基于置信规则的村镇应急避难场所暴雨灾害链风险诊断*

为解决村镇应急避难场所面临暴雨灾害时的风险问题,基于故障树和置信规则库推理方法,提出暴雨灾害链和村镇应急避难场所功能破坏链相结合的场所避难功能失效风险诊断模型。根据事故致因理论推理灾害节点变量,通过故障树描述灾害链,运用关联规则从历史灾害数据中挖掘规则,建立置信规则库系统,构建村镇应急避难场所功能失效风险诊断模型,并以四川省某寄宿制学校为例进行模型验证。研究结果表明:该模型可实现不同证据组合下村镇应急避难场所功能失效风险的诊断推理;实例的模型诊断结果与实际情况吻合,证实该模型能够科学地诊断村镇应急避难场所面临暴雨等恶劣自然条件时存在的风险,可为村镇应急避难场所规划设计和应急管理提供理论支撑。     

GIS-based assessment of the threat to world forests by patterns of non-sustainable civilisation nature interaction

The paper presents a novel transdisciplinary approach to investigate non-sustainable civilisation–nature interactions in the context of global change. The approach rests on the decomposition of intricate dynamics of Global Change into problematic patterns civilisation nature interactions (‘syndromes’) this is done by an iterative process of observations, data and theoretical system analysis and GIS-based modelling attempts. These syndromes of global change characterise endangering and risky developments of civilisation nature interaction and represent a baseline for measuring and indicating ‘non-sustainability’; in order to have a sustainable development it is necessary to have a far-reaching absence of syndromes. They encompass the core problems of global change, e.g. soil degradation, climate change, threats to biodiversity or global deforestation. Patterns are defined as characteristic constellations of global change trends (‘symptoms’). Cause–effect schemes of symptoms and their interrelations are constituted as complex phenomena resulting from interactions over the different spheres of the Earth system (biosphere, atmosphere, hydrosphere, population, pedosphere, economy, psychosocial sphere, social organisation, science and technology). The approach is illustrated by an analysis of civilisation–nature interaction patterns relevant for global deforestation. Global forest ecosystems have been chosen for this investigation because of their important role in the global carbon cycle and their importance for biological diversity. The resulting geographical patchwork of the combined dispositions and intensities of the different syndromes describes current and future regional threats to forests by their underlying global cause–effect patterns of civilisation–nature interaction.     

Relative density of finds for assessing similarity-based maps of orchid occurrence

Similarity-based mapping of the expected distribution of 10 orchid species was conducted in a study area covering 300 km² in south-eastern Estonia. The observation track and species finds were recorded during fieldwork. Absence locations were generated on the line of observation track. Both presence and absence sites having an in-between distance of at least 100 m were used as training data. Expected presence/absence of a species was calculated according to similarity between the predictable location and selected observations (examples) of presence and absence sites. For each species, the machine learning system identified the best predictive sets by selecting the most useful variables out of 136 map and remote sensing features. Similarity-based estimations were evaluated both by training fit and by independent verification data. Reliability of the predictive maps was expressed also by usefulness ratios—the densities of validation find sites (1) in the predicted presence area relative to the density of those in the predicted absence area, and (2) relative to the share of the observation track in the predicted presence area and in the predicted absence area. The predictive mapping was most efficient for Dactylorhiza incarnata, D. russowii, Epipactis palustris, and Goodyera repens. We conclude that the profound coverage of observations on any larger area is unrealistic and the reliability of similarity-based predictive maps depends on the representativity of existing records relative to the diversity of the study area. The investigation showed that the studied species are much more common in nature than the records in the national database indicate.     

Source Apportionment and Identification of Polycyclic Aromatic Hydrocarbons (PAHs) in Sediment Cores of Selected Creeks in Delta State,Nigeria

Polycyclic aromatic hydrocarbons (PAHs) seasonal variation and sources in Ubeji, Ifie, and Egbokodo Creeks of the Niger Delta, Nigeria, were predicted using diagnostic ratios (DRs) of parent PAHs (Phe/Phe + Ant; Flu/Flu + Pyr; BaA/BaA + Chry, and Ind/Ind + BghiP) and principal component analysis (PCA). A total of 222 sediment core samples were collected during the wet (August 2010) and the dry seasons (January 2011). The samples were dried and Soxhlet extracted; sample extracts were fractionated and analyzed by gas chromatography/flame ionization detection (GC/FID) to identify individual PAHs. The diagnostic PAH ratios revealed that PAHs in the sediment cores at the three creeks, in both seasons, mainly stemmed from the combustion process (pyrogenic sources). Principal component analysis further confirmed that wood-burning, coal combustion, diesel, gasoline-powered vehicular emissions, and petroleum combustion were the dominant contributors of PAHs sources at the sampling location. This study provided information on the origin and sources of PAHs in sediment cores, which may be useful for regulatory actions, environmental quality management, contamination history, and environmental forensic studies.     

