基于GIS的苏州河非点源污染的总量控制

通过模拟试验修正非点源污染模型;利用GIS技术建立非点源污染信息数据库,并对研究区域和苏州河进行网格和河网概化;以COD_Cr为控制因子,根据各断面的水环境目标,计算其水环境容量,以确定其削减量.结果表明,为达到苏州河2010年总量控制目标,各种非点源污染均须不同程度地削减,以村镇用地和畜禽养殖的污染负荷削减为主,村镇和鱼塘的单位网格削减量较高.最后提出非点源污染的总量控制对策,寻求目标总量控制和容量总量控制的有机结合.     

Credibility and advocacy in conservation science

Conservation policy sits at the nexus of natural science and politics. On the one hand, conservation scientists strive to maintain scientific credibility by emphasizing that their research findings are the result of disinterested observations of reality. On the other hand, conservation scientists are committed to conservation even if they do not advocate a particular policy. The professional conservation literature offers guidance on negotiating the relationship between scientific objectivity and political advocacy without damaging conservation science's credibility. The value of this guidance, however, may be restricted by limited recognition of credibility's multidimensionality and emergent nature: it emerges through perceptions of expertise, goodwill, and trustworthiness. We used content analysis of the literature to determine how credibility is framed in conservation science as it relates to apparent contradictions between science and advocacy. Credibility typically was framed as a static entity lacking dimensionality. Authors identified expertise or trustworthiness as important, but rarely mentioned goodwill. They usually did not identify expertise, goodwill, or trustworthiness as dimensions of credibility or recognize interactions among these 3 dimensions of credibility. This oversimplification may limit the ability of conservation scientists to contribute to biodiversity conservation. Accounting for the emergent quality and multidimensionality of credibility should enable conservation scientists to advance biodiversity conservation more effectively.     

基于现代信息技术的城市灾害应急管理系统

城市灾害应急管理系统是一项复杂的大系统工程。本文着重从应急管理过程中的应急资源调配、应急预案制定等方面阐述了现代信息技术在城市灾害应急管理中的应用与优势。认为应急管理信息系统的建立目的不是为了预测和估算,而应是当灾害发生时,对已造成的灾情局面做出快速的辅助决策支持。基于此,利用SuperMapObject5.0,Visual Basic6.0,SQL Server2000等现代信息技术软件,以辅助决策、资源快速查询以及生命线管网易损处调查为目的,本文建立了一个局部地区灾害应急管理信息系统。系统的数据库建立更突出了为应急管理服务所需的基础信息、空间数据、应急预案和应急资源等信息划分方式;结合已有的震害预测理论和方法,开发了供水系统和交通系统的震害预测模块,事先为灾害应急决策提供可能的震害信息。系统的开发过程进一步证明了GIS在城市灾害应急管理中有着直观显示、容易操作、辅助准确决策等优势。     

Ti-Si-Ox脱硝催化剂载体的组分优化及其性能研究

采用挤出成型法制备系列Ti-Si-Ox复合氧化物催化剂载体,研究了Ti-Si-Ox催化剂载体组分配比、酸量、比表面积与脱硝性能和抗压强度的关系。采用XRD、Raman spectra、N2-BET、Py-IR以及XPS等表征技术对催化剂载体进行表征。结果表明：Ti/Si元素摩尔比为9∶1时Ti0.9Si0.1Ox催化剂载体的轴向抗压强度适中且脱硝催化活性最高;在5 000 h-1空速条件下,催化剂载体最高脱硝活性在400 ℃时达到99.0%且起燃温度降至260 ℃;另外,Ti0.9Si0.1Ox催化剂载体的比表面积为75.4 m2·g-1,介孔最可几分布为8.1 nm,轴向抗压强度为1.4 MPa,符合工业应用条件,即Ti-Si-Ox载体具有较好的工业应用前景。     

基于改进粒子群算法和元胞自动机的城市扩张模拟——以南京为例

为合理利用多智能体算法解决城市扩张动态模拟问题,基于地理学理论和社会学规律对粒子群算法进行有针对性的改进,提出分段式粒子群算法(SPSO),并结合元胞自动机模拟复杂时空过程的能力,构建出适用于城市扩张模拟的地理元胞自动机SPSO-CA。在SPSO-CA中我们利用多时像的土地利用数据、交通路网数据和地形数据,挖掘出1995~2000年南京城市扩张的土地转换规则。再由此规则实现1995~2008年的南京市城市扩张过程的动态模拟。最后对比SPSO-CA、PSOCA及NULL模型结果得:SPSO-CA总精度86.3%,Kappa系数为0.792,Moran’s I为0.078,PSO-CA总精度83.6%,Kappa系数为0.755,Moran’s I为0.054,NULL模型总精度81.9%,Kappa系数为0.741,真实的Moran’s I为0.072。这表明无论是总精度还是空间一致性,SPSO-CA都优于PSO-CA和NULL模型,即用SPSO-CA模拟城市扩张是可行的。     

