城市土地利用生态经济适宜性评价—以天津居住新区为例

土地利用生态经济评价是近年来国际上进行土地开发与规划,合理利用土地资源的一种重要手段.土地利用不仅要考虑经济上的合理性,而且要考虑与其相关的社会效益和环境效益.本文运用城市复合生态系统理论,采用计算机辅助支持下的定性与定量分析相结合方法,通过专家咨询对评价指标体系中的关键指标进行筛选,在上地利用生态潜力与限制性分析的基础上,采用等级合并规则的选图分析法,得到天津市居住新区土地利用生态适宜性等级.该项结果可为天津市有关决策部门进行居住新区规划提供科学依据.     

天津生态城市建设现状定量评价

天津市于1996年将建设生态城市的思想体现在城市的总体规划中，又在2001年将建设生态城市的目标落实于笔端，体现在天津市经济与社会发展中（“十五”规划）。采用适合评价天津市生态城市建设的数学模型对天津城市生态系统进行现状定量分析，其中指标涉及经济发展，社会现状，生态环境3方面的内容，得出天津市城市生态化水平，从而促进天津市生态城市建设，为确定天津市生态城市建设模式提供科学依据。     

主成份分析研究天津市硫酸盐等空气污染

用主成份分析法研究了天津市1980年秋冬两季空气中硫酸盐等的污染,共分析342个样本,每个样本包括17个变量。发现天津市严重的硫酸盐空气污染出现在冬天早晨小风、静风时,总颗粒物、二氧化硫、氮氧化物等在城市下风方向聚积起来的空气污染烟幕中。识别出四个空气污染主要大气物理化学过程,各过程及其对硫酸盐变化的贡献率分别为:聚积过程57.9％、非甲烷烃6.5％、季节变化5.0％、相对湿度3.7％.推测一次排放和/或二氧化硫在颗粒物表面上的非均相似化氧化过程是天津市形成硫酸盐空气污染的主要过程。推算出二氧化硫转化成硫酸盐的表观一级转化速率为6.2±2.0％小时~(-1)或9.3±2.2％小时~(-1),视二氧化硫沉降速率的取值。     

天津市大气环境监测数据的主成分分析

本文采用主成分分析法分析了天津市大气环境监测数据,得到两个主成分;环境质量主成分和风沙主成分;根据主成分点图,将一年分成３个阶段;采暖期,非采暖期和风沙期。     

生态宜居城市建设指标与评价方法——以天津市为例

将定量指标与定性指标融为一体,从城市经济、文化教育、基础设施、生态环境和社会保障5个方面,构建生态宜居城市指标体系,并整合层次分析法（AHP）和风玫瑰图法,进行生态宜居化程度综合评价与分析。以天津市为例进行研究,结果显示本文所建立的指标体系能够客观地反映城市的生态宜居建设情况,并能根据评价结果分析出城市的发展潜力与存在...     

四川省嘉陵江水系污染源的分析

在常规环境监测中 ,对污染源的确定是在定点污染源的排水处设立特定的监测位 ,这种方法获取的结论可信度高 ,易于操作 .但是要求针对每一个特定的污染源排水处都设立特定的监测位 ,耗费人力、物力 ,而且对不定点污染源无效 ,不能及时有效地做出反映 .采用化学多变量因子分析方法对常规监测的水质数据进行分析 ,不仅可获得对该地区污染源的宏观认识 ,还可对该地区水质进行预测 .1　方法原理主成分分析方法 (ＰＣＲ)通过对原始数据阵进行降维后 ,采用一个较小维数 (ｐ)的矩阵来替代原始数据阵 ,使得原始数据阵中包含的信息由新产生的矩阵加以…     

城市生态环境演变趋势的集对分析预测研究——以武汉市为例

自然生态环境是城市社会经济健康发展的重要物质基础,生态足迹作为生态环境承载状态测度的指标,受到社会、经济、人口等多种因素的影响,表现出时空动态性和不确定性等特征,集对分析(SPA)为不确定性问题的建模提出一条新的思路。在对1988—2004年武汉市生态足迹及其社会经济影响因子时间序列分析的基础上,通过构建的集对分析动态模型,对武汉市2005—2020年总生态足迹发展趋势进行了预测。研究结果表明,2005—2020年总生态足迹将由1810.925万hm2增长到2873.857万hm2,呈现出低于GDP和生态效率增长速率的趋势,但生态环境将进一步恶化,对此就武汉市生态系统的发展提出了对策与建议。最后,指出SPA动态模型为城市生态系统预测研究提供了一种可行的解决方案。     

