Measurement and Management of Human-Induced Patterns of Forest Fragmentation: A Case Study

In many tropical developing countries, the twin pressures of population and poverty are resulting in substantial fragmentation of forests, increasing the probability of extinction for many species, Forest fragmentation occurs when large contiguous forests are perforated by small holes or broken up into edges and smaller patches to form a nonforested matrix of open spaces. Thus, forest fragmentation refers not only to the area of forest cleared, but also to the pattern of this clearance, the resulting forest’s spatial properties. Both characteristics are important for species survivability. Apart from opening up forests to many abiotic and biotic influences, fragmentation can affect species dispersal and migration through its effects on forest connectivity. Landscape ecology conceptualizes connectivity as a gradient of critical thresholds, ranging from the large intact forest to the small unconnected forest patch. This article reports results from a multiple-scale analysis of forest fragmentation in Jamaica’s Cockpit Country, an area of once contiguous forest now under threat from human encroachment. Spatial forest data derived from classification of ETM+ satellite imagery are used to measure fragmentation patterns representing various degrees of forest connectivity and density. The results suggest that, overall, 81% of the region is in forest. However, fragmentation patterns also suggest that this forest is riven with extensive perforations indicative of an early stage in the decline of contiguity. The results provided by the spatial fragmentation model are a first step in the design of effective conservation and rehabilitation plans for the area. The article concludes with a discussion of possible multiscale management options for the region.     

基于无人机多传感吊舱系统的火点实时定位方法

林火定位是林火智能监测设备的核心技术。提出了一种基于激光雷达、红外热像仪及组合惯导多数据融合的火点定位方法。首先设计了一个通用的无人机吊舱系统,并基于ROS框架实现数据采集、数据处理和数据传输等功能;其次提出了一个基于无人机吊舱系统的火点全局定位方案,根据红外热像仪成像特性识别火点,同时将密集点云与红外图像进行数据融合,估计出火点的三维空间位置;然后根据无人机位姿提出了一种基于墨卡托投影的火点全局定位方案,得到了火点的GPS位置。最后通过试验得到了该方案的全局定位精度：在实验中经度最大误差为2.36×10^-5°,纬度最大误差为1.84×10^-5°,高程最大误差为0.926 1 m,为其他林火定位方案提供了技术支持。     

生物滴滤器去除挥发性有机物的影响因素

生物滴滤器用于处理挥发性有机物,有机物的去除效率受到许多因素的影响。总结和讨论了填料、营养液、进气等诸多因素对生物滴滤器性能的影响。这些讨论结果有助于在生物滴滤器实际应用中选择合适的设计和操作条件,从而达到更佳的处理效果。     

好氧污泥颗粒化过程的影响因素分析

影响好氧颗粒污泥性质的因素多且复杂,具有灰色系统的特点.应用了灰色关联分析方法对好氧颗粒污泥的重要参数污泥体积指数(SVI)、沉降速率、颗粒粒径和污泥浓度(MLSS)进行了关联影响分析.结果表明:对颗粒污泥SVI的影响顺序为沉降速率＞颗粒粒径＞ MLSS,说明沉降速率对活性污泥的形态转变和颗粒化过程的作用最明显,SVI可作为评判颗粒化进程的一个理想指标;沉降速率对MLSS的影响最弱;颗粒粒径的最佳值为1.3～1.5 mm,此时,颗粒粒径对SVI降低的贡献最大,从而使颗粒污泥的沉降性能得到很大改善,并且使MLSS达到最大.     

驯化菌种降解油制气废水的影响因素研究

利用筛选的3种微生物,在初步研究其对某些化合物降解能力的基础上,考察其降解油制气废水过程中一般因素如温度、pH值、供氧条件、废水浓度以及营养因素如磷源、碳源对微生物降解能力的影响。研究表明,各菌种可在自然环境条件范围内,保持对废水CODCr降解效果的稳定性。葡萄糖、乙醇的加入,可将各菌种对CODCr的去除率分别提高16.9%~26.5%、17.6%~25.6%。     

