大型城市综合体火灾风险评估研究*

为提高大型城市综合体火灾风险评估准确性,建立“双主线”火灾风险评估模型,运用贝叶斯网络计算火灾发展主线与人员疏散主线各节点可靠性,并通过改进的火灾关联度法对“双主线”进行协调整合,确定各风险节点动态权重。结果表明:２条主线对于整体火灾风险相互独立又相互联系,“双主线”模型得到的火灾风险值更精确,有效解决传统火灾风险评估模型无法协调统一评价火灾发展与人员疏散的问题。研究结果为定量评估大型城市综合体火灾风险提供新的思路。     

不同进水方式对于EPBR系统厌氧释磷的影响

为了提高实际污水处理工艺中除磷效率,优化系统中厌氧释磷的条件,主要研究了三种不同原水投加方式对厌氧释磷过程的影响。本试验采用UniFed SBR系统内的活性污泥,考察了实际生活污水对活性污泥的释磷影响,采用1次进水、4次进水和连续进水3种不同原水供给方式对于厌氧释磷性能进行比较。研究结果表明,不同进水方式可有效延长实际生活污水的注入时间,大大提高其中有机底物的可利用性,释磷速率由0.082增至0.143 mg/(L.min),其中单位活性污泥释磷量分别为2.24×10-3、3.26×10-3和3.80×10-3mg/mg,这种碳源投加方式的改变,使得利用实际生活污水的厌氧释磷特性得到优化,并提高了实际生活污水中有机碳源的可利用性和除磷效率。     

基于贝叶斯网的网箱养殖区网箱移动预测建模

由专家知识和给定数据,采用著名贝叶斯网络学习K2算法构造了一个海底网箱养殖水环境指标与网箱移动间的贝叶斯网结构模型.该模型能有效的表达网箱养殖环境各个指标之间以及与网箱移动之间的因果关系和影响程度,实验结果表明,试验数据显示准确性为90.6%,以上证明该方法是有效可行的,表明贝叶斯网络是一种很有前途的网箱移动预测评价方法.     

太湖主体湖区对梅梁湾藻类影响定量化研究

针对太湖梅梁湾独特的地理位置和富营养化严重的问题,利用太湖水量水质模型和梅梁湾藻类生长模型,在对模型进行了率定、验证的基础下,模拟了在东南风和西北风两种常见风向的条件下,太湖主体湖区对梅梁湾内营养盐浓度和藻类浓度的影响情况,并将其影响定量化。本次模拟较好地反映了太湖主体湖区在两种常见风场的条件下对梅梁湾内水质影响情况。模拟得到以下结果：在不考虑风场和流场的情况下,梅梁湾内部2001年8月藻类平均浓度为43.18 mg/m³,总氮浓度为2.48 mg/L,总磷浓度为0.248 mg/L,只考虑东南风引起流场的情况下,藻类浓度为41.92 mg/m3,总氮浓度为2.07 mg/L,总磷浓度为0.231 mg/L;考虑东南风风场和流场两个因素的情况下,藻类浓度为53.86 mg/m3,总氮浓度为2.06 mg/L,总磷浓度为0.229 mg/L;同期,只考虑西北风风场的情况下,藻类浓度为42.55 mg/m³,总氮浓度为2.19 mg/L,总磷浓度为0.232 mg/L;考虑西北风风场和流场两个因素的情况下,藻类浓度为50.71 mg/m³,总氮浓度为2.17 mg/L,总磷浓度为0.233 mg/L。结果表明,梅梁湾内部的氮、磷等营养盐浓度受到由太湖主体湖区的风场变化引起的湖流扰动的一定影响;太湖主体湖区对梅梁湾内部藻类浓度的影响主要是由藻类漂移引起的,由营养盐浓度改变而引起的藻类浓度变化非常小。     

污泥有机碳在林地土壤中的分解动态

采用室内培养试验研究了不同温度及污泥施入量的条件下土壤后有机碳的降解动态规律。结果表明,污泥有机碳的矿化速度在前期较快(前3周)。在整个培养过程中,污泥有机碳的累积分解率和周分解率变化规律为12%>6%≥24%;结果还表明,污泥有机碳的分解受温度和污泥施用量的影响。在前3周,50℃培养条件下,有机碳的平均累积矿化量分别比30℃和15℃高出52%和71%。此外,随污泥施用量的增加,有机碳的矿化量增加,而矿化率则呈下降趋势。     