基于加权叠加模型的高原湖泊流域重要生态用地识别——以星云湖流域为例

生态用地在维持区域生态平衡和保障区域生态安全具有重要意义。以云南星云湖流域为研究区,运用层次分析法和GIS技术,从水土保持、地质灾害规避与防护、生物多样性保护和水资源安全4个方面,构建了流域重要生态用地识别指标及其识别方法,并识别出流域重要生态用地空间分布。结果表明:(1)加权叠加模型更适用于高原湖泊流域重要生态用地识别;(2)根据生态用地重要性分为核心型、辅助型、过渡型和非重要生态用地,面积分别为75.98 km~2、105.05 km~2、89.47 km~2和65.11 km~2,分别占流域生态用地总面积的22.64%、31.30%、26.66%和19.40%。识别结果能较好地反映重要生态用地维护流域的生态安全。以星云湖流域作为高原湖泊流域的典型,为高原湖泊生态保护提供科学方向,以期协调流域经济发展与生态保护的矛盾,促进可持续发展。     

Environmental forensic investigation of the air pollution from a cement manufacturing unit

Abstract

Cement manufacturing is a process that results in the emission of significant quantities of suspended particulate matter (SPM) to the ambient air. An environmental forensic investigation was carried out in the surroundings of a major cement manufacturing unit at a place called Coimbatore in the southern Indian state of Tamil Nadu. The investigation was carried out to identify the contribution of the cement manufacturing unit to the SPM concentration of the surrounding air environment. The sampling points’ selection and sample collection were done following the principles outlined in the INTERPOL Manual for Pollution Crime Forensic Investigation. On-site monitoring of the air samples was carried out using Mini Laser Aerosol Spectrometer (GRIMM, Mini-LAS Model 11R). The instrument was capable of measuring particles ranging from 0.25 to 32 µm and classifying them into 31 size channels. The test results at majority of the monitoring locations were well above the limits specified in the National Ambient Air Quality Standards of India. Microscopic studies of the dust samples were carried out for surface texture and particle shape. The spatial distribution of particles was analysed using geographic information system (GIS) for the visual identification of the extent of the pollution by keeping the cement factory as the focal point. The results from the GIS and microscopic analysis established the role of the cement factory in the particulate matter pollution of its surroundings, specifically in the areas North-West of the factory. The successfully adopted procedure can serve as a guideline for the environmental forensic investigation of similar pollution incidences.     

基于多元统计分析的渭河西咸段水质评价

基于2012年旱季和雨季的水质监测数据,采用主成分分析(PCA)和绝对主成分多元线性回归分析(APCS-MLR)方法对渭河西咸段水体的污染特征进行了综合评价。结果表明,2012年渭河西咸段水体主要以有机污染和富营养化污染为主,同时存在一定的As污染。总体上,研究水体旱季比雨季污染严重,旱季和雨季提取的主成分及大部分断面综合排名不同,说明降雨对水质产生一定的影响。渭河西咸段水体在皂河入渭之前的上游污染相对较轻,污染贡献以干流排污为主;对应的下游水质较差,皂河是其最大的污染源。     

Decline of traditional rice farming constrains the recovery of the endangered Asian crested ibis (Nipponia nippon)

Traditional agriculture benefits a rich diversity of plants and animals. The winter-flooded rice fields in the Qinling Mountains, China, are the last refuge for the endangered Asian crested ibis (Nipponia nippon), and intensive efforts have been made to protect this anthropogenic habitat. Analyses of multi-temporal satellite data indicate that winter-flooded rice fields have been continuously reduced across the current range of crested ibis during the past two decades. The rate of loss of these fields in the core-protected areas has unexpectedly increased to a higher level than that in non-protected areas in the past decade. The best fit (R² = 0.87) numerical response model of the crested ibis population shows that a reduction of winter-flooded rice fields decreases population growth and predicts that the population growth will be constrained by the decline of traditional winter-flooded rice fields in the coming decades. Our findings suggest that the decline of traditional rice farming is likely to continue to pose a threat to the long-term survival and recovery of the crested ibis population in China.

Electronic supplementary material

The online version of this article (doi:10.1007/s13280-015-0649-5) contains supplementary material, which is available to authorized users.     

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.