我国冬小麦霜冻灾害致灾因子危险度评价——基于作物生育阶段气象指标

霜冻是一种威胁农业生产的气象灾害,对其致灾因子危险度的研究,可为大尺度的作物霜冻灾害区划及农业保险提供依据。根据作物生长发育的阶段性原理,以全国751个气象台站56年的逐日最低气温资料以及作物生育期资料为基础,统计得到冬小麦各个生育阶段不同灾害等级（轻霜冻、中霜冻、重霜冻）的年霜冻日数,并计算出冬小麦全生育期内不同等级霜冻的发生概率,基于以上两项指标对冬小麦霜冻致灾因子危险度做出评价。结果表明,研究期内重霜冻年霜日最大值为129d,而轻霜冻和中霜冻霜日最大值分别为23 d和19 d,冬小麦受重霜冻危害时间较长;冬小麦霜冻的频发区、多发区、少发区从北向南依次分布,随着霜冻等级增加,频发区的面积差异不大,但多发区的面积减小、少发区面积增大;冬小麦霜冻灾害危险度等级最低的地区主要分布在低纬度的几个省份,如海南、广东、广西、云南等,危险度等级最高的地区集中在青藏高原地区。     

煤与瓦斯突出影响因素评价分析的模糊层次分析方法

在综合分析煤与瓦斯突出的影响因素的基础上,采用模糊层次分析法（FAHP）建立了煤与瓦斯突出模糊层次分析模型并进行了实例分析,确定了煤与瓦斯突出各影响因素的权重系数。评价结果表明：地应力、地质构造、瓦斯压力等是影响煤与瓦斯突出的主要因素。为制定相应的煤与瓦斯突出防治措施,提供了科学的理论依据和切合实际情况的评价方法。     

西安市城中村事故隐患分析及对策措施研究

以实地调查所得的西安市城中村的安全状况为依据,针对西安市城中村事故频发、救援困难的特点,从系统论的观点,全面分析了西安市城中村这一系统所存在的主要危险有害因素,得到以西安市城中村灾害事故为顶上事件的事故树,充分运用事故树模型,计算得出西安市城中村存在的主要危险有害因素的结构重要度排序。本次调查研究以有效的解决西安市城中村事故隐患为目的,利用系统论和FTA为研究手段,针对目前西安城中村事故隐患提出了“五加强”的对策：加强施工安全规范、加强交通管理、加强治安巡查力度、加强基础设施建设、加强消防知识教育,为全国各大城市城中村事故隐患问题的解决提供一定的参考。     

住宅建筑火灾财产损失影响因素及其实证研究

本研究目的在于通过实证研究确定影响住宅建筑火灾财产损失的关键因素.通过文献回顾,本文提出从居民、建筑物、消防抢救力量和火灾强度四个维度对研究命题展开讨论,构建了住宅建筑火灾财产损失影响因素模型.作者对84户受灾居民进行了问卷调查,并运用主成分因子分析和多元回归方法分析了有效数据,结果发现火灾强度、建筑结构、防火习惯、日常管理、消防设施和室外环境是导致住宅建筑火灾财产损失的关键因素.     

Evaluating the impact of uncertainty and variability on the value chain optimization of a forest biomass power plant using Monte Carlo Simulation

Mathematical programming models have been used to optimize the design and management of forest bioenergy supply chains. A deterministic mathematical model is beneficial for making optimum decisions; however, its applicability to real-world problems may be limited because it does not capture all the complexities, including uncertainties in the parameters, in the supply chain. In this paper, a combination of Monte Carlo Simulation and optimization model is used to evaluate the impact of uncertainty in biomass quality, availability and cost, and electricity prices on the supply chain of a forest biomass power plant. The optimization model is a deterministic mixed integer non-linear model with monthly time steps over a 1-year planning horizon. Variability in biomass quality, i.e. moisture content (MC) and higher heating value (HHV), based on the historical data of a real case study is studied in detail and fitted probability distributions are used in the model, while for electricity prices different scenarios are considered. The results show that the impact of variability in the MC on profit is higher than that of uncertainty in HHV. It is observed that the annual profit ranges between $13.3 million and $17.9 million in the presence of all possible uncertainties while its average is $15.5 million. Uncertainty in biomass availability and cost and electricity price results in the risks of having annual profit of less than $14 million and low monthly storage levels.