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.     

基于神经网络的火灾探测系统

本文提出了一种基于神经网络的火灾探测方法,其对火灾的各种状态进行了识别,提高了早期发现火灾的能力,实验结果表明,该方法在火灾自动探测中是一种十分有效的方法。     

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

IntroductionWiththedepletionofspaceforlandfillingofmunicipalsolidwasteandtherisingpricesforrawmaterials ,moreandmorefluidizedbedincineratorshavebeenusedtotreatMSWduetotheprimaryadvantagesofhygieniccontrol,volumereductionandenergyrecovery .BecausetheMSWhas…     

活性污泥系统动力学模拟方法的综合分析

活性污泥法的应用现状和污水中氮磷排放标准的日益严格，使得传统数学模型已满足不了目前的要求，需要对活性污泥系统复杂的动力学规律进行有效模拟。文章在综合分析活性污泥动态模型国内外研究现状的基础上，介绍了3种占主流地位的模型：活性污泥数学模型、神经网络模型和混合模型。这3种模型在污水处理的设计、运行控制和工艺优化等方面各有其独到之处。     

多氯酚QSAR数值模型比较研究

应用多元线性回归分析和新近发展起来的人工神经网络方法进行了一类重要环境污染物多氯酚的定量构效关系研究，并用所建立的模型进行毒性预报，计算值与实验值的比较表明，前的相关系数约为０．９２，后的相关系数约为０．９９．后的百分误差地明显小于前，后的预报能力略好于前，中还讨论了后优于前的的原因。     

石化装置火灾爆炸贝叶斯网络与防护层集成分析

复杂的石油化工装置在运转过程中存在诸多不确定因素,易发生火灾、爆炸等重大事故,给安全生产带来极大威胁。考虑到传统的系统安全分析方法在风险评估中存在一定局限性,引入贝叶斯网络与防护层集成分析模型。应用GeNIe软件将系统故障树转成贝叶斯网络,根据贝叶斯双向推理进行故障预测和诊断,快速识别系统薄弱环节并确定为风险贝叶斯故障节点,结合防护层分析提出相应的独立防护层,确定剩余风险水平。实例应用表明,所构建的贝叶斯网络与防护层集成分析模型对复杂系统进行风险评估是可行的,较传统的事件树、故障树分析方法更加科学、合理。     

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.     

Developing an empirical model of phytoplankton primary production: a neural network case study

We describe the development of a neural network model for estimating primary production of phytoplankton. Data from an enriched estuary in the eastern United States, Chesapeake Bay, were used to train, validate and test the model. Two error backpropagation multilayer perceptrons were trained: a simpler one (3-5-1) and a more complex one (12-5-1). Both neural networks outperformed conventional empirical models, even though only the latter, which exploits a larger suite of predictive variables, provided truly accurate outputs. The application of this neural network model is thoroughly discussed and the results of a sensitivity analysis are also presented.     

提高洪水智能预报中洪峰预报精度方法的研究

针对防洪减灾的实际需要,对如何提高智能网络对洪峰的预报精度问题进行了深入系统的研究,提出了峰值放大修正系数和遗传算法优化网络初始权重相结合的改进算法,历史资料的检验结果表明了这些改进策略的有效性和可靠性.