基于化学分析和气象信息综合表征大气铅污染

对天津市1998年11月-1999年2月期间21个玻璃纤维滤膜大气颗粒物样品,采用稀酸浸出法溶解后,测量了ρ(Pb),w(Pb)和同位素丰度比206Pb/207Pb.采用IDAS软件对样品进行聚类分析,分组的结果表明,在不同的天气条件下,各种污染源排放的贡献也不同.48 h反向风迹图提供了大量详实的气象信息,结合ρ(TSP),ρ(Pb),w(Pb)和同位素丰度比的数据,可用于综合表征铅污染的状况.      

边着色图上单色三角形个数的一种证明

通过赋权的方法讨论了单色三角形数量的范围,给出一种较简单的证明,介绍了板植图的存在性。     

彩色红外航片用于水污染遥感监测的定量分析

采用比值法对1：2000彩红外航空胶片透射密度值进行修正,并与运河水质污染参数进行相关统计分析,提出了水质有机污染参数的最佳影象探测密度,建立了相关模型。根据航片密度值估算了苏南大运河水质有机污染参数。结果表明,利用彩红外航片进行大范围水污染遥感监测是可行的。     

水利工程高危作业人因评价体系中的认知图理论研究

为加强水利工程施工安全管理和预防事故发生,通过建立认知地图对高危作业中的人为因素进行了分析。在修订的人为因素分析与分类系统(HFACS)对高危作业人为因素进行识别的基础上,通过引入认知地图理论,建立了人为因素的因果认知地图并进行头节点、尾节点和中心性分析;提出认知广度和认知深度概念,通过数理统计各人为因素出现的频数和采用G1法求其各因素权重,建立二维平面认知地图,对事故的关键影响因素进行了识别和分析。结果表明:教育培训、作业环境和人员健康状况是导致高危作业事故最核心因素,该方法能有效对水利工程人为因素进行分析研究,具有一定适用性。     

Community-scale analysis of the farmland abandonment occurrence process in the mountain region of Ladakh,India

Farmland abandonment has been a significant issue in mountain regions. Thus, many studies have attempted to uncover its driving factors. Most existing studies take a large- or meso-scale view of mountain regions, and there are a few studies that provide community-level analysis. Therefore, it is unclear how ecological and sociopolitical factors are actually combined to influence farmland abandonment at a community level. Thus, we conducted a field survey in the mountain village of Ladakh, northern India, beginning in 2009 and tried to understand the farmland abandonment occurrence process from the viewpoint of traditional altitudinal models of land use. Using data collected during the survey, a relationship between altitude and farmland abandonment was established. However, this relationship was complex, combining traditional forms of land use system in the village. The altitudinal model of land use was very common in mountain regions; thus, discussions of current land use issues should be based on an understanding of such traditional altitudinal systems.     

A Voronoi diagram based population model for social species of wildlife

A population model is presented that accounts for spatial structure within habitat patches. It is designed for social species of wildlife that form social group home ranges that are much smaller than patch size. The model represents social group home ranges by Voronoi regions that tessellate a patch to form a Voronoi diagram. Neighbouring social groups are linked with habitat-confined shortest paths and form a dispersal network. The model simulates population dynamics and makes use of Voronoi diagrams and dispersal networks as a spatial component. It then produces density maps as outputs. These are maps that show predicted animal densities across the patches of a landscape. A construction procedure for the particular Voronoi diagram type used by the model is described. As a test case, the model is run for the squirrel glider (Petaurus norfolcensis), a small arboreal marsupial native to Australia. A time series of density maps are produced that show squirrel glider density changing across a landscape through time.     