城市山斑鸠(Streptopelia orientalis)栖息地选择研究概况——以拉萨市为例

近年来,随着城市化的发展,野生动物栖息地的减少,使得城市中动物的种类和数量也急剧下降.山斑鸠作为城市鸟类动物多样性的重要组成部分,其栖息环境更需严格保护.通过拉萨市区的6个具有代表性地点对山斑鸠的栖息地选择进行了初步调查研究;用主成分分析(PCA)6个栖息地,最终得到6个地区的主成分综合得分排序表,山斑鸠对栖息地环境的...     

生态足迹改进模型在可持续发展评价中的应用研究

生态足迹分析通过计算生物物理量来衡量人类经济活动对自然生态系统服务的需求与自然生态系统承载力之间的协调程度,因为该方法思路新颖、计算简便而被广泛应用于国家和地区的可持续发展评价中。文章针对Rees W E等提出的生态足迹模型中存在的问题与不足(如:产量因子和当量因子参数选取偏差,生态功能差异表现不充分,模型不具动态性等)提出了改进生态足迹模型。利用改进后的生态足迹模型计算天津市1995—2005年的生态足迹及其动态变化值,天津市耕地、牧草地、林地、水域和化石能源用地人均生态足迹盈亏分别为0.0009,-0.3224,-0.1991,0.0255,-1.9600hm2·人-1,除耕地和水域略有盈余外,牧草地、林地和化石能源用地人均生态赤字均呈递增趋势。1995—2005年间天津市单位产值能耗居高不下、人口规模增长与建筑用地扩张是导致人均生态赤字扩大的根本原因。文章提出的生态足迹改进模型可以动态指示区域或城市经济发展水平与自然生态系统承载力之间的协调程度,为区域可持续发展战略制定和实施提供决策依据。     

天津市创建国家环境保护模范城市的战略思考

根据天津市“十五”计划纲要提出的“实施可持续发展战略,建设生态城市”的要求,在大量调查研究的基础上,从天津的实际出发,本着与时俱进的精神,创造性地提出天津市生态城市建设两步走,把创建国家环境保护模范城市作为生态城市建设的基础和初级阶段的战略构想,文章深入地阐述了天津市“创模”的必要性,迫切性和得大意义。根据国家的标准,客观地,实事求是地分析了天津“创模”的基本条件,存在的突出问题,努力方向。提出了天津“创模”的目标任务,措施和建议。     

Spectral characteristics of plant communities from salt marshes: A case study from Chongming Dongtan, Yangtze estuary, China

The spectral reflectance of recently formed salt marshes at the mouth of the Yangtze River, which are undergoing invasion by Spartina alterniflora, were assessed to determine the potential utility of remotely sensed data in assessing future invasion and changes in species composition. Following a review of published research on remote sensing of salt marshes, 53 locations along three transects were sampled for paired data on plant species composition and spectral reflectance using a FieldSpec? Pro JR Field Portable Spectroradiometer. Spectral data were processed concerning reflectance, and the averaged reflectance values for each sample were reanalysed to correspond to a 12-waveband bandset of the Compact Airborne Spectral Imager. The spectral data were summarised using principal components analysis (PCA) and the relationships between the vegetation composition, and the PCA axes of spectral data were examined. The first PCA axis of the reflectance data showed a strong correlation with variability in near infrared reflectance and ‘brightness’, while the second axis was correlated with visible reflectance and ‘greenness’. Total vegetation cover, vegetation height, and mudflat cover were all significantly related to the first axis. The implications of this in terms of the ability of remote sensing to distinguish the various salt marsh species and in particular the invasive species S. alterniflora were discussed. Major differences in species with various physiognomies could be recognised but problems occurred in separating early colonising S. alterniflora from other species at that stage. Further work using multi-seasonal hyperspectral data might assist in solving these problems.     

