functions served by interactions within the social networks of involved stakeholders; (2) describe key structural properties of these social networks; and (3) determine how these structural properties varied when the networks were serving different functions. The case studies relied on semi-structured, in-depth interviews of 8 to 11 key stakeholders at each site who had played a significant role in the collaborative projects. Interview questions focused on the roles played by key stakeholders and the functions of interactions between them. Interactions allowed the exchange of ideas, provided access to funding, and enabled some stakeholders to influence others. The exchange of ideas involved the largest number of stakeholders, the highest percentage of local stakeholders, and the highest density of interactions. Our findings demonstrated the value of tailoring strategies for involving stakeholders to meet different needs during a collaborative, community-based natural resource management project. Widespread involvement of local stakeholders may be most appropriate when ideas for a project are being developed. During efforts to exert influence to secure project approvals or funding, however, involving specific individuals with political connections or influence on possible sources of funds may be critical. Our findings are consistent with past work that has postulated that social networks may require specific characteristics to meet different needs in community-based environmental management.     

有机化合物厌氧生物降解性的测定和预测

测定有机物厌氧生物降解性的方法包括非特性参数和特性参数测定法。本文着重介绍有机物厌氧生物降解性的筛选测定法，以基团贡献法为基础，不外加其它理化参数的有机物结构与生物降解性关系的预测已经由简单的线性模型发展至专家系统和人工神经网络模型，并显示出极好的应用前景。     

Short-term Model of the Production of Construction Aggregates in Taiwan Based on Artificial Neural Networks

BACKGROUND: Taiwan's geography and limited stock of sandstone have caused sandstone resources to gradually decline to the point of exhaustion after long-term excavation. Moreover, the Taiwanese government has continuously increased the amount of land area near rivers that cannot be excavated to facilitate riverbed remediation and promote conservation of water resources. Accordingly, predicting and managing the annual production of construction aggregates in future construction projects, and dealing appropriately with some thorny problems, for instance, demand that excess supply, excessive excavation, unregulated excavation, and the consequent environmental damage, will significantly affect the efficient use of natural resources in a manner that accords with the national policy of Sustainable Development (SD). METHODS:. This study establishes an empirical model for forecasting the annual production of future construction aggregates using Artificial Neural Networks (ANN), based on 15 relevant socio-economic indicators, such as indicator of annual consumption of cement. A sensitivity analysis is then performed on these indicators. RESULTS AND DISCUSSION: This work applies ANN to estimate the annual production of construction aggregates; the estimates, the verification of the model and the sensitivity analysis are all acceptable. Furthermore, sensitivity analysis results indicate that the annual consumption of cement is the indicator that most strongly influences the production of construction aggregates, as well as whether construction waste can be recycled and steel structures can be used in buildings, helping to reduce the future production of construction aggregates in Taiwan. CONCLUSIONS: The elaborate prediction methodology presented in this study avoids some of the weaknesses or limitations of conventional linear statistics, linear programming or system dynamics. Additionally, the results not only provide a short-term prediction of the production of construction aggregates in Taiwan, but also provide a viable and flexible means of verifying quality certification of the production data of construction aggregates in the future by incorporating those relevant socio-economic indicators. RECOMMENDATIONS AND OUTLOOK: The continuity and quality of the database of relevant indicators used in this study should be closely scrutinized in order to ensure the SD means of exploiting resources.