长江流域鄂西四河流大型底栖动物群落结构特征及水质生物学评价

鄂西水系是国家级水源保护区,也是湖北省重要的生态旅游区。为了解鄂西水生态健康状况,于2014年10月~2017年1月不同时段在宜昌国家可持续发展实验示范区卷桥河（J）、清江利川段（Q）、宜昌市水源地黄柏河（H）和丹江口入库河流天河郧西段（T）开展了底栖动物群落特征研究。研究表明：4个河段共采集到大型底栖动物5门8纲18目61科。基于底栖动物指示种耐污性及其丰度占比分析,表明J、Q和T三河流污染程度沿水流方向呈加重趋势,而H水体健康状况整体较好。运用FBI和SIGNAL底栖生物评价指数,显示四条河流健康程度沿水流方向均呈下降趋势,但H变化趋势平缓,结果表明H水体上下游均处于较健康状态,其他3条河流上游或源头水体健康状况良好,下游受到较重污染。对主要环境因子（TP、NH₃-N、COD_Mn和TN）做主成分分析表明,轴1的解释率达到65.1%,表明轴1可以有效表征主要环境压力梯度。FBI和SIGNAL指数与轴1线性拟合度均较高,表明二者在鄂西河流水生态健康评估中可作为快速生物评价指数。     

种群水平生态风险评价方法概述及其在环境管理中的应用

生态风险评价的目的是保护生态系统功能的完整性、稳定性和持久性,为环境风险管理提供理论依据。然而,目前常见的用于保护生物的化学污染物浓度阈值大多是以个体水平的毒性试验结果为基础,忽略了物种在时间和空间相互作用等因素,不能够完全保护生态环境安全和生态系统功能的延续性。本文从生态风险评价的概念、目的和意义引出种群水平生态风险评价在环境管理应用的重要性,综述了种群水平生态风险评价的科学问题(如密度依赖、遗传变异和空间结构等),归纳了种群水平风险评价主要模型方法及其应用(如Euler-Lotka方程、预测矩阵、个体模型、空间模型和动态能量预算模型等),列举了各国现有法律法规中关于种群水平生态风险评价的规定,以期为种群水平生态风险评价方法研究及在环境管理中的应用提供有益借鉴。     

Experimental study on the characteristics of the strong boiling induced by pressure relief at the top of vertical vessels

In this study, the strong boiling process of a superheated liquid induced by pressure relief at the top of a vertical vessel was experimentally investigated. Through monitoring the response of the pressure at the top of the vessel and the full field morphology of the two-phase flow, the correlations between the pressure rise and the two-phase flow as well as the trend of the characteristic pressure values under different discharge areas and filling rates were analyzed. The results indicated that the expansion of the mist-like two-phase flow, which was generated due to the strong boiling and bubble collapse, was the direct reason for the two pressure rebounds at the top of the vessel. And under the effect of the intermittent expansion of the two-phase flow, the pressure rising rate in the second rebound stage fluctuated. When the discharge area changed, the characteristic pressure values presented different relativeness under different filling rates. In addition, the average pressure rising rate during the second pressure rebound process presented an exponential growth with the increase of the discharge area, and the exponent coefficient decreased with the increase of the liquid filling rate.     

多传感器耦合区域火灾报警技术研究

早期预警与低误报率一直是建筑火灾探测面临的挑战与难题。已有研究多针对特殊场所特定燃烧产物或多种传感器耦合,普适性不强,探测设备成本高,无法大规模应用。通过将火灾烟气蔓延规律与探测器信号时空分布融合,在不增加探测器数量和分布的情况下,提出了一种基于建筑结构微元的多传感器耦合区域火灾报警模型。对典型火灾场景的烟气蔓延情况进行了模拟分析,狭长结构中探测器信号强度变化具有一定的规律性。应用区域火灾报警模型后,报警时间较传统模式提前了14.7%,基本杜绝了单个探测器误报引发建筑物火灾报警的问题。结果表明:多传感器耦合探测模式显著缩短了火灾报警时间,降低了火灾探测误报率,实现了火灾的早期准确识别与预警。     

Prediction of BLEVE mechanical energy by implementation of artificial neural network

In the event of a BLEVE, the overpressure wave can cause important effects over a certain area. Several thermodynamic assumptions have been proposed as the basis for developing methodologies to predict both the mechanical energy associated to such a wave and the peak overpressure. According to a recent comparative analysis, methods based on real gas behavior and adiabatic irreversible expansion assumptions can give a good estimation of this energy. In this communication, the Artificial Neural Network (ANN) approach has been implemented to predict the BLEVE mechanical energy for the case of propane and butane. Temperature and vessel filling degree at failure have been considered as input parameters (plus vessel volume), and the BLEVE blast energy has been estimated as output data by the ANN model. A Bayesian Regularization algorithm was chosen as the three-layer backpropagation training algorithm. Based on the neurons optimization process, the number of neurons at the hidden layer was five in the case of propane and four in the case of butane. The transfer function applied in this layer was a sigmoid, because it had an easy and straightforward differentiation for using in the backpropagation algorithm. For the output layer, the number of neurons had to be one in both cases, and the transfer function was purelin (linear). The model performance has been compared with experimental values, proving that the mechanical energy of a BLEVE explosion can be adequately predicted with the Artificial Neural Network approach.     