北京市土壤重金属形态分析

采用Tessier连续提取法对北京市朝阳、通州、石景山、海淀、顺义等地土壤进行重金属元素锌、铅、铬的形态分析,并用原子吸收分光光度法测定了重金属元素的含量。研究结果表明,在所测区土壤中,铬主要以可交换态和残渣态存在,说明元素铬较活泼,具有潜在的环境影响能力;锌元素主要以有机结合态和残渣态存在,说明元素锌相对比较稳定;而铅则主要以可交换态形式存在,说明铅最为活泼,应该给予更多的关注。     

江西省肝癌与岩石类型

利用岩石类型资料１０９２个数据，肝癌死亡调查资料１５４４例，研究了肝癌死亡率与人群生存区的岩石类型（岩浆岩、变质岩、碳酸盐岩、碎屑岩、红色碎屑岩、第四系松散岩）的相关性。在此基础上，综合评价了与肝癌有相关性的岩石类型对肝癌的产生和发展的影响。     

Different ranking methods: potentialities and pitfalls for the case of European opinion poll

Prioritization and ranking of objects are primary needs in various substantive fields. It might be said that ranking and comparison are the first step in every risk assessment procedure, whatever the ‘risk’ is intended as: social, environmental, political or economic. Often objects to be ranked are valued by a multi-dimensional attribute which is usually transformed into a composite numerical score. In spite of conventional solutions, the author agrees with recent recommendations of performing multiple ranking, keeping indicators separated. Different innovative methods are analyzed and compared: Hasse diagrams method, POSAC and Nonlinear PCA. The first one stems directly from partial order theory, the second one may be seen as an approximation of Hasse representation in a two dimensional space, whilst the third one belongs to the wide set of non-linear multivariate techniques and it is particularly suitable in handling data of categorical type. Among them, the first two methods compare objects on the basis only on order property of data, whilst the last one simultaneously performs an optimal scale of qualitative attributes and a ranking of objects. The case study is based on the Eurobarometer survey carried out in 2002, at the request of the European Commission, which collects Europeans opinion about various political and social issues. The analysis is focused on users’ level of satisfaction about access easiness, cost, quality, information received and contracts of various services of general interest, such as telephone services, power (gas and electricity) providers, water and postal utilities, urban and rail transports. Separate indicators are set up for each facet of each service within different European regions. Eventually, the ranking of European regions is performed on the basis of the overall performance of services of general interest, as perceived by users. Selected methods lead to almost alike results, still with some differentiations due to different approaches used. As it frequently occurs, each method has its own advantages and pitfalls which are here explored and compared.     

增强UV-B辐射对植物的影响

综述了大气臭氧层的减少是影响地面UV-B辐射增强的主要因素,并进一步简述了增强UV-B辐射对陆生植物影响的最新研究进展。UV-B辐射一方面在分子水平上直接或间接地损害植物的DNA分子和蛋白质的结构,从而影响植物的各种生理生化过程影响;另一方面对植物的形态学特征(如叶表皮结构和花的形态结构等)产生广泛的影响。过强的UV-B辐射穿过叶表皮后直接对植物的光合系统、膜系统和植物生长调控激素等方面产生影响,从而会在植株个体、种群和生态系统等层次上表现出来。增强UV-B辐射作为全球变化因子,可导致敏感植物种类的死亡或退化,较能忍耐UV-B辐射的耐荫树种比阳生树种的适应性更强,能竞争到更多的利用资源,因此,在全球UV-B辐射增强下阳生树种可能会提早退出植物群落,这对森林群落的演替更新会起着重要的作用。文章还综述了UV-B辐射与其它污染胁迫因子和环境因子的复合作用共同对植物产生的影响,以及植物对UV-B辐射的防御和修复等防护对策,提出了今后要加强对生态系统的大尺度、长时间的宏观研究,以及开展增强UV-B辐射对地下生态学,对DNA的修复与表达机理,对植物信号接收和传导的新途径等方面的研究。     

Participatory Bayesian network modeling to understand driving factors of land-use change decisions: insights from two case studies in northeast Madagascar

ABSTRACT

Forest frontiers worldwide reveal trade-offs that are key in mitigating global change. In the forest frontiers of northeast Madagascar, land-use changes result from decisions made by smallholder farmers. In the past, subsistence needs led to increasing shifting cultivation, resulting in forest degradation and deforestation. This study focuses on investigating the role of locally determined factors in land-use change decisions in the forest frontier context. Therefore, we developed a Bayesian network-based land-use decision model that represents the causalities between factors influencing land-use decisions and takes into account local decision-makers’ knowledge. The approach is applied in two comparative case studies in northeast Madagascar. Results show that farmers mostly aim at extending the cultivation of cash crops. These results and the causal mechanisms disentangled for the forest frontier of northeast Madagascar help understand change mechanisms and hence, support decision-making to attain the Sustainable Development Goals.