A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions

Spatial information in the form of geographical information system coverages and remotely sensed imagery is increasingly used in ecological modeling. Examples include maps of land cover type from which ecologically relevant properties, such as biomass or leaf area index, are derived. Spatial information, however, is not error-free: acquisition and processing errors, as well as the complexity of the physical processes involved, make remotely sensed data imperfect measurements of ecological attributes. It is therefore important to first assess the accuracy of the spatial information being used and then evaluate the impact of such inaccurate information on ecological model predictions. In this paper, the role of geostatistics for mapping thematic classification accuracy through integration of abundant image-derived (soft) and sparse higher accuracy (hard) class labels is presented. Such assessment leads to local indices of map quality, which can be used for guiding additional ground surveys. Stochastic simulation is proposed for generating multiple alternative realizations (maps) of the spatial distribution of the higher accuracy class labels over the study area. All simulated realizations are consistent with the available pieces of information (hard and soft labels) up to their validated level of accuracy. The simulated alternative class label representations can be used for assessing joint spatial accuracy, i.e., classification accuracy regarding entire spatial features read from the thematic map. Such realizations can also serve as input parameters to spatially explicit ecological models; the resulting distribution of ecological responses provides a model of uncertainty regarding the ecological model prediction. A case study illustrates the generation of alternative land cover maps for a Landsat Thematic Mapper (TM) subscene, and the subsequent construction of local map quality indices. Simulated land cover maps are then input into a biogeochemical model for assessing uncertainty regarding net primary production (NPP).     

北上台风生成和路径的卫星云图特征研究

对在太平洋上北上转向登陆日本的台风的生成和移动,利用卫星云图分析和云顶黑体温度计算作了研究。得出结论认为如果有一条或几条长达500～1000km以上的长云带卷入,且云顶黑体温度降低到-70℃以下,而-60℃的范围扩大到3×4个纬距以上,则热带低压很可能在24h以内发展成为热带风暴－台风。本文还确认,海表面温度Tss≥29℃是台风生成最重要的环境条件。通过对北上台风移动的卫星云图特征的研究,指出台风外围卷入的长云带走向往往预示着台风的未来移向。尤其是台风南部来自SW-S-SE方向的季风汇合带云带,不仅意味着台风强度的加强和维持,更预示着台风将往北移动。     

密度对聚氨酯软泡收缩及燃烧行为的影响

采用锥形量热仪研究不同密度聚氨酯软泡(FPUF)的燃烧行为,并根据摄像、体视显微镜所观察的样品形体及泡孔结构的变化来研究火灾条件下FPUF的收缩特性。研究结果表明,收缩是表层泡孔受热发生热解生成焦油所致的泡孔塌缩现象,焦油层下方泡孔结构基本没有变化;FPUF密度越大着火前的收缩速率越小,同一时刻表层泡孔热解生成的焦油层越薄。FPUF的着火燃烧可分为受热收缩、燃烧收缩和池火燃烧三个阶段。中高密度样品HRR曲线先出现平台后出现峰值,分别对应于燃烧收缩和池火阶段,且密度越大HRR曲线的平台越低、峰值越高,燃烧收缩阶段的FIGRA越小,而池火燃烧阶段的FIGRA越大。着火前的快速收缩导致低密度样品的HRR曲线不出现平台只呈现单峰。     

Identification of process operating state with operational map in municipal wastewater treatment plant

This work was performed to develop an operational map for the objective diagnosis of the process operating states of a municipal wastewater treatment plant, for which multivariate statistical analysis techniques were applied. PCA (principal component analysis) was used to reduce the dimension of the data sets obtained from the field municipal wastewater treatment plant. A K-means clustering analysis was used to classify the group according to the property of the process operating state. A Fisher's linear discriminant analysis was used to derive the discriminant function of each classified group. An operational map was developed by scatter-plotting the derived principal components (PCs) on a two-dimensional coordinate according to the classified groups. Using the new data sets not used for developing the operational map, the practical usefulness of the operational map and discriminant function in diagnosing the process operating state were evaluated. Hence, the process operating state could be easily and quickly diagnosed and the dynamic trend of the process operating state was also able to be estimated using the operational map.