宁夏能源结构的低碳优化研究

宁夏的经济结构具有显著的高碳特征,煤炭与电力行业占全区工业产值的比重约1／2,工业产值又占据宁夏GDP的半壁江山。低碳时代的到来不可逆转,减碳是对宁夏经济的重大考验与挑战。采用灰色关联分析法对宁夏各行业与碳排放的关联度进行分析,并结合各相关产业区域专业化系数分析,提出宁夏产业结构升级和能源结构优化的建议。     

基于偏离份额法的我国区域科技经费投入“来源—执行”结构分析

科技经费投入既应注重量的提升,又应关注科技经费投入结构的优化,在加大科技经费投入的同时积极优化投入结构是提高科技资源使用效率的途径。采用偏离份额分析法,从科技经费投入的＂来源—执行＂结构角度测算了我国八大经济区的科技经费投入结构,并从科技经费投入的增长效应、结构效应、竞争力三个方面进行分析,反映出区域科技经费投入发展的速度、动力机制和制度特征。     

山东省经济结构的失衡水平:测度和比较

山东省是中国经济发展最快的省区之一,但随着经济增长的不断推进所积累的矛盾和失衡水平也逐渐提高,经济发展的可持续性日趋降低。为考察山东省经济发展的现状,明确山东省经济结构中存在的问题,该文拟根据文献[1]的方法,构建省域经济结构失衡程度指标体系。考虑到省域经济的特殊性,确定了包括产业结构、投资消费结构、金融结构、省内经济结构和国际收支结构5大类共17个指标作为基本指标体系,对近10年以来山东省经济结构失衡程度进行了测试,并且利用相同的指标体系对广东、江苏、浙江等东部代表性省区以及全国的整体结构失衡水平进行了测度和比较分析,明确了山东省经济结构失衡的绝对水平和相对水平。结果表明:山东省经济结构失衡现象较为突出,经济发展的可持续性日趋降低。本文建议山东省加快第三产业发展,理顺投资与消费的关系,提高国民经济中消费所占比重,平衡省内各市际间经济发展水平才能真正缓解经济结构失衡状况,增强可持续发展的动力。     

麻栎混交林空间结构与物种多样性研究

林分空间结构是指林木在林地上的分布格局,以及林木之间树种、大小、分布等空间关系。林分空间结构决定了树木之间的竞争势及其空间生态位,在很大程度上决定了林分的稳定性、发展的可能性和经营空间的大小。研究目的在于揭示麻栎混交林林分空间结构与物种多样性特征,为林分空间结构调整和物种多样性保护提供理论依据。利用混交度、大小比数和角尺度3种空间结构参数,以及丰富度指数、Shannon-Wiener指数、Pielou指数等物种多样性数量指标,分析了青阳县麻栎混交林林分空间结构以及群落物种多样性特征。结果表明:乔木层优势树种麻栎多为中度以下混交,小叶栎和枫香多为强度和极强度混交;麻栎在空间大小和重要值对比上占有优势地位,小叶栎处于次要地位,枫香处于劣势地位为受压树种;从林分平面布局上看,该林分空间分布多为随机分布。分层物种丰富度和Shannon-Wiener指数依次是乔木层<草(含藤)本层<灌木层;而均匀度Pielou指数则是乔木层<灌木层<草(含藤)本层;生态优势度Simpson指数为乔木层>草本层>灌木层;灌木层有17科18属21种植物,淡竹占优势地位,野茉莉、山胡椒等占亚优势地位;草本层有10科12属13种植物,禾叶土麦...     

Analysis of gas flow behavior in an annular fluidized-bed reactor for polystyrene waste treatment

The characteristics of bubble properties and the chaotic flow behavior of gas were investigated in an annular fluidized bed (0.102 m in inner diameter and 2 m in height) because the behavior of gas flow in such a reactor is one of the important factors governing reactor operation, reactor performance, and the reaction itself. Pressure fluctuations as a state variable for the analysis of gas flow behavior were measured and analyzed. Bubble properties were determined by adopting the cross-correlation function of pressure fluctuations. The resultant chaotic flow behavior of gas was interpreted by means of chaotic parameters such as the Kolmogorov entropy. It was found that the Kolmogorov entropy could be utilized effectively to explain the nonlinear dynamic behavior of gas-solid flow in the annular fluidized bed. The pierced length and rising velocity of bubbles increased with increasing gas velocity, bed temperature, and particle size of the bed material. The bubble frequency increased with increasing gas velocity and bed temperature, while it decreased with increasing particle size of the bed material. Correlations to predict the bubble properties in annular fluidized-bed reactors were suggested.