苏皖交界河网区浮游植物群落结构及其与环境因子关系的研究

苏皖交界处的石臼湖是长江下游唯一的通江淡水湖,通过青弋江和姑溪河直接与长江相连,形成了复杂的河网水系。为探讨这种复杂的河网水系中浮游植物种群结构特征,2012年平水期和枯水期分别对石臼湖及其周边入湖支流进行了浮游植物调查,研究河网水系中河网与湖区浮游植物的种类组成、季节变化及与环境因子的关系。结果显示:共采集到浮游植物105种,平水期与枯水期种类数差别不大,两次调查均出现的种类约有70%相同;浮游植物丰度最高可达1×108cell/L,达到了水华暴发的标准。优势种类主要绿藻门和蓝藻门为主,但是从生物量来看硅藻门占优。排序分析显示河网区域浮游植物季节演替明显,且河网与湖区浮游植物群落结构差异明显,进一步分析则表明影响浮游植物主要的环境因子是氮、磷等营养元素。     

湖北省道路网络中心性与土地利用景观格局关系分析

深入理解道路网络与土地利用之间的关系有助于开展可持续的土地利用与道路网络规划。引入多中心性评价模型,对临近中心性、中介中心性、直线中心性3个指标,运用UNA对湖北省道路网络中心性进行测度;选取景观百分比,香农多样性指数,平均斑块大小,平均形状指数4个景观格局指数计算土地利用景观格局特征,并通过斯皮尔曼相关方法对两者之间的关系进行探讨。结果表明:(1)湖北省道路网络中心性格局和空间特征各异,整体上东高西低,临近中心性呈现条带状分布,中介中心性呈孔隙状分布,直线中心性呈片状分布;(2)景观层面上,土地利用景观破碎化和多样化对直线中心性最为敏感,而临近中心性与中介中心性与景观格局指数关系相对复杂;(3)在类型层面上,道路网络中心性与各类土地利用景观格局相关性各异,其中高中心性区域对林地表现为鲜明的排斥和破碎化效应,而对建设用地表现为强烈的吸引和空间集聚现象。该研究以道路网络中心性视角探讨道路网络与土地利用景观格局之间的关系,为区域空间形态和构成及其相关影响因素提供了新的视角。     

Evaluating efforts to build sustainable WEEE reverse logistics network design: comparison of regulatory and non-regulatory approaches

Many developing countries such as Turkey are still making an effort on building an infrastructure for waste of electrical and electronic equipment (WEEE) reverse logistic network design (RLND) processes. It is obvious that policies/laws/regulations related to WEEE management provide a sustainable framework for implementation in the RLND. The question is here: Does the implementation of WEEE directives make sense in terms of reducing the total cost of the network in the long term? This study aims to compare regulatory and non-regulatory situations of WEEE RLND in developing countries by formulating two models named as ‘regulatory’ and ‘non-regulatory’. Model 1 is considered as sustainable with economic, environmental and social goals, and the quotas imposed by the environmental directive are taken into consideration as the data of product return amount. In Model 2, only economic goal is considered, and product return amount is forecasted using Artificial Neural Network (ANN). A case study is conducted in a recycling company in order to evaluate performance of the proposed models. This study contributes to the relevant literature by (1) comparing the regulatory and non-regulatory situations RL models explicitly and (2) proposing ANN model to forecast EEE product return or WEEE quantity for non-regulatory situation.     

Addressing challenges for future strategic‐level emergency management: reframing,networking, and capacity‐building

The mounting frequency and intensity of natural hazards, alongside growing interdependencies between social‐technical and ecological systems, are placing increased pressure on emergency management. This is particularly true at the strategic level of emergency management, which involves planning for and managing non‐routine, high‐consequence events. Drawing on the literature, a survey, and interviews and workshops with Australia's senior emergency managers, this paper presents an analysis of five core challenges that these pressures are creating for strategic‐level emergency management. It argues that emphasising ‘emergency management’ as a primary adaptation strategy is a retrograde step that ignores the importance of addressing socio‐political drivers of vulnerabilities. Three key suggestions are presented that could assist the country's strategic‐level emergency management in tackling these challenges: (i) reframe emergency management as a component of disaster risk reduction rather than them being one and the same; (ii) adopt a network governance approach; and (iii) further develop the capacities of strategic‐level emergency managers.     