基于高光谱技术的土壤镉污染分级评价研究

提出一种利用高光谱技术进行土壤镉污染分级评价的方法。以FieldSpec 3地物光谱仪采集厂矿区土壤光谱反射率175份,随机分成校正集（135份）和检验集（40份）。光谱经小波去噪和标准归一化（SNV）处理后,以主成分分析法（PCA）降维。将降维所得的前5个主成分数据为输入变量,分别采用Fisher线性判别、Byes逐步判别、模糊模式识别、BP-ANN判别以及SVM 5种方法建立了土壤镉污染分级评价模型,并利用40个未知样对模型进行检验。结果表明：Fisher线性判别准确率为77.5%,Byes逐步判别与模糊模式识别预测为80.0%,BP-ANN模型预测精度为82.5%,SVM模型预测精度最高,达85.0%。说明采用高光谱技术进行土壤镉污染分级评价是可行,其中SVM是建模的优选算法。     

The effects of soil properties on the turbidity of catchment soils from the Yongdam dam basin in Korea

Environmental concerns have been raised that suspended solids in turbid water adversely affect human health, and that their removal increases in the cost of water treatment. The Yongdam dam reservoir, located in the southwestern region of Korea, is severely affected by inflowing turbid water after storms. In this study, soil samples were collected from 37 sites in the Yongdam upstream basin to investigate mineralogical and environmental factors associated with the turbidity potential of soils in water environments. Turbidity potential was estimated by measuring the turbidity of soil-suspension solutions after settling for 24 h. The mineralogy of the soils was dominated by four minerals—quartz, microcline, albite, and muscovite—with lesser amounts of hornblende, chlorite, kaolinite, illite, and mixed layer illite. The quartz content was the most variable of the soil mineralogy among the collected samples. Principal-components analysis (PCA) was used to examine relationships between turbidity potential and other soil properties. The variables considered in the PCA included turbidity potential, quartz content, albite content, mean size of soil particles, clay content, clay mineral content, zeta potential, conductivity, and pH of the soil-suspension solution. The first two components of the PCA explained 52% of the overall variation of the selected variables. The first component was possibly explained by physical properties such as the size of the soil particles; the second was correlated with chemical properties of the soils, for example dissolution and extent of weathering. Closer examination of the PCA results revealed that the quartz content of the soils was negatively correlated with their turbidity potential. A linear correlation (r = 0.63) was obtained between measured turbidity potential and that predicted using multiple regression analysis based on the content of clay-sized particles, clay minerals, and quartz, and the conductivity of the soil-suspension solution.     

Interaction structures analysed from water-quality data

Fortnightly observations of water quality parameters, discharge and water temperature along the River Elbe have been subjected to a multivariate data analysis. In a previous study [Petersen, W., Bertino, L., Callies, U., Zorita, E., 2001. Process identification by principal component analysis of river-quality data. Ecol. Model. 138, 193–213] applied principal component analysis (PCA) to show that 60% of variability in the data set can be explained through just two linear combinations of eight original variables. In the present paper more advanced multivariate methods are applied to the same data set, which are supposed to suit better interpretations in terms of the underlying system dynamics.The first method, graphical modelling, represents interaction structures in terms of a set of conditional independence constraints between pairs of variables given the values of all other variables. Assuming data from a multinormal distribution conditional independence constraints are expressed by zero partial correlations. Different graphical structures with nodes for each variable and connecting edges between them can be assessed with regard to their likelihood. The second method, canonical correlation analysis (CCA), is applied for studying the correlation structures of external forcing and water quality parameters.Results of CCA turn out to be consistent with the dominant patterns of variability obtained from PCA. The percentages of variability explained by external forcing, however, are estimated to be smaller. Fitting graphical models allows a more detailed representation of interaction structures. For instance, for given discharge and temperature correlated variations of the concentrations of oxygen and nitrate, respectively, can be modelled as being mediated by variations of pH, which is a representer for algal activity. Considerably simplified graphical models do not much affect the outcomes of both PCA and CCA, and hence it is concluded that these graphical models successfully represent the main interaction structures represented by the covariance matrix of the data. The analysed conditional independence patterns provide constraints to be satisfied by directed probabilistic networks, for instance.     

Some approaches to the construction of a sustainable development index for the Philippines

Principal Component Analysis (PCA) is a convenient tool used in aggregating the indicators of sustainable development and providing indices where different weights are assigned to the various indicators. There are, however, problems in interpreting of indices, especially if time series data are used. This study explores the feasibility of applying recent developments in PCA of time series using Philippine data. We present the comparative advantages of SPCA (Sparse Principal Component Analysis) relative to averaging of an adequacy/inadequacy index and PCA in index construction from various indicators of sustainable development in the Philippines in terms of usefulness and validity of indices being developed. SPCA can attain sparse and non-overlapping loadings without losing a large amount of explained variance compared to PCA. Because of the non-overlapping contribution of variables in SPCA components, indices can have clear and mutually exclusive meanings, facilitating interpretation. Even with a more complicated algorithm, reduced dimensions and simpler interpretation of indices justify the advantages of SPCA over PCA in index construction. The indices are interpreted in terms of the milestone of sustainability in the Philippines. The resulting indices provide an adequate summary of the sustainable indicators and evidence of the importance of leadership and political will in sustainable development.     