Metaphors for the War (or Race) against Climate Change

Despite overwhelming scientific consensus, millions of Americans fail to view climate change as a pressing threat. How can we address this disconnect between science and public opinion? In the present study, we investigated the role of metaphorical framing in shaping attitudes toward climate change. Participants read a brief article that metaphorically described US efforts to reduce carbon emissions as a war or race against climate change, or non-metaphorically described it as the issue of climate change. We further manipulated whether these emission-reduction goals emphasized the relatively near or distant future. We found that, compared to the race frame, the war metaphor made people perceive more urgency and risk surrounding climate change and express a greater willingness to increase conservation behavior, irrespective of the time horizon. Those who read the non-metaphorical report tended to respond in between these two extremes. We discuss the implications of these findings for climate communications.     

华北雨季不同时段严重旱涝特征的诊断研究

利用华北平原地区的１３个站点,４５年（１９５１～１９９５）的降水资料,对夏季４个时段（６月、７月、８月、４～８月）进行了Ｚ指数计算,确定了干旱则主要发生在８月,并可明显看出由５０～６０年代的多水期向７０年代中期以后的少水期的转变,另外,乌拉尔山到巴尔喀什湖附近的低压槽加深和西北太平洋副热带高压的北抬,对华北地区雨季持续性暴雨的产生起着决定性的作用。严重洪涝年的夏季（６～８月）,西北太平洋副热带高压     

非常规突发事件应急决策方法研究

为深入研究非常规突发事件应急决策方法,提高对非常规突发事件的应对能力,调查研究基于应急预案、范例推理和智能规划等3种非常规突发事件的应急决策方法,对比分析这3种方法的特点。研究发现:这3种方法都可以在一定程度上为非常规突发事件提供辅助决策,但是基于智能规划的应急决策方法更适于非常规突发事件复杂的决策环境,有较大发展潜力。然后针对该方法,提出一些需要深入研究的开放性课题。     

基于范例推理的灾害性地震应急物资需求预测研究

基于范例推理(CBR)理论,利用最近相邻法和粗糙集理论搜索相似度最高的主震历史范例,分析各主要物资需求量的影响因素,预测当前范例主震期应急物资需求量。通过序贯决策,采用马尔科夫预测模型预测余震类型,进而搜索余震历史范例,预测余震期应急物资需求量。以"玉树"地震为例,运用该方法估算地震发生后食物类、生活用品类、药品类、工程机械类的需求量。     

基于实测资料海洋内孤立波诊断模型设计

目的基于实测的海洋资料,研究设计海洋内孤立波诊断模型。方法首先对实测的ADCP海洋资料和温度链资料进行质量控制处理,然后利用海洋内波动力学理论和诊断分析技术,建立提取海洋内波特征的诊断模型。结果成功设计了能够提取海洋内波振幅、内波周期、变差流矢量时间序列、内波波致流和内波传播方向的诊断模型。结论该诊断模型通过实测的温度链、潜标、ADCP等连续观测的海洋资料,可诊断出海洋内波周期、内波振幅等特征要素,为实施海洋水文气象保障提供了重要技术手段。     

Characterization of aliphatic hydrocarbons in deep subsurface soils near the outskirts of Beijing, China

Thirty-nine deep subsurface soils( 150—180 cm depth) near the outskirts of Beijing were investagated. The concentrations including n-alkanes from C13 to C36, pristane and phytane were in the range of 0.60 to 170.10μg/g, with a median value of 4.26. Carbon preference index values for n-alkanes ranged from 1.08 to 2.98, with a median value of 1.48. The percentage contribution of “wax” nalkanes was in the range of 6.03%--46.22%. A predominance of odd/even carbon n-alkanes and unresolved complex mixtures with different shapes and ranges were frequently observed. Factor analysis reduced the data set into three principal components and confirming contributions from low ( 19.58% ), medium ( 20.49% ) molecular weight species and long-chain n-alkanes (43.41% ), respectively.Molecular biomarkers such as pristane, phytane, hopanes and steranes were detected. Based on the principal component analysis, the concentration profiles and molecular markers, it was found that the aliphatic hydrocarbons were from both biogenic and anthropogenic sources.