Optimal monitoring and control under state uncertainty: Application to lionfish management

State variables in many renewable resource management problems, such as the abundance of a fish stock, are imperfectly observed over time. In systems characterized by state uncertainty, decision makers often invest in monitoring to learn about the level of a stock. We develop a stochastic bioeconomic model of marine invasive species management under state uncertainty. The decision maker in our model simultaneously evaluates optimal investment in monitoring and population control. Using a recently-devised method for solving continuous-state Partially Observable Markov Decision Processes (POMDPs), we find that the ability to learn through monitoring can alter the role of population control in the optimal policy function, for example by reducing control intensity in favor of monitoring. Optimal monitoring depends on the management context, including in our application lionfish population structure. The rich transient dynamics of our model depend critically on the relationship between the initial conditions for information and invader abundance.     

Non-linear autoregressive neural network approach for inside air temperature prediction of a pillar cooler

The volcanic plate made pillar cooler system is designed for outdoor spaces as a heat exchanging medium and reduces the outcoming air temperature which flows through the exhaust port. This paper proposes the use of artificial neural networks (ANNs) to predict inside air temperature of a pillar cooler. For this purpose, at first, three statistically significant factors (outside temperature, airing and watering) influencing the inside air temperature of the pillar cooler are identified as input parameters for predicting the output (inside air temperature) and then an ANN was employed to predict the output. In addition, 70%, 15% and 15% data was chosen from a previously obtained data set during the field trial of the pillar cooler for training, testing and validation, respectively, and then an ANN was employed to predict inside air temperature. The training (0.99918), testing (0.99799) and validation errors (0.99432) obtained from the model indicate that the artificial neural network model (NARX) may be used to predict inside air temperature of pillar cooler. This study reveals that, an ANN approach can be used successfully for predicting the performance of pillar cooler.     

Optimization of thermal performance of the parabolic trough solar collector systems based on GA-BP neural network model

The aim of this paper is to optimize the thermal performance (system output energy, thermal efficiency, and heat loss of cavity absorber) of parabolic trough solar collector (PTC) systems in order to improve its thermal performance, based on the genetic algorithm-back propagation (GA-BP) neural network model. There are a number of undefined problems, fuzzy or incomplete information and a complex thermal performance of the PTC systems. Therefore, the thermal performance prediction of the PTC systems based on GA-BP neural network model was developed. Subsequently, the metrics performances have been adopted to comprehensively understand the algorithm and evaluate the prediction accuracy. Results revealed that the GA-BP neural network model can be successfully used to predict the complex nonlinear relationship between the input variables and thermal performance of the PTC systems. The cosine effect has a great influence on the thermal performance; thereby the geometrical structure of the PTC systems was optimized. It was found that the optimized geometrical structure was beneficial to improve the thermal performance of the PTC system. In conclusion, the GA-BP neural network model has higher prediction accuracy than the other algorithm and it can be feasible and reliable.     

土壤环境监测技术规范中的土壤环境质量评价问题

近年来,场地和土壤污染勘查评价与修复逐渐发展成为环境科学和土木工程领域的热点,场地和土壤修复业务随之愈来愈多。但土壤修复理论和技术发展相对缓慢,无法满足土壤修复行业发展的需要。实践中,在场地和土壤污染勘查与评价的现行标准中,对土壤环境质量评价仅考虑了土壤中某物质成分的人为超标问题,而忽视了土壤中自然异常而导致土壤中某物质成分超标问题的评价,且误将土壤中某物质成分的超标等级定为土壤污染程度等级的全部。针对这些问题,本文通过研究,在深入分析各领域土壤污染评价差异的基础上,提出了土壤污染评价的一般过程、方法和标准,供标准修订参考。     

用微生物群落评价水系的自组织人工神经网络方法

利用原生物群落在PFU上群集过程中的变化,可以评价水质和监测水污染,运用T．Kohonen自组织人工神经网络模型,根据NacArthur－Wilson平衡模型提出了3个功能参数和另外2个结构参数,对常德市16个站四季的水质进行了分析,建立了水质污染程度预测的计算机智能专家系统,预测成功率达100％。     

水利水电工程环境影响综合评价的人工神经网络专家系统

在分级加权评价模型层次结构体系基础上,以三峡工程为背景,应用人工神经网络技术,建立了一个水利水电工程环境影响综合评价的人工神经网络专家系统,该专家系统的知识获取和存贮方式与普通专家系统不同,具有较高的推理效率,较强的容错、自适应和自我更新能力。