基于小波分解及遗传BPNN耦合模型的地表水As浓度预测研究

随着工业的快速发展,水体中污染物超标事件时有发生,造成了较严重的水环境污染问题.水环境监测与预报是环境科学研究的重要内容.为了实现地表水砷（As）污染的准确预报,本研究提出小波分解、遗传算法与BP人工神经网络的耦合建模方法,并结合某河流监测站1998—2016年共19年的地表水质监测数据,通过皮尔逊相关系数和信息指标评价法对模型输入变量进行筛选,最后对比分析了在不同水质参数输入情况下BP人工神经网络（BPNN）、遗传算法改进的BPNN（GABP）、小波-遗传BPNN耦合模型（W-GABP）对后6年（2011—2016年） As浓度预测结果的均方根误差（RMSE）、决定系数（R²）、平均绝对百分比误差（MAPE）,以确立最优模型.结果表明：①多水质参数BPNN、GABP与W-GABP耦合模型预测结果的MAPE分别为17.51%、15.98%、14.46%,单水质参数BPNN、GABP与W-GABP耦合模型预测结果的MAPE分别为18.78%、16.74%、7.83%;②小波分解数据前处理及遗传算法均能较大程度地提高预测模型的精度;③对于地表水水质预报,需对比不同模型在不同输入变量下的预测结果,以获得最佳的预测精度.单水质参数输入的W-GABP耦合模型能较准确地预报地表水As浓度的变化情况,对数据缺乏地区水质监控和地表水As污染防治具有重要意义.     

乡村路网对三峡库区社区水平森林结构的影响

道路作为人为扰动介质被认为是短时空尺度森林景观演化或用途转换的主要驱动力之一,体现为明显的“通道-阻隔”效应。论文以重庆市石柱县西沱镇为例,以3期遥感影像和调研数据为基础,利用ArcGIS的缓冲叠加功能,基于路网动态变化角度,从类型、林龄、起源等主要森林结构方面分析路网变化对森林结构变化的影响。结果表明：1）研究区道路数量先增加后减少,道路质量以等外公路和村级公路的改善为主。2）道路对森林景观干扰性明显。森林从类型、起源、林龄三方面在道路缓冲区内的破碎度均高于非缓冲区内的破碎度。1992—2002年及2002—2014年两个时期相比,前一时期其他灌木林、幼龄林以及萌生林大幅减少,基本表现为道路缓冲区内减少量大于非缓冲区内减少量。后一时期乔木林、中龄林以及萌生林有所增加,其他灌木林和成熟林轻微减少,其中,乔木林在道路缓冲区内增加量大于非缓冲区内增加量,中龄林及萌生林则表现为道路缓冲区内增加量弱于非缓冲区内增加量。3）道路影响域范围内,在无政策性干预条件下,道路的增加和质量的提高很大程度上导致森林景观的退化,低级路段的消失则有助于森林景观的恢复。     

输水情景下白洋淀好氧反硝化菌群落对溶解性有机物的响应

溶解性有机物（DOM）是影响微生物群落演变的重要因素,而生态输水是白洋淀的一个重要特征,为了探究输水情境下DOM对好氧反硝化菌的影响,本文结合水体DOM的组分解析和好氧反硝化高通量测序技术,进行了水体好氧反硝化菌对DOM的响应研究.结果显示,白洋淀水体DOM的相对浓度存在显著差异,河口区要低于内部区;DOM呈现出较强自生源特征,河口区具有更高的分子量和更强的腐殖化;平行因子法解析出3种类蛋白组分和1种类腐殖质组分,类蛋白组分占比达到35.64%~96.38%,与荧光区域积分得到类蛋白占主体的结果相一致.与此同时,该时期水体好氧反硝化菌主要属于变形菌门（Protebacterice）,主要包括Cupriavidus、Aeromonas、Thauera、Shewanella和Pseudomonas,与随机森林筛选出的指示物种相一致;网络分析得到35个网络关键节点,主要隶属于Thauera、Cupriavidus以及Unclassified_bacteria;冗余分析（RDA）显示类腐殖质物质是影响水体整体好氧反硝化菌群落组成的因子,类蛋白物质是影响指示物种群落和关键节点群落结构分布的重要因素.综上可知,水体溶解性有机物中类蛋白组分可以作为筛选适于生态输水期水体特征的耐低温高效好氧反硝化菌的碳源选择.     