K-select analysis: a new method to analyse habitat selection in radio-tracking studies

Two kinds of wildlife habitat studies can be distinguished in the literature: hindcasting and forecasting studies. Hindcasting studies aim to emphasize among a large set of habitat variables those that are of interest for the focus species, whereas forecasting studies are intended to predict habitat selection according to a small number of habitat variables for a given area. We provide here a new analytical tool which relies on the concept of ecological niche, the K-select analysis, for hindcasting studies of habitat selection by animals using radio-tracking data. Each habitat variable defines one dimension in the ecological space. For each animal, the difference between the vector of average available habitat conditions and the vector of average used conditions defines the marginality vector. Its size is proportional to the importance of habitat selection, and its direction indicates which variables are selected. By performing a non-centered principal component analysis of the table containing the coordinates of the marginality vectors of each animal (row) on the habitat variables (column), the K-select analysis returns a linear combination of habitat variables for which the average marginality is greatest. It is a synthesis of variables which contributes the most to the habitat selection. As with principal component analysis, the biological significance of the factorial axes is deduced from the loading of variables. An example is provided: habitat selection by wild boar is studied in a Mediterranean habitat using the K-select analysis. The numerous advantages of the analysis (a large number of variables that can be included, individual variability in habitat selection taken into account, a lack of too strict underlying hypotheses) make it a powerful approach in radio-tracking studies designed to identify habitat variables that are selected by animals.     

Forward selection of explanatory variables

This paper proposes a new way of using forward selection of explanatory variables in regression or canonical redundancy analysis. The classical forward selection method presents two problems: a highly inflated Type I error and an overestimation of the amount of explained variance. Correcting these problems will greatly improve the performance of this very useful method in ecological modeling. To prevent the first problem, we propose a two-step procedure. First, a global test using all explanatory variables is carried out. If, and only if, the global test is significant, one can proceed with forward selection. To prevent overestimation of the explained variance, the forward selection has to be carried out with two stopping criteria: (1) the usual alpha significance level and (2) the adjusted coefficient of multiple determination (Ra(2)) calculated using all explanatory variables. When forward selection identifies a variable that brings one or the other criterion over the fixed threshold, that variable is rejected, and the procedure is stopped. This improved method is validated by simulations involving univariate and multivariate response data. An ecological example is presented using data from the Bryce Canyon National Park, Utah, U.S.A.     

制度-技术-资源协同影响生态经济的效果分析

生态经济是在生态系统承载能力范围内,运用系统工程方法和生态经济学原理改变生产和消费方式,挖掘资源的潜力,建设体制合理与社会和谐环境的经济形态。生态经济的以资源为基础,以政策与制度为导向,以技术创新为支撑,是制度、技术、资源等重要影响变量协同作用的结果,制度变迁、技术创新、资源利用三者共同作用于生态经济运行过程都呈现出了典型的非线性特征;将制度、技术、资源分别视为生态经济系统的高层、中层和低层子系统,以逻辑斯蒂（Logistic）曲线为基础,建构了生态经济发展的非线性动力学模型;最后以系统论的观点分析了制度、技术、资源与生态经济发展、科学发展观贯彻、和谐社会构建之间的互动关系。     

Ranked set sampling allocation models for multiple skewed variables: an application to agricultural data

The mean of a balanced ranked set sample is more efficient than the mean of a simple random sample of equal size and the precision of ranked set sampling may be increased by using an unbalanced allocation when the population distribution is highly skewed. The aim of this paper is to show the practical benefits of the unequal allocation in estimating simultaneously the means of more skewed variables through real data. In particular, the allocation rule suggested in the literature for a single skewed distribution may be easily applied when more than one skewed variable are of interest and an auxiliary variable correlated with them is available. This method can lead to substantial gains in precision for all the study variables with respect to the simple random sampling, and to the balanced ranked set sampling too.