Modelling global-scale potential grassland changes in spatio-temporal patterns to global climate change

Grassland is one of the most widespread vegetation types worldwide and plays a significant role in global carbon cycling. Understanding the sensitivity of grassland to climate change and the effect of climate changes on the grassland ecosystems is a key issue in global carbon cycling. One of the goals of this study was to evaluate the three net primary productivity (NPP)–climate models, i.e. the Miami model, the Schuur model and the classification indices-based model. Results indicated that the classification indices-based model was the most effective model at estimating large-scale grassland NPP. In this research, changes in the spatial pattern of global potential grassland from recent past (1950–2000) to future (2001–2050) A2a scenario were analysed with the integrated orderly classification system of grassland (IOCSG) approach in a Geographic Information System (GIS) environment. NPP was evaluated with the classification indices-based model. Results indicate that under recent past climatic conditions, the main parts of global grassland are the savanna and tundra and alpine grassland and will be converted into the savanna, steppe and semi-desert grassland in A2a scenario. As a whole, areas of grassland will increase by 31.76 million hectares. The classification indices-based model estimated a 12.40% increase of total NPP in grassland from recent past to A2a scenario. It will impose a new issue for future grassland researches to support sustainable development and to provide action relevant knowledge to meet the challenge of climate change.     

外来入侵物种的基因签名及其遗传多样性聚类分析

密码子的使用频率分布能够反映一定的生物特性,因而可作为一种基因签名。本文使用CGR方法来研究外来入侵物种不同组织序列的基因签名及遗传多样性聚类分析,首先得出了刺花莲子草(Alternanthera pungens),紫茎泽兰(Ageratina adenophora),水葫芦(Eichhornia crassipes),微甘菊(Mikania micrantha),土荆芥(Chenopodium ambrosioides),一枝黄花(Solidago canadensis)等6种外来入侵植物的31条序列核苷酸字串长k=1到k=6的情况,并选取k=3,即基因序列的密码子,作为生物特性的一个重要表达。并且构造序列间的CGR欧式距离,进而对外来入侵植物序列遗传多样性进行了聚类分析。通过对所获得的6种外来入侵植物的31条序列的基因签名,得出如下结果：CGR是一种简便且计算量小的方法,且基于CGR方法的基因签名,具有典型的生物特性;入侵植物的基因序列在密码子的使用上是非均衡的,且物种亲缘关系近的,则基因签名相似越高;而且基因签名也揭示出了密码子的第三位碱基偏好使用碱基T的现象,与一般物种密码子第三位碱基偏好G/C情况有强烈反差。此外,从获得的6个物种的31条序列聚类谱系图可以直观看出,入侵植物间存在着一定的亲缘关系,遗传多样性较丰富。由于我们所建立的基于CGR方法的基因签名,不仅能够反映植物特性和进化关系,而且能揭示序列中密码子和碱基的偏好使用情况,因而该方法有利于对外来入侵物种的遗传多样性分析、风险评估及预防控制等提供科学依据。     

基于多元统计的引黄水库沉沙条渠泥沙与水质的变异分析

以济南市引黄供水鹊山水库沉沙条渠为研究对象,测定了9个断面（Y1—Y9）表层水体中的含沙量、总有机碳、总磷、总氮、叶绿素a等19个水质指标.运用主成分分析法和聚类分析法分别探讨水体的主要污染因子及监测断面的沿程归类,并通过室内模拟实验分析了泥沙对水体中N、P浓度的影响.结果表明,沉沙条渠中的水质总体特征以Ⅱ类水和Ⅲ类水为主,其中,超标水质指标为TN,达到劣Ⅴ类水标准,P为藻类生长的限制因子.19个水质指标可由6个主成分来反映,其贡献率分别为：F1（TN、NH3-N、NO3--N、NO2--N、总碱度、Br-、硫酸盐）34.41%,F2（含沙量、浊度、色度、电导率）22.78%,F3（pH和UV254）13.95%,F4（TP）6.280%,F5（CODCr）4.990%和F6（Chl-a）4.830%;9个监测断面归为4类,相应地整个沉沙条渠可划分为4个功能区：入渠布水区（Y1、Y2、Y3）、快速沉降区（Y4、Y5）、缓慢沉降区（Y6、Y7、Y8）和出水稳定区（Y9）.此外,水质与泥沙关系的室内模拟实验表明,条渠来水的P浓度随着泥沙颗粒的沉降而降低,TP在0—15 h内快速下降,而后缓慢下降,30 h内基本达到平衡;而泥沙颗粒对水中N浓度基本没有影响.     