2008年秋季珠江三角洲污染气象分析

利用2008年10月15日至11月20日珠江三角洲从化、广州、开平三地的加强观测资料,分析了加强观测期间珠江三角洲的污染气象特征.结果表明,观测期间,造成珠江三角洲空气污染的天气形势可分为冷锋前部型、高压底部型、高压脊控制型3类.冷锋前部型易造成珠江三角洲中部和东北部污染;高压底部型易造成珠江三角洲西南部污染;高压脊控制型易造成珠江三角洲局地性污染.珠江三角洲城市群污染与盛行东北风且日平均风速小于2m/s有密切关系,大部分时间珠江三角洲西南部出现污染,是由于污染物沿着主导风向输送并累积造成.     

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

An understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two non-linear procedures (artificial neural networks—ANNs and generalised additive models—GAMs) and one more traditional linear approach (Multiple linear regression—MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.     

Application of a temperature-dependent von Bertalanffy growth model to bullhead (Cottus gobio)

The most studied and commonly applied model of fish growth is the von Bertalanffy model. However, this model does not take water temperature into account, which is one of the most important environmental factors affecting the life cycle of fish, as many physiological processes that determine growth, e.g. metabolic rate and oxygen supply, are directly influenced by temperature. In the present study we propose a version of the von Bertalanffy growth model that includes mean annual water temperatures by correlating the growth coefficient, k, explicitly and the asymptotic length, L_∞, implicitly to water temperature. All relationships include parameters with an obvious biological relevance that makes them easier to identify. The model is used to fit growth data of bullhead (Cottus gobio) at different locations in the Bez River network (Drme, France). We show that temperature explains much of the growth variability at the different sampling sites of the network.     

Multiple structural modifications to dendritic ecological networks produce simple responses

Structural modifications to landscapes affect the ability of organisms to access different habitat patches. There exist, however, very few general methods by which to relate modifications to expectations of effects, and even fewer that enable understanding of how multiple modifications may interact. In the absence of any guiding principles, ecologists have assumed that interactions will result in complex landscape-scale effects. One way of understanding such effects is through rendering a landscape as a graph or network, among the simplest of which are dendritic networks typified by stream systems. Yet even for stream networks, there are no known general principles concerning the nature of interactions between multiple modifications. We developed a model to describe the ability of fish to access and use different habitat patches within dendritic networks. We used mathematical and numerical analyses of the model to investigate how the habitat value of a network is affected by changes in connectivity and habitat quality, and then to examine interactions between multiple modifications. Rather than showing complex interactions, our analytic and simulation-based results show that the combined effect of multiple modifications approximately equals the sum of individually predicted effects. Dendritic networks thus appear to respond far more simply to multiple modifications than has previously been assumed. These results have implications for stream management planning, and offer a firm foundation from which to better understand population processes within dendritic networks.     

基于人工神经网络法的绿色建筑评价

以实际建筑物为例,介绍了用层次分析法建立绿色建筑评价模型的过程,并分别用层次分析法和人工神经网络法对实际建筑物进行了评价。评价结果显示,人工神经网络法与层次分析法相对误差不到0.5%,表明人工神经网络法作为一种客观科学的评价方法,应用于绿色建筑的评价,能有效降低主观因素带来的影响,会使结果更具有客观性。     

Quantitative risk analysis of urban natural gas pipeline networks using geographical information systems

This paper presents a novel quantitative risk analysis process for urban natural gas pipeline networks using geographical information systems (GIS). The process incorporates an assessment of failure rates of integrated pipeline networks, a quantitative analysis model of accident consequences, and assessments of individual and societal risks. Firstly, the failure rates of the pipeline network are calculated using empirical formulas influenced by parameters such as external interference, corrosion, construction defects, and ground movements. Secondly, the impacts of accidents due to gas leakage, diffusion, fires, and explosions are analyzed by calculating the area influenced by poisoning, burns, and deaths. Lastly, based on the previous analyses, individual risks and social risks are calculated. The application of GIS technology helps strengthen the quantitative risk analysis (QRA) model and allows construction of a QRA system for urban gas pipeline networks that can aid pipeline management staff in demarcating high risk areas requiring more frequent inspections.     

Social capital networks for achieving sustainable development

There have been few empirical studies investigating whether and how social capital (SC) relates to better achievement of sustainability goals and, if so, how. This research investigates the roles three types of SC, namely, bonding, bridging, and bracing SC, play in achieving sustainable development (SD), using a case study of the Japanese region to explore the process of SC accumulation leading to collective action. The research question as to whether SC accumulation makes a difference in the progress towards sustainability is addressed qualitatively and quantitatively using a case study, and network and regression analysis; in particular the impact and functions of bracing SC are closely investigated. The study concludes that SC accumulation can indeed make a difference in achieving sustainability and that bracing SC plays an essential role in expediting the processes of goal sharing and resource flow by connecting various networks across sectors and scales, thereby making collective action possible. These findings suggest that creating an environment in which the generation of all three types of SC is encouraged may help local governments to achieve their desired policy goals for SD.