辽东湾大凌河口湿地土壤微生物群落分析

土壤微生物对湿地生态系统的物质转化和能量流动具有重要作用,目前对天然湿地的微生物资源及群落结构缺少研究。应用PCR-DGGE技术研究辽东湾大凌河口湿地土壤中的微生物群落结构,并对其群落组成进行分析,结合冗余分析（RDA）方法考察环境因子对微生物群落结构的影响。共设定7个站位采集土壤样品,直接提取样品中微生物的总DNA,以巢式PCR扩增细菌16S rDNA-V3区片段,应用变性梯度凝胶电泳（DGGE）技术分离片段,研究河口区湿地土壤细菌的群落结构。结果表明：辽东湾大凌河口区土壤具有较为丰富的细菌种群,主要包含Proteobacteria、Actinobacteria和Bacteroidetes几大类门,且同一季节采集的样品在聚类分析图上聚类成簇。RDA分析表明,土壤的温度与盐度2个变量分别解释了6.5%、2.3%的土壤细菌群落结构变化,表明大凌河口区土壤的群落结构变化是多种环境因子共同作用的结果,其中温度的影响略大于盐度。     

Determination of the principal factors of river water quality through cluster analysis method and its prediction

In this paper, an artificial neural network model was built to predict the Chemical Oxygen Demand (COD_Mn) measured by permanganate index in Songhua River. To enhance the prediction accuracy, principal factors were determined through the analysis of the weight relation between influencing factors and forecasting object using cluster analysis method, which optimized the topological structure of the prediction model input items of the artificial neural network. It was shown that application of the principal factors in water quality prediction model can improve its forecasting skill significantly through the comparison between results of prediction by artificial neural network and the measurements of the COD_Mn. This methodology is also applicable to various water quality prediction targets of other water bodies and it is valuable for theoretical study and practical application.     

基于聚类分析的我国火灾空间分布研究

为了解我国火灾事故的空间分布情况,使用系统聚类分析了我国1996 -2010年的火灾统计数据,并结合地理信息系统把分类的结果呈现在相应的区域上进行火灾的空间分布特征研究.聚类分析结果将我国31个省市自治区的火灾事故分为5类,对聚类的结果使用方差分析,类与类之间存在显著差异,说明通过系统聚类得到的分类结果是科学的.进一步分析显示我国火灾事故在空间分布上存在一定的规律性.分析结果可以对了解我国火灾分布特点以及为相关部门提供决策支持.     

区县级产业集聚区突发环境事件应急预案编制思考

产业集聚区是新常态下区域经济发展的重要载体,集聚区应急预案已成为产业集 聚区突发事件责任主体准备程度和处置能力的集中体现。针对笔者参加预案评审和大量 一线调研发现的区县级产业集聚区突发环境事件应急预案存在事件分级不清、机构设置 混乱与处置程序笼统等典型问题,研究提出了基于责任主体的事件等级与响应级别划分 方法、绩效评价导向的应急机构设置与职责分工匹配模式和基于模拟演练的应急处置程 序与履责路径刻画的区县级产业集聚区突发环境事件应急预案编制参考意见。本研究是 当前应急预案“从有到优”升级路径选择的现实思考,对区县级政府/产业集聚区突发 环境事件应急预案修编管理具有重要指导作用。     

广州地区大气污染物输送通道的特征

运用NCEP的GDAS全球气象要素资料与HYSPLIT后向轨迹模式,计算广州地区2010~2012年逐日72h后向轨迹,利用聚类分析方法,分析了广州地区污染物输送通道垂直特征, 计算不同输送通道下广州市颗粒物浓度日均值,同时分析污染物在输送过程中跨越边界层的特征.结果表明:广州地区近地面污染物输送通道可以分为局地、东北路、北路、西北路、东路、南路,不同输送路径对应的PM2.5浓度存在显著差异.局地与东北路输送时,PM2.5浓度最大,北路次之,东路较小,南路输送时最小.西北路输送时,PM10中粗粒子比重明显增加.影响广州地区主要为湖南东南部、江西中部及东南沿海,超过1000m高度的轨迹数所占百分比为16.1%,而低于500m高度的轨迹数所占百分比为 73.1%,近地面污染物的输送主要发生在边界层内部.     

重庆市中小型企业实现规模经济的举措

中小型企业要在激烈的市场竞争中占据主动并获取良好的经济效益,实现规模经济是较佳的选择,其途径是内在规模和外在规模同时并举.基于此,通过对重庆市中小型企业的现状特点分析,认为实现规模经济不但能增强企业的市场竞争力,而且还能为重庆市创造更多的就业机会,并在转移农村剩余劳动力、推动城镇化建设等方面具有重要的现实意义.     

SPATIAL CLUSTERS OF SUBSURFACE DRAINAGE WATER NO3‐N LEACHING LOSSES1

ABSTRACT: Grouping of nitrate‐nitrogen (NO₃‐N) leaching losses from agricultural fields into spatial clusters can help determine the cause/effect relationships for their occurrence. This study was designed to investigate the spatial relationships of low, medium, and high NO₃‐N leaching losses clusters with soil and landscape attributes using cluster and discriminant analysis and the map overlay capability of a geographical information system (GIS). Field measured data of a six‐year (1993 through 1998) study on NO₃‐N leaching losses from 36 experimental fields at the Iowa State University's northeastern research center near Nashua, Iowa, were normalized on an annual basis to compare over the years. The cluster analysis resulted in the formation of three clusters based on the satisfactory evaluation criteria of pseudo‐F statistic, cubic clustering criterion, and R² values. The discriminant analysis, carried out on the basis of clusters, identified elevation and subsurface drainage as the factors that contributed significantly (p > 0.01) in discriminating among these clusters. The verification of discriminant functions developed on these factors predicted the cluster membership for all the groups with an overall accuracy of 86 percent. The map overlay analyses of GIS showed that spatial occurrence of the clusters transporting high NO₃‐N leaching losses was affected by the interaction of soil type and elevation levels.